Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 59
Visualizing Chemistry: The Progress and Promise of Advanced Chemical Imaging 3 Imaging Techniques: State of the Art and Future Potential The case studies in Chapter 2 underscore the power of chemical imaging to provide insights into a wide variety of problems in the chemical sciences. In this chapter,1 the current capabilities of chemical imaging are examined in detail, as are areas in which basic improvements in imaging capabilities are needed. However, the chapter is not intended to be an exhaustive review of all chemical imaging techniques. It is assumed that the reader has a basic knowledge of the imaging techniques described. The objective of this chapter is to provide an overview of the state of the art in chemical imaging and to identify those areas that would most likely provide breakthroughs. The imaging techniques described are divided into three main categories. In addition, a section on image processing and computation—which has bearing on virtually all chemical imaging techniques—is also included: Optical imaging (Raman, infrared [IR], and fluorescence) and magnetic resonance Electron microscopy, X-rays, ions, neutrons Proximal probe (force microscopy, near field, field enhancement) Processing analysis and computation OPTICAL IMAGING AND MAGNETIC RESONANCE Imaging techniques that utilize low-energy resonant phenomena (electronic, vibrational, or nuclear) to probe the structure and dynamics of molecules, molecular complexes, or higher-order chemical systems differ from approaches
OCR for page 60
Visualizing Chemistry: The Progress and Promise of Advanced Chemical Imaging using higher-energy radiation (X-rays, electrons, etc.) in that they are largely nondestructive and can be performed under in vivo or in situ conditions, even with soft matter. However, these techniques lack the inherent spatial resolution of the higher-energy approaches. Although similar in these respects, magnetic resonance and optical spectroscopy (electronic and vibrational spectroscopy) have different strengths and weaknesses. Magnetic resonance is the lowest-energy method and as such uses the longest-wavelength radiation. Exquisite detail in molecular structure can be defined due to the fact that atomic interactions can be measured. However, this detail about the atomic interactions is accompanied by a low inherent sensitivity, thus requiring extensive averaging over many molecules and limiting the inherent temporal and spatial resolution. In contrast, optical spectroscopy utilizes radiation at an energy level high enough to allow individual photons to be measured relatively easily with modern equipment at a detection sensitivity almost matched by the mammalian eye. As a result, imaging data are acquired at the sensitivity of individual molecules. The inherent temporal and spatial resolution is also increased proportionately, but the resonance itself is broad because environmental influences are not averaged out within the inherent time scale of interaction between the molecules and this frequency of radiation. As a result, the structural information content of optical spectra is considerably lower than that of magnetic resonance, particularly in the electronic region of the spectrum. The long-term technical challenge is to extract the maximum possible information from each type of resonance, ultimately providing a detailed structural picture of the chemistry at the molecular level with the spatial resolution of individual molecules and a temporal resolution on the time scale of chemical bonding. Nuclear Magnetic Resonance Over the past 50 years, nuclear magnetic resonance (NMR) has grown into an essential tool for chemists in determining structures of newly synthesized compounds, for scientists interested in the structure of solids, and for biochemists in determining structure-function relationships in biomolecules. NMR also forms the basis for magnetic resonance imaging (MRI). The incredible breadth of NMR and its impact on chemical, biological, and medical sciences have created a vibrant and innovative community of scientists working to increase the scope and usefulness of NMR. Many books are dedicated to subsets of the techniques involved in NMR and MRI: thus, the goal here is to give a small taste of the types of information available and to point out areas in which progress would impact a large subset of NMR and MRI experiments. In addition, there is an equally rich field, which is not discussed explicitly, that applies electron spin resonance to many of the same problems to which NMR and MRI are applied. Recent advances have pushed the limits of molecular structure determination, including applications of NMR to larger and larger molecules and new ways
OCR for page 61
Visualizing Chemistry: The Progress and Promise of Advanced Chemical Imaging to enhance the detection limits of NMR. MRI has also undergone a major transition from a tool that provides primarily anatomical information to one that can measure a number of aspects of tissue function. Indeed, active areas of the human brain can now be mapped at unprecedented resolution using functional MRI. However, there is much room for improvement, and there are a number of fruitful areas for development. Higher-magnitude magnetic fields, more sensitive detection strategies, and an ever-growing list of MRI contrast agents will continue to expand the usefulness of NMR and MRI, rendering them essential in chemical imaging. This section provides a general outline of the present state of the art of NMR and MRI, describes some exciting new developments in the area, and finally points out some opportunities for future work that can impact NMR and MRI broadly. Present State of the Art Nuclear Magnetic Resonance Spectroscopy: Molecular Structure and Dynamics. NMR is the only tool that provides detailed three-dimensional information at angstrom (Å) resolution of molecules both in solution and in noncrystalline solids. NMR is thus important in imaging molecules not only for the organic chemist but also for materials scientists and biochemists. Its exquisite sensitivity to molecular structure is due to the ability to monitor interactions between atoms that report on structure and dynamics. Chemical shift and J-coupling information obtained from NMR is the result of specific chemical bonds and bond angles. Through-space interactions, such as dipole-dipole interactions, are sensitive to short range (1-5 Å) nonbonded information. Thus, rather than using diffraction of radiation as in X-ray crystallography, NMR builds up structures from a large number of specific interatomic distances and bond angles. Over the past 30 years, the development of complex multidimensional NMR experiments on molecules isotopically labeled with 15N, 13C, and 2H has made routine the probing of detailed structures of molecules in solution up to a molecular weight of approximately 40,000. Similar developments in solid-state NMR now allow a number of structural constraints to be obtained for much larger molecules. The awarding of the Nobel Prize in chemistry in 1991 to Richard Ernst for his work in developing fundamental strategies in NMR and in 2002 to Kurt Wuthrich for his work in using NMR to solve protein structures testifies to the impact of NMR.2 In addition to structural information, dynamic information can also be obtained through NMR. Time scales of both fast (picoseconds) and slow (seconds and longer) processes can be followed. Slow processes such as chemical reactivity are probed by following a change in an NMR property such as chemical shift or transfer of magnetization from one spectral site to another. Detailed kinetic information can be extracted in well-established experiments. Faster processes influence the NMR spin relaxation properties, such as T1 or T2, with kinetic information linked to the specific structure being examined. Model-independent ways
OCR for page 62
Visualizing Chemistry: The Progress and Promise of Advanced Chemical Imaging of analyzing relaxation data have enabled very efficient procedures for determining which parts of a molecule are more dynamic and over what time scales the fluctuations occur. Thus, NMR is unmatched in the detailed structural and dynamic information it offers. The main limitation of NMR continues to be its relatively low sensitivity, requiring homogeneous (or heterogeneous mixtures with only a few components) samples of relatively high concentrations (e.g., a milliliter of 10 mM concentration) to be studied. Separation techniques such as high-performance liquid chromatography (HPLC) can be performed prior to NMR to help study complex mixtures, but the ability to obtain detailed structural information about complex mixtures that vary at high spatial resolution requires large gains in sensitivity. Three major directions are being pursued to increase sensitivity. First, higher-magnitude magnetic fields increase anywhere from linearly to quadratically in sensitivity with an increase in field strength, depending on the sample. Magnets with fields up to about 20 Tesla operating at 900 MHz frequencies are becoming available at a few dedicated research sites. A second pursuit has been the improvement of detectors for NMR. One such strategy that has become widely available over the past five years is cooling of the NMR detectors to reduce noise, which has increased sensitivity by a factor of 2 to 4. Work is progressing to miniaturize NMR detectors and use detector arrays to increase sensitivity and throughput. Furthermore, work is aimed at using innovative approaches to detect magnetic resonance signals, such as magnetic force microscopy,3 which borrows concepts from near-field imaging, and other classes of detectors continue to be developed, such as superconducting quantum interference devices (SQUID) for NMR.4 A third approach to increase sensitivity is to increase the signal available from a molecule using hyperpolarization techniques. Indeed, hyperpolarization techniques are leading to large increases in sensitivity from 100- to 100,000-fold. Techniques to transfer polarization were pioneered by physicists such as Albert Overhauser, who was awarded the National Medal of Science in 1994 for his work predicting that electron spin polarization could be coupled to nuclear spin polarization, and Alfred Kastler, who was awarded the Noble Prize in physics in 1966 for his work demonstrating that optical pumping could lead to hyperpolarization. These techniques are now beginning to find widespread application. When samples are placed in the magnets typically used for NMR, at least a million spins are required to generate enough of a population difference between ground and excited states to give a signal. In practice, many more molecules are needed for a sufficient signal to be generated for detection. There is a class of techniques that rely on transferring polarization from molecules that have greater population differences to molecules that one would like to detect with NMR and in this way generate a larger population difference with much fewer spins. There are numerous ways to transfer polarization and increase signal. Three specific techniques that have found growing use are transfer of polarization from unpaired electrons in stable free radicals to nuclear spins,5 laser-induced polarization of noble gases
OCR for page 63
Visualizing Chemistry: The Progress and Promise of Advanced Chemical Imaging such as xenon and helium,6 and chemical formation of molecules from parahydrogen that can be produced in a polarized state.7 These hyperpolarization strategies are being used to increase sensitivity for application to a wide range of problems in physics, chemistry, biochemistry, and medical imaging. In addition to increasing the sensitivity of NMR, much work is being done to improve the specificity and accuracy of information available from NMR. Perhaps this is most evident in work on biological macromolecules, which is an active area of development for NMR. An exciting recent example shows that partial orientation of molecules in solution greatly increases the strength of dipole-dipole interactions that are important for obtaining distance information. The strategy of partial alignment has led to structural information about molecules (such as proteins) at very high resolution and with very high accuracy.8 There are also a variety of new NMR techniques to measure dynamics of complex molecules in solutions. In general, these techniques rely on measuring NMR relaxation times and interpreting them in the context of a model of the motion. Recent work measuring the relaxation time of deuterium has enabled the measurement of side chain motion of proteins in solution, with molecular weights up to about 100,000 daltons.9 Indeed, a variety of sophisticated NMR pulse sequences enable motion to be analyzed on the picosecond through millisecond time scale. Development of these pulse sequences continues to be an active area of research. Finally, much of the information about structure and dynamics obtained in the solution state by NMR can also be obtained using solid-state NMR for molecules of much higher molecular weight. Detailed structural and dynamic information can be obtained even if the material being studied defies crystallization.10 The exciting area of solid-state NMR is rapidly developing for determining structures of novel materials important for nanotechnology as well as for proteins that do not readily crystallize. Magnetic Resonance Imaging: Noninvasive Measurement of Anatomy, Function, and Biochemistry. In 1974, Paul Lauterbur introduced a gradient field strategy to obtain images based on NMR. Today, MRI is being employed in more than 10 million scans per year in the United States and is thus having a great impact on the diagnosis and treatment of a wide variety of diseases. Its importance was recognized when the 2003 Nobel Prize in medicine was awarded to Drs. Lauterbur and Mansfield.11 The basis for MRI is the change in chemical shift that an atom undergoes in an applied magnetic field. With proper calibration of the magnetic field gradient, a change in chemical shift can be related to a specific location—a process known as frequency encoding of spatial information. In addition, controlling the applied magnetic field gradients in combination with specific radio-frequency pulses to excite specific regions enables signals to come from these specific regions—a process known as slice selection. Finally, the time evolution of the NMR signal during a series of radio-frequency excitation pulses can be modulated by the chemical shift of the nucleus being detected. Because the chemical shift can be altered by applied magnetic field gradients during these evolution
OCR for page 64
Visualizing Chemistry: The Progress and Promise of Advanced Chemical Imaging times, spatial information can be obtained—a process known as phase encoding. There is a wide variety of techniques that use innovative combinations of frequency encoding, slice selection, and phase encoding to generate images. Any nucleus that can be detected by NMR can be imaged with MRI. The most widely used atom is the hydrogen in water because the high concentration of water enables high-resolution images and a large amount of information can be obtained about the environment of water from changes in its NMR relaxation times, T1 and T2. However, much work has been done detecting other nuclei such as 23Na, 31P, and compounds labeled with 13C, to name a few. In most cases, MRI is performed on the hydrogen atoms in water and detects the single NMR peak from water. However, strategies referred to as spectroscopic imaging or chemical shift imaging enable a series of images to be obtained that represent every resonance in an NMR spectrum. In this way, images of complex metabolite distributions have been obtained and applied to get a metabolic fingerprint of normal and diseased tissue. Interaction of the hydrogen on a water molecule (or any other NMR active nucleus) with an applied magnetic field gradient enables MRI to create images at much higher resolution than the wavelength of the applied radiation, leading to images with resolutions in the range of 0.2-3 mm in humans and as low as 0.05 mm in animals. With small samples at high magnetic fields, resolution as low as a few microns has been achieved. This is a key factor in the ability of MRI to obtain high resolution of tissues nondestructively using long-wavelength, and thus low-energy, nonionizing radiation. The second reason behind the usefulness of MRI is the remarkable degree of specificity and sensitivity to disease. Water reports on changes in its environment, and the relaxation times of water are sensitive to specific tissues, enabling unparalleled anatomical information to be obtained from soft tissues in the body. In addition, spectroscopic imaging gives information about a large range of metabolites that can be affected early in disease processes. The largest application of MRI has been to biomedical problems, but there is a growing list of problems from characterization of solids to understanding fluid flow in complex media that have been addressed with MRI. Indeed, funding to translate developments of MRI in the biomedical arena to other areas central to chemical imaging would have a major impact. The past decade has seen a rapid growth in the use of MRI to obtain anatomical information and functional information about tissues. Strategies have been developed that enable MRI to generate images of flowing water, enabling angiography to be performed on the circulatory system. MRI can also be used to measure bulk flow of water, allowing regional blood flow to be measured from a number of tissues. NMR has been used for decades to measure the magnitude and direction of molecular diffusion in solution, and it is possible to extend these techniques to MRI. Techniques for measuring regional blood flow and diffusion are having a major impact on assessing ischemic disease such as heart attacks and stroke. Indeed, at an early stage, diffusion and perfusion MRI can be used to
OCR for page 65
Visualizing Chemistry: The Progress and Promise of Advanced Chemical Imaging decide therapeutic strategies for stroke victims. In addition, MRI can be sensitized to blood oxygenation levels to assess the degree of metabolic activity in a region of a tissue. When a region of the brain becomes active, the increases in blood flow and metabolism lead to changes in blood oxygenation that can be detected by MRI. This oxygenation-dependent, functional MRI contrast has revolutionized cognitive psychology and is leading to a detailed understanding of the regions of the brain that are responsible for complex cognitive functions.12 Finally, NMR spectroscopy can be combined with MRI to generate detailed spectroscopic images of a range of metabolites. The entire range of functional MRI tools is poised to have a major impact on the diagnosis and management of disease.13 The Cutting Edge and Future Directions in NMR and MRI Higher Magnetic Fields. The sensitivity of magnetic resonance increases with higher magnetic fields. Indeed, in the range where detector noise dominates, sensitivity increases as approximately the square of the increase in field. In practice, this is hard to realize, particularly because many samples of interest contribute noise, leading to an increase in sensitivity that is linearly proportional to magnetic field strength. Nonetheless, much interest has been focused on producing higher magnetic fields for NMR. Most of this work occurs in industry where fields as high as 20 Tesla (T) can be produced for routine analytical chemistry and biochemistry. In MRI, magnets up to 9.4 T that are large enough for humans are becoming available. These high fields should increase the resolution of MRI of hydrogen as well as be a great boost to MRI of nuclei less sensitive than hydrogen, such as 23Na, 31P, and 13C. The cutting edge for development of high-field magnets is at the National Magnet Laboratory at the University of Florida, where magnets as high as 40 T are available for use.14 In France, a new project is proceeding to increase the strength of magnetic fields available for MRI on humans to 12 T.15 Transforming these exciting projects into commercially viable products would have widespread impact and enable the development of new technologies that allow even higher magnetic fields to be created. This major challenge is in need of creative thinking to move forward without the very great expenditures that these projects currently require. For example, with present magnet technology, significant space is required to house a high-strength magnet. Work to decrease the siting requirement of high-field magnets, for example by employing innovative designs for superconducting wire that can carry higher current densities, could decrease the size of magnets, enabling very high field NMR and MRI to transition from dedicated laboratories to widespread use. There is some work indicating that NMR can become a more portable modality. For example, in the oil industry the NMR system is attached directly to the exploration drill to mine for petroleum sources. A generalization of this portability of NMR could lead to applications in a range of environmental studies as well as in medical contexts, where a handheld MRI device might be available to
OCR for page 66
Visualizing Chemistry: The Progress and Promise of Advanced Chemical Imaging clinicians working far from a hospital’s radiology department. Recently, the use of a SQUID detector has been demonstrated to lead to excellent NMR spectra at very low magnetic fields, pointing to the possibility of making NMR more portable.16 Thus, there is much room for innovative work, both to enable higher magnetic fields and to make NMR more portable with lower magnetic fields. Development of New MRI Detectors. Another important strategy for increasing sensitivity in NMR and MRI is the development of new detectors. For NMR, an increase in sensitivity from two- to fourfold has occurred by decreasing the temperature of the detector. These advances, using either high-temperature super-conducting materials or traditional materials, are now being implemented widely. There have been similar sensitivity gains in MRI due to the widespread availability of high magnetic fields (3-9 T) for human use and the development of parallel detector arrays. Five years ago, for example, an effective scan of a human head was achieved with an MRI detector containing only one element. Today detectors with 8 to 32 elements are becoming common,17 with preliminary data obtained from arrays with up to 90 elements. These arrays increase sensitivity from two- to fivefold and also enable MRI to be performed at much faster speeds.18 When these arrays are dense enough for the coil noise to dominate over the sample noise, cooling arrays should increase the sensitivity of MRI further. The challenge is to insulate the detectors so that very cold temperatures can be achieved while keeping the detectors close to the body so that sensitivity gains can be realized. With the rapid increase in detector density, it is critical to develop strategies that enable miniaturization of the electronics necessary to perform MRI. A concerted effort to miniaturize NMR components not only will enable engineering of dense detector arrays, but also should increase the portability of NMR in general. There is much to gain by focusing research efforts to increase sensitivity in NMR and MRI. At present, MRI on humans is performed at resolutions of about a millimeter, with recent results pushing these limits to about 300 microns. A factor of 100X gain in sensitivity would place MRI on the brink of detecting single cells in any organ within the human body. This would also enable chemical imaging for a larger variety of problems where the unmatched chemical sensitivity of NMR can be combined with the spatial resolution afforded by MRI. Research on other detector strategies besides those commonly used should be encouraged, for example developing SQUID detectors for NMR or other innovative approaches to detecting signals. Indeed, it is only the lack of sensitivity that at present limits widespread application of MRI as a chemical imaging tool to the full range of problems discussed throughout this report. Increasing the NMR Signal with Hyperpolarization. A very promising avenue for increasing sensitivity in NMR and MRI is to increase the signal from the molecules being detected. The low radio-frequency energy used for NMR means that
OCR for page 67
Visualizing Chemistry: The Progress and Promise of Advanced Chemical Imaging specific nuclei in molecules are as likely to be in the excited state as in the ground state. Signal detection is proportional to the population difference between the two states. Typically, it takes a million molecules to generate a larger ground state than excited state population. There are a number of ways to alter this population difference and polarize the sample to obtain more signals. As discussed previously, increasing the magnetic field for NMR and MRI is one way to achieve incremental gains. Another alternative is to decrease the temperature, which is useful only if the sample is amenable to lower temperatures. A final and very dramatic way is to couple the nuclear spins being detected by NMR to other spins with a higher polarization. As mentioned earlier, transferring polarization from electrons, optically pumping to achieve increased nuclear polarization of noble gases, and using parahydrogen have all been successful in increasing the signal by as much as 100- to 100,000-fold. For example, so-called dynamic nuclear polarization experiments coupling a stable free radical to NMR-detectable nuclei have demonstrated great gains in sensitivity for solid-state NMR, enabling experiments that would ordinarily last days to be performed in minutes.19 Furthermore, clever strategies allow the solid to be thawed to a liquid and prepared in a manner such that it can be injected, which enables hyperpolarization to be used in vivo for MRI. Hyperpolarized MRI of 13C-labeled compounds has been shown to increase sensitivity more than 100,000-fold; this offers exciting possibilities to trace specific metabolic pathways to identify diseases such as cancer.20 One major drawback is that these techniques cannot be applied generally to all molecules. Optical pumping of the noble gases xenon and helium can also lead to very large gains in sensitivity. Recent work has demonstrated the potential for producing biosensors from optical-pumped xenon to enable detection to about 200 nM.21 Hyperpolarized noble gases are also finding increasing use for MRI of the air spaces in lungs.22 A major shortcoming of these hyperpolarization studies is that they are applicable to only a few molecules. Generation of new materials optimized for hyperpolarization is very important to enable a large range of molecules to be hyperpolarized. Another major limitation is that the hyperpolarized signal lasts for a time defined by the nuclear spin lattice relaxation time. In the molecules being developed this means that the increased signal lasts for about a minute. Innovative approaches to making the best use of the polarization while it lasts and procedures for replenishing the signal are critical to a broader range of application. Ideally, a new generation of physicists, chemists, and biochemists would be trained to conduct this truly interdisciplinary work. Detection of Single Spins with Scanning Force Magnetic Resonance. Within the last year the detection of a single electron spin was accomplished with a scanning magnetic resonance experiment using cantilevers similar to those used for scanning force microscopy.23 This was the culmination of many years of progress to detect increasingly fewer electron or nuclear spins using the magnetic resonance
OCR for page 68
Visualizing Chemistry: The Progress and Promise of Advanced Chemical Imaging phenomenon. The experiment relied on measuring the force generated when the electron spin orientation was flipped by application of the appropriate radio frequency in a magnetic field. Because the electron spin is 1,000 times stronger than a nuclear spin, this result opens the possibility of detection of single nuclei and thus single-molecule detection by magnetic resonance. As a result, one can envision the use of a small cantilever to scan a molecule or molecular assembly to determine its detailed chemical composition and three-dimensional structure. Such an advance will take years of development to realize and requires advances similar to those needed in other scanning near-field imaging techniques, including (1) the development of more sensitive cantilever strategies to measure increasingly smaller forces and (2) a deeper theoretical understanding of single-molecule behavior with respect to magnetic resonance. Quantitative Understanding of Chemical Shifts. A great triumph for NMR has been the ability to obtain detailed three-dimensional information from molecules with weights up to about 40,000 grams per mole with accuracy to a few angstroms. It is well known that NMR chemical shifts are sensitive to very small bond length and bond angle changes and can thus probe chemical potentials at very short distances. This is due to the exquisite sensitivity of nuclear spins to their electronic environment. One of the great challenges of modern chemistry is to develop quantum mechanical calculations that can predict chemical interactions and chemical reactions of large molecules. A great hurdle to this work is developing analytical tools that can measure potential changes over short distances. Analysis of the chemical shift of nuclei is one of the few techniques that can probe these potentials over short distances. Thus, a critical frontier in work in NMR is to develop computational approaches that enable prediction of chemical shifts in large molecules. Indeed, if this work is successful it will be possible to determine molecular structures of very complex molecules in a time-efficient manner to an unprecedented level of resolution. Novel Contrast Agents for MRI. Contrast agents have played an important role in the development of MRI. For example, simple gadolinium chelates are critical for the usefulness of MRI in detecting brain tumors, performing angiography, and measuring regional blood flow and metabolism. With the rapid developments in molecular genetics identifying a large number of potential indicators of disease and therapeutic targets, there is increasing interest in developing MRI contrast agents that are specific for particular cells, molecules, or biochemical processes. This emerging area of molecular imaging depends on the marriage of (1) chemical synthesis of new labels to add specificity to the agent and (2) MRI acquisition and processing to optimize strategies to detect these new agents. Recent work has demonstrated that MRI can be used to specifically target cell surface molecules, image gene expression, detect enzymatic reactions, and follow the migration of cells in intact organs.24 These developments are a long way from routine clinical
OCR for page 69
Visualizing Chemistry: The Progress and Promise of Advanced Chemical Imaging use, and the realization of this potential will take the concerted efforts of a multidisciplinary team of chemists, molecular biologists, radiologists, and MRI physicists. Particularly lacking are chemists with a commitment to work in this highly multidisciplinary area. Furthermore, the general strategies being offered are applicable to a broad range of problems outside the field of medicine, such as detection of sparse molecules of environmental interest or characterization of complex materials. Funding to translate developments in the biomedical area to broader use in chemical imaging would have a great impact. Conclusions NMR and MRI represent mature technologies that have widespread impact on the materials, chemical, biochemical, and medical fields. Recent results in determining the structures of key biological macromolecules and the transformation of the cognitive sciences due to functional MRI exemplify this tremendous influence. Despite these achievements, there is much progress yet to be made. Research aimed at improving magnet technology to achieve higher field strengths in smaller footprints will advance the sensitivity and applicability of NMR. Developments to miniaturize NMR electronics will greatly aid the rapid progress in parallel detection for MRI and increase the portability of NMR. Investment in the exciting area of hyperpolarization has an excellent chance to greatly increase the sensitivity and applicability of NMR and MRI. Investments in the theoretical aspects of NMR, especially those that enable the prediction of structural information from chemical shifts and the optimization of approaches to increase sensitivity using hyperpolarization, will pay large dividends. Finally, funding toward development of new materials can impact NMR on many levels. New superconducting materials can impact magnet and detector design, and new approaches to generating sensitive cantilevers will usher in the era of single-molecule detection by magnetic resonance. A new generation of chemists can impact NMR and MRI research by focusing on the development of new molecules amenable to hyperpolarization strategies as well as new contrast agents to contribute to the rapidly growing field of molecular imaging. Funding mechanisms that can lead to faster translation of developments made in the biomedical area to other areas of chemical imaging should be pursued. It is clear that in the coming years, NMR and MRI will continue to expand rapidly and continue to be key tools for chemical imaging. Vibrational Imaging A vibrational spectrum provides something like a structural “fingerprint” of matter because it is characteristic of chemical bonds in a specific molecule. Therefore, imaging based on vibrational spectroscopic signatures, such as Raman scattering and IR absorption, provides a great deal of molecular structural information about the target under study.
OCR for page 168
Visualizing Chemistry: The Progress and Promise of Advanced Chemical Imaging 219. Betzig, E., and R.J. Chichester. 1993. Single molecules observed by near-field scanning optical microscopy. Science 262:1422-1425. 220. Hollars, C.W., and R.C. Dunn. 2000. Probing single molecule orientations in model lipid membranes with near-field scanning optical microscopy. J. Chem. Phys. 112:7822-7830. 221. Higgins, D.A., J. Kerimo, D.A. Vanden Bout, and P.F. Barbara. 1996. A molecular yarn: Near field optical studies of self assembled, flexible, fluorescent fibers. J. Am. Chem. Soc. 118:4049-4058. 222. (a) Teetsov, J.A., and D.A. Vanden Bout. 2001. Imaging molecular and nanoscale order in conjugated polymer thin films with near-field scanning optical microscopy. J. Am. Chem. Soc. 123:3605-3606. (b) DeAro, J.A., K.D. Weston, S.K. Buratto, and U. Lemmer. 1997. Mesoscale optical properties of conjugated polymers probed by near-field scanning optical microscopy. Chem. Phys. Lett. 277:532-538. (c) Blatchford, J.W., T.L. Gustafson, A.J. Epstein, D.A. Vanden Bout, J. Kerimo, D.A. Higgins, P.F. Barbara, D.-K. Fu, T.M. Swager, and A.G. MacDiarmid. 1996. Spatially and temporally resolved emission from aggregates in conjugated polymers. Phys. Rev. B 54:R3683-R3686. 223. Paesler, M.A., and P.J. Moyer. 1996. Near-Field Optics: Theory, Instrumentation, and Applications. New York: John Wiley & Sons. 224. (a) Higgins, D.A., D.A. Vanden Bout, J. Kerimo, and P.F. Barbara. 1996. Polarization-modulation near field scanning optical microscopy of mesostructured materials. J. Phys. Chem. 100:13794-13803. (b) Vaez-Iravani, M., and R. Toledo-Crow. 1993. Pure linear polarization imaging in near field scanning optical microscopy. Appl. Phys. Lett. 63:138-140. (c) McDaniel, E.B., S.C. McClain, and J.W.P. Hsu. 1998. Nanometer scale polarimetry studies using a near-field scanning optical microscope. Appl. Opt. 37:84-92. (d) Goldner, L.S., M.J. Fasolka, S. Noufler, H.-P. Nguyen, G.W. Bryant, J. Hwang, K.D. Weston, K.L. Beers, A. Urbas, and E.L. Thomas. 2003. Fourier analysis near-field polarimetry for measurement of local optical properties of thin films. Appl. Opt. 42:3864-3881. (e) Lacoste, T., T. Huser, R. Prioli, and H. Heinzelmann. 1998. Contrast enhancement using polarization-modulation scanning near-field optical microscopy. Ultramicroscopy 71:333-340. 225. Humphris, A.D.L., J.K. Hobbs, and M.J. Miles. 2003. Ultrahigh-speed scanning near-field optical microscopy capable of over 100 frames per second. Appl. Phys. Lett. 83:6-8. 226. Bukofsky, S.J., and R.D. Grober. 1997. Video rate near-field scanning optical microscopy. Appl. Phys. Lett. 71:2749-2751. 227. (a) McNeill, J.D., and P.F. Barbara. 2002. NSOM investigation of carrier generation, recombination, and drift in a conjugated polymer. J. Phys. Chem. B 106:4632-4639. (b) Higgins, D.A., J.E. Hall, and A. Xie. 2005. Optical microscopy studies of dynamic within individual polymer-dispersed liquid crystal droplets. Acc. Chem. Res. 38:137-145. 228. (a) Levy, J., V. Nikitin, J.M. Kikkawa, D.D. Awschalom, and N. Samarth. 1996. Femtosecond near-field spin microscopy in digital magnetic heterostructures. J. Appl. Phys. 97:6095-6100. (b) Guenther, T., C. Lienau, T. Elsaesser, M. Glanemann, V.M. Axt, T. Kuhn, S. Eshlaghi, and A.D. Wieck. 2002. Coherent nonlinear optical response of single quantum dots studied by ultrafast near-field spectroscopy. Phys. Rev. Lett. 89:057401. 229. (a) McNeill, J.D., and P.F. Barbara. 2002. NSOM investigation of carrier generation, recombination, and drift in a conjugated polymer. J. Phys. Chem. B 106:4632-4639. (b) Adams, D.M., J. Kerimo, C.-Y. Liu, A.J. Bard, and P.F. Barbara. 2000. Electric field modulated near-field photo-luminescence of organic thin films. J. Phys. Chem. B 104:6728-6736. 230. Higgins, D.A., J.E. Hall, and A. Xie. 2005. Optical microscopy studies of dynamic within individual polymer-dispersed liquid crystal droplets. Acc. Chem. Res. 38:137-145. 231. Davy, S., and M. Spajer. 1996. Near field optics: Snapshot of the field emitted by a nanosource using a photosensitive polymer. Appl. Phys. Lett. 69:3306-3308. 232. (a) H’dhili, F., R. Bachelot, G. Lerondel, D. Barchiesi, and P. Royer. 2001. Near-field optics:
OCR for page 169
Visualizing Chemistry: The Progress and Promise of Advanced Chemical Imaging Direct observation of the field enhancement below and apertureless probe using a photosensitive polymer. Appl. Phys. Lett. 79:4019-4021. (b) Tarun, A.., M.R.H. Daza, N. Hayazawa, Y. Inouye, and S. Kawata. 2002. Apertureless optical near-field fabrication using an atomic force microscope on photoresists. Appl. Phys. Lett. 80:3400-3402. (c) Yin, X., N. Fang, X. Zhang, I.B. Martini, and B.J. Schwartz. 2002. Near-field two-photon nanolithography using an apertureless optical probe. Appl. Phys. Lett. 81:3663-3665. 233. Yin, X., N. Fang, X. Zhang, I.B. Martini, and B.J. Schwartz. 2002. Near-field two-photon nanolithography using an apertureless optical probe. Appl. Phys. Lett. 81:3663-3665. 234. Davy, S., and M. Spajer. 1996. Near field optics: Snapshot of the field emitted by a nanosource using a photosensitive polymer. Appl. Phys. Lett. 69:3306-3308. 235. (a) Tarun, A.., M.R.H. Daza, N. Hayazawa, Y. Inouye, and S. Kawata. 2002. Apertureless optical near-field fabrication using an atomic force microscope on photoresists. Appl. Phys. Lett. 80:3400-3402. (b) Yin, X., N. Fang, X. Zhang, I.B. Martini, and B.J. Schwartz. 2002. Near-field two-photon nanolithography using an apertureless optical probe. Appl. Phys. Lett. 81:3663-3665. 236. Sun, S., and G.J. Leggett. 2004. Matching the resolution of electron beam lithography by scanning near-field photolithography. Nano Lett. 4:1381-1384. 237. Lee, M.B., N. Atoda, K. Tsutsui, and M. Ohtsu. 1999. Nanometric aperture arrays fabricated by wet and dry etching of silicon for near-field optical storage application. J. Vac. Sci. Technol. B 17:2462-2466. 238. Bukofsky, S.J., and R.D. Grober. 1997. Video rate near-field scanning optical microscopy. Appl. Phys. Lett. 71:2749-2751. 239. Sanchez, E.J., L. Novotny, and X.S. Xie. 1999. Near-field fluorescence microscopy based on two-photon excitation with metal tips. Phys. Rev. Lett. 82:4014-4017. 240. (a) Jahncke, C.L., M.A. Paesler, and H.D. Hallen. 1995. Raman imaging with near-field scanning optical microscopy. Appl. Phys. Lett. 67:2483-2485. (b) Anderson, N., A. Hartschuh, and L. Novotny. 2005. Near-field Raman microscopy. Mat. Today 8:50-54. (c) Hartschuh, A., E.J. Sanchez, X.S. Xie, and L. Novotny. 2003. High-resolution near-field Raman microscopy of single-walled carbon nanotubes. Phys. Rev. Lett. 90:095503. (d) Pettinger, B., B. Ren, G. Picardi, R. Schuster, and G. Ertl. 2004. Nanoscale probing of adsorbed species by tip-enhanced Raman spectroscopy. Phys. Rev. Lett. 92:096101. 241. Krug, J.T., E.J. Sanchez, and X.S. Xie. 2002. Design of near-field optical probes with optimal field enhancement by finite difference time domain electromagnetic simulation. J. Chem. Phys. 116:10895-10901. 242. (a) Novotny, L., E.J. Sanchez, and X.S. Xie. 1998. Near-field optical imaging using metal tips illuminated by higher-order Hermite-Gaussian beams. Ultramicroscopy 71:21-29. (b) Sanchez, E.J., L. Novotny, and X.S. Xie. 1999. Near-field fluorescence microscopy based on two-photon excitation with metal tips. Phys. Rev. Lett. 82:4014-4017. 243. (a) Adams, D.M., J. Kerimo, C.-Y. Liu, A.J. Bard, and P.F. Barbara. 2000. Electric field modulated near-field photo-luminescence of organic thin films. J. Phys. Chem. B 104:6728-6736. (b) Mei, E., and D.A. Higgins. 1998. Near-field optical microscopy studies of electric-field-induced molecular reorientation dynamics. J. Phys. Chem. A 102:7558-7563. 244. (a) Adams, D.M., J. Kerimo, C.-Y. Liu, A.J. Bard, and P.F. Barbara. 2000. Electric field modulated near-field photo-luminescence of organic thin films. J. Phys. Chem. B 104:6728-6736. (b) Mei, E., and D.A. Higgins. 1998. Near-field optical microscopy studies of electric-field-induced molecular reorientation dynamics. J. Phys. Chem. A 102:7558-7563. (c) Moerner, W.E., T. Plakhotnik, T. Irngartinger, U.P. Wild, D.W. Pohl, and B. Hecht. 1994. Near-field optical spectroscopy of individual molecules in solids. Phys. Rev. Lett. 73:2764-2767.
OCR for page 170
Visualizing Chemistry: The Progress and Promise of Advanced Chemical Imaging 245. (a) DeAro, J.A., D. Moses, and S.K. Buratto. 1999. Near-field photoconductivity of stretch-oriented poly(para-phenylene vinylene). Appl. Phys. Lett. 75:3814-3816. (b) Gray, M.H., J.W.P. Hsu, L. Giovane, and M.T. Bulsara. 2001. Effect of anisotropic strain on the crosshatch electrical activity in relaxed GeSi films. Phys. Rev. Lett. 86:3598-3601. (c) McNeill, C.R., H. Frohne, J.L. Holdworth, and P.C. Dastoor. 2004. Near-field scanning photocurrent measurements of polyfluorene blend devices: Directly correlating morphology with current generation. Nano Lett. 4:2503-2507. 246. Kwak, J., and A.J. Bard. 1989. Scanning electrochemical microscopy. Theory of the feedback mode. Anal. Chem. 61:1221-1227. 247. (a) James, P.J., L.F. Garfias-Mesias, P.J. Moyer, and W.H. Smyrl. 1998. Scanning electrochemical microscopy with simultaneous independent topography. J. Electrochem. Soc. 145:L64-L66. (b) Lee, Y., Z. Ding, and A.J. Bard. 2002. Combined scanning electrochemical/optical microscopy with shear force and current feedback. Anal. Chem. 74:3634-3643. 248. Ibid. 249. Fan, F.-R.F., and A.J. Bard. 1999. Imaging of biological macromolecules on mica in humid air by scanning electrochemical microscopy. Proc. Nat. Acad. Sci. USA 96:14222-14227. 250. Basame, S.B., and H.S. White. 1999. Scanning electrochemical microscopy of metal/metal oxide electrodes. Analysis of spatially localized electron-transfer reactions during oxide growth. Anal. Chem. 71:3166-3170. 251. Fernandez, J.L., D.A. Walsh, and A.J. Bard. 2005. Thermodynamic guidelines for the design of bimetallic catalysts for oxygen electroreduction and rapid screening by scanning electrochemical microscopy. M-Co (M: Pd, Ag, Au). J. Am. Chem. Soc. 127:357-365. 252. Liu, B., S.A. Rotenberg, and M.V. Mirkin. 2000. Scanning electrochemical microscopy of living cells: Different redox activities of nonmetastatic and metastatic human breast cells. Proc. Nat. Acad. Sci. USA 97:9855-9860. 253. Liu, B., A.J. Bard, M.V. Mirkin, and S.E. Creager. 2004. Electron transfer at self-assembled monolayers measured by scanning electrochemical microscopy. J. Am. Chem. Soc. 126:1485-1492. 254. Zhang, J., C.J. Slevin, C. Morton, P. Scott, D.J. Walton, and P.J. Unwin. 2001. New approach for measuring lateral diffusion in Langmuir monolayers by scanning electrochemical microscopy (SECM): Theory and application. J. Phys. Chem. B 105:11120-11130. 255. (a) Zhang, J., C.J. Slevin, C. Morton, P. Scott, D.J. Walton, and P.J. Unwin. 2001. New approach for measuring lateral diffusion in Langmuir monolayers by scanning electrochemical microscopy (SECM): Theory and application. J. Phys. Chem. B 105:11120-11130. (b) O’Mullane, A.P., J.V. Macpherson, P.R.Unwin, J. Cervera-Montesinos, J.A. Manzanares, F. Frehill, J.G. Vos. 2004. Measurement of lateral charge propagation in [Os(Bpy)2(Pvp)Ncl]Cl thin films: A scanning electrochemical microscopy approach. J. Phys. Chem. B 108:7219-7227. 256. (a) Bath, B.D., H.S. White, and E.R. Scott. 2000. Electrically facilitated molecular transport. Analysis of the relative contributions of diffusion, migration, and electroosmosis to solute transport in an ion-exchange membrane. Anal. Chem. 72:433-442. (b) Uitto, O.D., and H.S. White. 2001. Scanning electrochemical microscopy of membrane transport in the reverse imaging mode. Anal. Chem. 72:533-539. 257. (a) Liu, B., S.A. Rotenberg, and M.V. Mirkin. 2000. Scanning electrochemical microscopy of living cells: Different redox activities of nonmetastatic and metastatic human breast cells. Proc. Nat. Acad. Sci. USA 97:9855-9860. (b) Yasykawa, T., T. Kaya, and T. Matsue. 1999. Dual imaging of topography and photo-synthetic activity of a single protoplast by scanning electrochemical microscopy. Anal. Chem. 71:4637-4641. 258. Liu, B., S.A. Rotenberg, and M.V. Mirkin. 2000. Scanning electrochemical microscopy of living cells: Different redox activities of nonmetastatic and metastatic human breast cells. Proc. Nat. Acad. Sci. USA 97:9855-9860. 259. Wittstock, G., and W. Schuhmann. 1999. Formation and imaging of microscopic enzymatically
OCR for page 171
Visualizing Chemistry: The Progress and Promise of Advanced Chemical Imaging active spots on an alkanethiolate-covered gold electrode by scanning electrochemical microscopy. Anal. Chem. 69:5059-5066. 260. Turyan, I., T. Matsue, and D. Mandler. 2000. Patterning and characterization of surfaces with organic and biological molecules by the scanning electrochemical microscope. Anal. Chem. 72:3431-3435. 261. Frenken, J.W.M., T.H. Oosterkamp, B.L.M. Hendriksen, and M.J. Rost. 2005. Pushing the limits of SPM. Mat. Today 8:20-25. 262. (a) James, P.J., L.F. Garfias-Mesias, P.J. Moyer, and W.H. Smyrl. 1998. Scanning electrochemical microscopy with simultaneous independent topography. J. Electrochem. Soc. 145:L64-L66. (b) Lee, Y., Z. Ding, A.J. Bard. 2002. Combined scanning electrochemical/optical microscopy with shear force and current feedback. Anal. Chem. 74:3634-3643. 263. Gray, M.H., J.W.P. Hsu, L. Giovane, M.T. Bulsara. 2001. Effect of anisotropic strain on the crosshatch electrical activity in relaxed GeSi films. Phys. Rev. Lett. 86:3598-3601. 264. Treutler, T.H., and G. Wittstock. 2003. Combination of an electrochemical tunneling microscope (ECSTM) and a scanning electrochemical microscope (SECM): Application for tip-induced modification of self-assembled monolayers. Electrochim. Acta 48:2923-2932. 265. (a) Macpherson, J.V., and P.J. Unwin. 2000. Combined scanning electrochemical-atomic force microscopy. Anal. Chem. 72:276-285. (b) Kranz, C., G. Friedbacher, and B. Mizaikoff. 2001. Integrating an ultramicroelectrode in an AFM cantilever—Combined technology for enhanced information. Anal. Chem. 73:2491-2500. 266. (a) James, P.J., L.F. Garfias-Mesias, P.J. Moyer, and W.H. Smyrl. 1998. Scanning electrochemical microscopy with simultaneous independent topography. J. Electrochem. Soc. 145:L64-L66. (b) Lee, Y., Z. Ding, and A.J. Bard. 2002. Combined scanning electrochemical/optical microscopy with shear force and current feedback. Anal. Chem. 74:3634-3643. 267. Lee, Y., Z. Ding, A.J. Bard. 2002. Combined scanning electrochemical/optical microscopy with shear force and current feedback. Anal. Chem. 74:3634-3643. 268. Alpuche-Aviles, M.A., and D.O. Wipf. 2001. Impedance feedback control for scanning electrochemical microscopy. Anal. Chem. 73:4873-4881. 269. (a) Sidles, J.A. 1992. Folded Stern-Gerlach experiment as a means for detecting nuclear magnetic resonance in individual nuclei. Phys. Rev. Lett. 68:1124-1127. (b) Rugar, D., C.S. Yannoni, and J.A. Sidles. 1992. Mechanical detection of ″5agnetic resonance. Nature 360:563-566. 270. (a) Gremlich, H.U., and B. Yan. 2001. Infrared and Raman Spectroscopy of Biological Materials. New York: Marcel Dekker. (b) Lewis, E.N., P.J. Treado, R.C. Reeder, G.M. Story, A.E. Dowres, C. Marcott, and I.W. Levin. 1995. FTIR spectroscopic imaging using an infrared focal-plane array detector. Anal. Chem. 67:3377-3381. (c) Kotula, P.G., M.R. Keenan, and J.R. Michael. 2003. Automated analysis of SEM X-ray spectral images: A powerful new microanalysis tool. Microsc. Microanal. 9:1-17. (d) Artyushkova, K., and J. Fulghum. 2003. XPS imaging. In Surface Analysis by Auger and X-ray Photoelectron Spectroscopy, D. Briggs, and J.T. Grant, eds. Chichester, Manchester, U.K.: IM publications and SurfaceSpectra Limited. (e) Winograd, N. 2003. Prospects or imaging TOF-SIMS: From fundamentals to biotechnology. Appl. Surf. Sci. 203:13-19. (f) Treado, P.J., M.P. Nelson, and T.H. Myers. 2000. Current state of the art in Raman microscopy and chemical imaging. Microbeam Anal. 2000, Proc. 165:37-38. 271. (a) Oberholzer, M., M. Ostreicher, H. Christen, and M. Bruhlmann. 1996. Methods in quantitative image analysis. Histochem. Cell Biol. 105:333-355. (b) Bovik, A. 2000. Handbook of Image and Video Processing. San Diego, CA: Academic Press.
OCR for page 172
Visualizing Chemistry: The Progress and Promise of Advanced Chemical Imaging 272. Wilson, D.L., K.S. Kump, S.J. Eppell, and R.E. Marchant. 1995. Morphological restoration of atomic-force microscopy images. Langmuir 11:265-272. 273. Jak, M.J.J., C. Konstapel, A. van Kreuningen, J. Verhoeven, R. van Gastel, and J.W.M. Frenken. 2001. Automated detection of particles, clusters and islands in scanning probe microscopy images. Surf. Sci. 494:43-52. 274. Meijering, E.H.W., W.J. Niessen, and M.A. Viergever. 2001. Quantitative evaluation of convolution-based methods for medical image interpolation. Med. Image Anal. 5:111-126. 275. (a) Henderson, R. 1995. The potential and limitations of neutrons, electrons and X-rays for atomic resolution microscopy of unstained biological molecules. Q. Rev. Biophys. 28(2):171-193. (b) Bajaj, C., Z.Y. Yu, and M. Auer. 2003. Volumetric feature extraction and visualization of tomographic molecular imaging. J. Struct. Biol. 144:132-143. (c) Baldock, B., C.J. Gilpin, A.J. Koster, U. Ziese, K.E. Kadler, C.M. Kielty, and D.F. Holmes. 2002. Three-dimensional reconstructions of extracellular matrix polymers using automated electron tomography. J. Struct. Biol. 138:130-136. 276. Al-Kofahi, K.A., A. Can, S. Lasek, D.H. Szarowski, N. Dowell-Mesfin, W. Shain, J.T. Turner, and B. Roysam. 2003. Median-based robust algorithms for tracing neurons from noisy confocal microscope images. IEEE Trans. Info. Tech. Biomed. 7:302-317. 277. Udupa, J.K. 1999. Three-dimensional visualization and analysis methodologies: A current perspective. Radiographics 19:783-806. 278. (a) Leong, F.J.W.M., M. Brady, and J.O. McGee. 2003. Correction of uneven illumination (vignetting) in digital microscopy images. J.Clin. Pathol.y 56:619-621. (b) Tomazevic, D., B. Likar, and F. Pernus. 2002. Comparative evaluation of retrospective shading correction methods. J. Microsc. 208:212-223. 279. (a) Lippert, T., E. Ortelli, J.C. Panitz, F. Raimondli, J. Wambach, J. Wei, and A. Wokuan. 1999. Imaging—XPS/Raman investigation on the carbonization of polyimide after irradiation at 308 nm. Appl. Phys. A. 69:S651-S654. (b) Artyushkova, K., and J.E. Fulghum. 2004. Multivariate analysis applied to topographical corrections in X-ray photoelectron images. Surf. Inter. Anal. 36:1304-1313. (c) Allen, G.C., K.R. Hallam, J.R. Eastman, G.J. Graveling, V.K. Ragnarsdottir, and D.R. Skuse. 1998. XPS analysis of polyacrylamide adsorption to kaolinite, quartz and feldspar. Surf. Interface Anal. 26:518-523. 280. (a) Jak, M.J.J., C. Konstapel, A. van Kreuningen, J. Verhoeven, R. van Gastel, and J.W.M. Frenken. 2001. Automated detection of particles, clusters and islands in scanning probe microscopy images. Surf. Sci. 494:43-52. (b) Lippert, T., E. Ortelli, J.C. Panitz, F. Raimondli, J. Wambach, J. Wei, and A. Wokuan. 1999. Imaging—XPS /Raman investigation on the carbonization of polyimide after irradiation at 308 nm. Appl. Phys. A. 69:S651-S654. 281. (a) Oberholzer, M ., M. Ostreicher, H. Christen, and M. Bruhlmann. 1996. Methods in quantitative image analysis. Histochem. Cell Biol. 105:333-355. (b) Tyler, B. 2003. Interpretation of TOF-SIMS images: Multivariate and univariate approaches to image de-noising, image segmentation and compound identification. Appl. Surf. Sci. 203-204:825-831. (c) Newsam, S., S. Bhagavathy, C. Kenney, B.S. Manjunath, and L. Fonseca. 2001. Object-based representations of spatial images. Acta Astronaut. 48:567-577. 282. Oberholzer, M ., M. Ostreicher, H. Christen, and M. Bruhlmann. 1996. Methods in quantitative image analysis. Histochem.Cell Biol. 105:333-355. 283. Wilson, D.L., K.S. Kump, S.J. Eppell, and R.E. Marchant. 1995. Morphological restoration of atomic-force microscopy images. Langmuir 11:265-272. 284. (a) Newsam, S., S. Bhagavathy, C. Kenney, B.S. Manjunath, and L. Fonseca. 2001. Object-based representations of spatial images. Acta Astronaut. 48:567-577. (b) Bernasconi, A., S.B. Antel, D.L. Collins, N. Bernasconi, A. Olivier, F. Dubeau, G.B. Pike,
OCR for page 173
Visualizing Chemistry: The Progress and Promise of Advanced Chemical Imaging F. Andermann, and D.L. Arnold. 2001. Texture analysis and morphological processing of magnetic resonance imaging assist detection of focal cortical dysplasia in extra-temporal partial epilepsy. Ann. Neurol. 49:770-775. 285. (a) Dur, J.C., F. Elsass, V. Chaplain, and D. Tessier. 2004. The relationship between particle-size distribution by laser granulometry and image analysis by transmission electron microscopy in a soil clay fraction. European J. Soil Sci. 55:265-270. (b) Costa, L.D., C.A. Rodrigues, N.C. de Souza, and O.N. Oliveira. 2003. Statistical characterization of morphological features of layer-by-layer polymer films by image analysis. J. Nanosci. Nanotech. 3:257-261. 286. (a) Rasa, M., and A.P. Philipse. 2002. Scanning probe microscopy on magnetic colloidal particles. J. Magn. Magn. Mat. 252:101-103. (b) Plaschke, M., T. Schafer, T. Bundschuh, T.N. Manh, R. Knopp, H. Geckeis, and J.I. Kim. 2001. Size characterization of bentonite colloids by different methods. Anal. Chem. 73:4338-4347. 287. (a) Gerlich, D., J. Mattes, and R. Eils. 2003. Quantitative motion analysis and visualization of cellular structures. Methods 29:3-13. (b) Sbalzarini, I.F., and P. Koumoutsakos. 2005. Feature point tracking and trajectory analysis for video imaging in cell biology. J. Struct. Biol. 151:182-195. 288. (a) Ashin, R., A. Morimoto, M. Nagase, and R. Vaillancourt. 2005. Image compression with multiresolution singular value decomposition and other methods. Math. Comp. Modeling 41:773-790. (b) Schelkens, P., A. Munteanu, J. Barbarien, M. Galca, X. Giro-Nieto, and J. Cornelis. 2003. Wavelet coding of volumetric medical datasets. IEEE Trans. Medi. Imag. 22:441-458. 289. Wickes, B.T., Y. Kim, and D.G. Castner. 2003. Denoising and multivariate analysis of time-of-flight SIMS images. Surface and Interface Analysis 35:640-648. 290. (a) Daubechies, I. 1988. Orthnormal bases of compactly supported wavelets. Commu. Pure Appl.Math. 41:909-996. (b) Mallat, S. 1989. A theory for multiresolution signal decomposition. IEEE Tran. Pattern Anal. Machine Intell. 11:674-693. 291. Starck, J.L., and A. Bijaoui. 1994. A signal processing filtering and deconvolution by the wavelet transform. Signal Proc. 35:195-211. 292. Wolkenstein, M., H. Hutter, S.G. Nikolov, M. Grasserbauer. 1997. Improvement of SIMS image classification by means of wavelet de-noising. Fresenius J. Anal.l Chem. 357:783-788. 293. (a) Barequet, G., and M. Sharir. 1996. Piecewise-linear interpolation between polygonal slices. Comput. Vis. Image Und. 63:251-272. (b) Goshtasby, A., D.A. Turner, and L.V. Ackerman. 1992. Matching of tomographic image slices. IEEE Trans. Med. Imaging 11:507-516. (c) Chen, E., and L. Williams. 1993. View interpolation for image synthesis. SIGGRAPH ’93, http://citeseer.ist.psu.edu/chen93view.html. 294. (a) Geladi, P., and G. Hans. 1996. Multivariate Image Analysis. New York: John Wiley & Sons. (b) Geladi, P., H. Isaksson, L. Lindquist, S. Wold, and K. Esbensen. 1989. Principal component analysis of multivariate images. Chemomet. Intell. Lab. Sys. 5:209-220. (c) de Juan, A., R. Tauler, R. Dyson, C. Marcolli, M. Rault, and M. Maeder. 2004. Spectro-scopic imaging and chemometrics: A powerful combination for global and local sample analysis. Trac-Trends Anal. Chem. 23:70-79. (d) Richards, J.A. 1999. Remote Sensing Digital Image Analysis: An Introduction. New York: Springer. (e) Stubbings, T.C., M.G. Wolkenstein, and H. Hutter. 1999. Comparison of different approaches to analytical images classification. J. Trace Microprobe Tech. 17:1-16. (f) Prutton, M., D.K. Wilkinson, P.G. Kenny, and D.L. Mountain. 1999. Data processing for spectrum-images: Extracting information from the data mountain. Appl. Surf. Sci. 145:1-10.
OCR for page 174
Visualizing Chemistry: The Progress and Promise of Advanced Chemical Imaging 295. (a) Geladi, P., H. Isaksson, L. Lindquist, S. Wold, K. Esbensen. 1989. Principal component analysis of multivariate images. Chemomet. Intell. Lab. Sys. 5:209-220. (b) Prutton, M., D.K. Wilkinson, P.G. Kenny, and D.L. Mountain. 1999. Data processing for spectrum-images: Extracting information from the data mountain. Appl. Surf. Sci. 145:1-10. 296. (a) Wickes, B.T., Y. Kim, and D.G. Castner. 2003. Denoising and multivariate analysis of time-of-flight SIMS images. Surf.Interface Anal. 35:640-648. (b) Coullerez, G., S. Lundmark, E. Malmstrom, A. Hult, and H.J. Mathieu. 2003. ToF-SIMS for the characterization of hyperbranched aliphatic polyesters: Probing their molecular weight on surfaces based on principal component analysis (PCA). Surf. Interface Anal. 35:693-708. (c) Peterson, R.E., and B.J. Tyler. 2003. Surface composition of atmospheric aerosol: Individual particle characterization by TOF-SIMS. Appl. Surf. Sci. 203:751-756. (d) Tyler, B. 2003. Interpretation of TOF-SIMS images: Multivariate and univariate approaches to image de-noising, image segmentation and compound identification. Appl. Surf. Sci. 203:825-831. 297. (a) Kotula, P.G., M.R. Keenan, and J.R. Michael. 2003. Automated analysis of SEM X-ray spectral images: A powerful new microanalysis tool. Microsc. Microanal. 9:1-17. (b) Roseman, A.M. 2004. FindEM—A fast, efficient program for automatic selection of particles from electron micrographs. J. Struct. Biol. 145:91-99. (c) Jembrih, D., M. Schreiner, M. Peev, P. Krejsa, and C. Clausen. 2000. Identification and classification of iridescent glass artifacts with XRF and SEM/EDX. Mikrochim Acta 133:151-157. (d) Flesche, H., A.A. Nielsen, and R. Larsen. 2000. Supervised mineral classification with semiautomatic training and validation set generation in scanning electron microscope energy dispersive spectroscopy images of thin sections. Math. Geol. 32:337-366. (e) Egelandsdal, B., K.F. Christiansen, V. Host, F. Lundby, J.P. Wold, and K. Kvaal. 1999. Evaluation of scanning electron microscopy images of a model dressing using image feature extraction techniques and principal component analysis. Scanning 21:316-325. 298. (a) Artyushkova, K., and J.E. Fulghum. 2002. Multivariate image analysis methods applied to XPS imaging data sets. Surf. Interface Anal. 33:185-195. (b) Peebles, D.E., J.A. Ohlhausen, P.G. Kotula, S. Hutton, and C. Blomfield. 2004. Multivariate statistical analysis for x-ray photoelectron spectroscopy spectral imaging: Effect of image acquisition time. J. Vacuum Sci. Techn. A 22:1579-1586. (c) Walton, J., and N. Fairley. 2004. Quantitative surface chemical-state microscopy by X-ray photoelectron spectroscopy. Surf. Inter. Anal. 36:89-91. 299. (a) Duponchel, L., W. Elmi-Rayaleh, C. Ruckebusch, and J.P. Huvenne. 2003. Multivariate curve resolution methods in imaging spectroscopy: Influence of extraction methods and instrumental perturbations. J.Chem. Info. Com. Sci. 43:2057-2067. (b) Budevska, B.O., S.T. Sum, and T.J. Jones. 2003. Application of multivariate curve resolution for analysis of FT-IR microspectroscopic images of in situ plant tissue. Appl. Spectrosc. 57: 124-131. (c) Budevska, B.O. 2000. Minimization of optical non-linearities in Fourier transform-infrared microspectroscopic imaging. Vibrational Spectrosc. 24:37-45. 300. (a) Zhang, D.M., and D. Ben-Amotz. 2000. Enhanced chemical classification of Raman images in the presence of strong fluorescence interference. Appl. Spectrosc. 54:1379-1383. (b) Sasic, S., D.A. Clark, J.C. Mitchell, and M.J. Snowden. 2004. A comparison of Raman chemical images produced by univariate and multivariate data processing–A simulation with an example from pharmaceutical practice. Analyst 129:1001-1007. (c) Drumm, C.A., and M.D. Morris. 1995. Microscopic Raman line-imaging with principal component analysis. Appl.Spectrosc. 49:1331-1337. (d) Gallagher, N.B., J.M. Shaver, E.B. Martin, J. Morris, B.M. Wise, and W. Windig. 2004. Curve resolution for multivariate images with applications to TOF-SIMS and Raman. Chemomet. Intell. Lab. Sys. 73:105-117.
OCR for page 175
Visualizing Chemistry: The Progress and Promise of Advanced Chemical Imaging 301. (a) Bonnet, N. 1998. Multivariate statistical methods for the analysis of microscope image series: Applications in materials science. J.Microsc.—Oxford 190:2-18. (b) Hadjiiski, L., S. Munster, E. Oesterschulze, and R. Kassing. 1996. Neural network correction of nonlinearities in scanning probe microscope images. J. Vacuum Sci. Tech. B 14:1563-1568. (c) Velthuizen, R.P., L.O. Hall, and L.P. Clarke. 1999. Feature extraction for MRI segmentation. J. Neuro. 9:85-90. 302. (a) Roseman, A.M. 2004. FindEM—a fast, efficient program for automatic selection of particles from electron micrographs. J. Struct. Biol. 145:91-99. (b) Jembrih, D., M. Schreiner, M. Peev, P. Krejsa, and C. Clausen. 2000. Identification and classification of iridescent glass artifacts with XRF and SEM/EDX. Mikrochim. Acta 133:151-157. (c) Flesche, H., A.A. Nielsen, and R. Larsen. 2000. Supervised mineral classification with semiautomatic training and validation set generation in scanning electron microscope energy dispersive spectroscopy images of thin sections. Math. Geol. 32:337-366. (d) Egelandsdal, B., K.F. Christiansen, V. Host, F. Lundby, J.P. Wold, and K. Kvaal. 1999. Evaluation of scanning electron microscopy images of a model dressing using image feature extraction techniques and principal component analysis. Scanning 21:316-325. 303. (a) Tauler, R., A. Smilde, and B.J. Kowalski. 1995. Selectivity, local rank, 3-way data analysis and ambiguity in multivariate curve resolution. J. Chemom. 9:31-58. (b) Duponchel, L., W. Elmi-Rayaleh, C. Ruckebusch, and J.P. Huvenne. 2003. Multivariate curve resolution methods in imaging spectroscopy: Influence of extraction methods and instrumental perturbations. J. Chem. Infor. Com. Sci. 43:2057-2067. 304. (a) Hadjiiski, L., S. Munster, E. Oesterschulze, and R. Kassing. 1996. Neural network correction of nonlinearities in scanning probe microscope images. J. Vacuum Sci. Tech. B 14:1563-1568. (b) Duponchel, L., W. Elmi-Rayaleh, C. Ruckebusch, and J.P. Huvenne. 2003. Multivariate curve resolution methods in imaging spectroscopy: Influence of extraction methods and instrumental perturbations. J. Chem. Info. Com. Sci. 43:2057-2067. 305. MATLAB: The Language of Technical Computing. The Mathworks, Inc., Natick, Massachusetts. 306. PLS_toolbox. Eigenvector Research Inc., Wenatchee, Washington. 307. http://www.mathworks.com/matlabcentral/fileexchange/loadCategory.do. 308. ENVI: The Environment for Visualizing Images. The Research Systems, Inc., Boulder, Colorado. 309. Kotula, P.G., M.R. Keenan, and J.R. Michael. 2003. Automated analysis of SEM X-ray spectral images: A powerful new microanalysis tool. Microsc. Microanal. 9:1-17. 310. Ibid. 311. (a) Smentkowski, V.S., J.A. Ohlhausen, P.G. Kotula, and M.R. Keenan. 2004. Multivariate statistical analysis of time-of-flight secondary ion mass spectrometry images-looking beyond the obvious. Appl. Surf. Sci. 231-232:245-249. (b) Ohlhausen, J.A.T., M.R. Keenan, P.G. Kotula, and D.E. Peebles. 2004. Multivariate statistical analysis of time-of-flight secondary ion mass spectrometry images using AXSIA. Appl. Surf. Sci. 231-232:230-234. 312. Peebles, D.E., J.A. Ohlhausen, P.G. Kotula, S. Hutton, and C. Blomfield. 2004. Multivariate statistical analysis for x-ray photoelectron spectroscopy spectral imaging: Effect of image acquisition time. J. Vacuum Sci. Tech. A 22:1579-1586. 313. (a) Pohl, C. 1999. Tools and methods for fusion of images of different spatial resolution. Int.Arch.Photogram. Remote Sens. 32:W6. (b) Hall, D.L., and S.A.H. McMullen. 2004. Mathematical Techniques in Multisensor Data Fusion. Norwood, Massachusetts: Artech House. 314. Glasbey, C.A., and N.J. Martin. 1996. Multimodal microscopy by digital image processing. J. Microsc.—Oxford 181:225-237.
OCR for page 176
Visualizing Chemistry: The Progress and Promise of Advanced Chemical Imaging 315. Leonardi, A.J., B.A. Blakistone, and S.W. Kyryk. 1990. Application of microscopy in the paper industry: Case histories of the Mead corporation. Food Struc. 9:203-213. 316. Stephens, D.J., and V.J. Allan. 2003. Light microscopy techniques for live cell imaging. Science 300:82-86. 317. Richards, J.A. 1999. Remote Sensing Digital Image Analysis: An Introduction. New York: Springer. 318. (a) Artyushkova, K., and J. Fulghum. 2003. XPS imaging. In Surface Analysis by Auger and X-ray Photoelectron Spectroscopy, D. Briggs, and J.T. Grant, eds. Chichester, Manchester, U.K.: IM publications and SurfaceSpectra Limited. (b) Artyushkova, K., B. Wall, J. Koenig, and J.E. Fulghum. 2000. Correlative spectroscopic imaging: XPS and FT-IR studies of PVC/PMMA polymer blends. Appl. Spectrosc. 54:1549-1558. 319. Clarke, F.C., M.J. Jamieson, D.A. Clark, S.V. Hammond, R.D. Jee, and A.C. Moffat. 2001. Chemical image fusion. The synergy of FT-NIR and Raman mapping microscopy to enable a more complete visualization of pharmaceutical formulations. Anal. Chem. 73:2213-2220. 320. Carugo, O., and S. Pongor. 2002. The evolution of structural databases. Trends Biotechnol. 20:498-501. 321. (a) Deshpande, N., K.J. Addess, W.F. Bluhm, J.C. Merino-Ott, W. Townsend-Merino, Q. Zhang, C. Knezevich, L. Xie, L. Chen, Z.K. Feng, R.K. Green, J.L. Flippen-Anderson, J. Westbrook, H.M. Berman, and P.E. Bourne. 2005. The RCSB Protein Data Bank: A redesigned query system and relational database based on the mmCIF schema. Nucleic Acids Res. 33:D233-D237. (b) Venclovas, C., K. Ginalski, and C. Kang. 2004. Sequence-structure mapping errors in the PDB: OB-fold domains. Protein Sci. 13:1594-1602. (c) Berman, H.M., J. Westbrook, Z. Feng, G. Gilliland, T.N. Bhat, H. Weissig, I.N. Shindyalov, and P.E. Bourne. 2000. The Protein Data Bank. Nucleic Acids Res. 28:235-242. 322. (a) Fischer, D., J. Pas, and L. Rychlewski. 2004. The PDB-Preview database: A repository of in-silico models of “on-hold” PDB entries. Bioinformatics (Oxford) 20:2482-2484. (b) Fitzkee, N.C., P.J. Fleming, and G.D. Rose. 2005. The protein coil library: A structural database of nonhelix, nonstrand fragments derived from the PDB. Proteins—Struc. Function Bioinform. 58:852-854. 323. Nattkemper, T.W. 2004. Multivariate image analysis in biomedicine. J. Biomed. Inform. 37: 380-391. 324. Brown, J., D. Buckley, A. Coulthard, A.K. Dixon, J.M. Dixon, D.F. Easton, R.A. Eeles, D.G. Evans, F.G. Gilbert, M. Graves, C. Hayes, J.P. Jenkins, A.P. Jones, S.F. Keevil, M.O. Leach, G.P. Liney, S.M. Moss, A.R. Padhani, G.J. Parker, L.J. Pointon, B.A. Ponder, T.W. Redpath, J.P. Sloane, L.W. Turnbull, L.G. Walker, and R.M. Warren. 2000. Magnetic resonance imaging screening in women at genetic risk of breast cancer: Imaging and analysis protocol for the UK multicentre study. Magn. Reson. Imaging 18(7):765-776. 325. (a) Van Horn, J.D., J.S. Grethe, P. Kostelec, J.B. Woodward, J.A. Aslam, D. Rus, D. Rockmore, and M.S. Gazzaniga. 2001. The Functional Magnetic Resonance Imaging Data Center (fMRIDC): The challenges and rewards of large-scale databasing of neuroimaging studies. Philosoph. Trans. R. Soc. Lond. B Biol. Sci. 356:1323-1339. (b) Van Horn, J.D., and M.S. Gazzaniga. 2002. Databasing fMRI studies—Towards a “discovery science” of brain function. Nat. Rev. Neurosci. 3:314-318. 326. Van Horn, J.D., and M.S. Gazzaniga. 2002. Databasing fMRI studies—Towards a “discovery science” of brain function. Nat. Rev. Neurosci. 3:314-318. 327. Gonzalez-Couto, E., B. Hayes, and A. Danckaert. 2001. The life sciences Global Image Database (GID). Nucleic Acids Res. 29:336-339. 328. (a) Fayyad, U., D. Haussler, and P. Stolorz. 1996. Mining scientific data. Commun. ACM 39:51-57. (b) Hsu, W., M.L. Lee, and K.G. Goh. 2000. Image mining in IRIS: Integrated retinal information system. SIGMOD Record 29:593-593.
OCR for page 177
Visualizing Chemistry: The Progress and Promise of Advanced Chemical Imaging 329. Cooke, C.D., C. Ordonez, E.V. Garcia, E. Omiecinski, E.G. Krawczynska, R.D. Folks, C.A. Santana, L. DeBraal, and N.F. Ezquerra. 1999. Data mining of large myocardial perfusion SPECT (MPS) databases to improve diagnostic decision making. J. Nucl. Med. 40(suppl.S): 293P-293P. 330. Andrienko, G., and N. Andrienko. 1999. Knowledge-based visualization to support spatial data mining. Lect. Notes Comp. Sci. 1642:149-160. 331. (a) Allen, M.P., and D.J. Tildesley. 1989. Computer Simulation of Liquids. Oxford: Clarendon Press. (b) Frenkel, D., and B. Smit. 2002. Understanding Molecular Simulation. San Diego, CA: Academic Press. 332. Larson, S.M., C.D. Snow, M. Shirts, and V.S. Pande. 2002. Computational Genomics, Grant, R., ed. Norwich, UK: Horizon Press. 333. Todorov, I.T., and W. Smith. 2004. DL_POLY_3: The CCP5 national UK code for molecular-dynamics simulations. One contribution of 12 to a theme “Discrete element modelling: Methods and applications in the environmental sciences.” Phil. Trans. R. Soc. Lond. A 362:1835-1852. 334. Kendall, R.A., E. Apra, D.E. Bernholdt, E.J. Bylaska, M. Dupuis, G.I. Fann, R.J. Harrison, J. Ju, J.A. Nichols, J. Nieplocha, T.P. Straatsma, T.L. Windus, and A.T. Wong. 2000. High performance computational chemistry: An overview of NWChem a distributed parallel application. Computer Phys. Comm. 128:260-283. 335. (a) Brooks, B.R., R.E. Bruccoleri, B.D. Olafson, D.J. States, S. Swaminathan, and M. Karplus. 1983. CHARMM: A program for macromolecular energy, minimization, and dynamics calculations. J. Comp. Chem. 4:187-217. (b) MacKerell, Jr., A.D., B. Brooks, C.L. Brooks, III, L. Nilsson, B. Roux, Y. Won, and M. Karplus. 1998. CHARMM: The energy function and its parameterization with an overview of the program. The Encyclopedia of Computational Chemistry, Schleyer, P.v.R., et al., eds. Chichester, U.K.: John Wiley & Sons. 336. (a) Ponder, J.W., and F.M. Richards. 1987. An efficient newton-like method for molecular mechanics energy minimization of large molecules. J. Comput. Chem. 8:1016-1024. (b) Ren, P., and J.W. Ponder. 2002. Consistent treatment of inter- and intramolecular polarization in molecular mechanics calculations. J. Comput. Chem. 23:1497-1506. 337. van der Spoel, D., E. Lindahl, B. Hess, A.R. van Buuren, E. Apol, P.J. Meulenhoff, D.P. Tieleman, A.L.T.M. Sijbers, K.A. Feenstra, R. van Drunen, and H.J.C. Berendsen. 2004. Gromacs User Manual Version 3.2 www.gromacs.org. 338. Laxmikant, K., R. Skeel, M. Bhandarkar, R. Brunner, A. Gursoy, N. Krawetz, J. Phillips, A. Shinozaki, K. Varadarajan, and K. Schulten. 1999. NAMD2: Greater scalability for parallel molecular dynamics. J. Comp. Phys. 151:283-312. 339. Siepmann, J.I. 2001. Challenges in the development of transferable force fields for phase equilibrium calculations. in Forum 2000: Fluid Properties for New Technologies, Connecting Virtual Design with Physical Reality, Rainwater, J.C., et al., eds. Pp. 110-112. Boulder, CO: NIST Special Publication. 340. Bolton, K., W.L. Hase, and G.H. Peshlherbe. 1998. Modern Methods for Multidimensional Dynamics Computation in Chemistry, Thompson, D.L., ed. Singapore: World Scientific. 341. Car, R., and M. Parrinello. 1985. Unified approach for molecular dynamics and density-functional theory. Phys. Rev. Lett. 55:2471. 342. (a) Schlegel, H.B., J.M. Millam, S.S. Iyengar, G.A. Voth, A.D. Daniels, G.E. Scuseria, and M.J. Frisch. 2001. Ab initio molecular dynamics: Propagating the density matrix with Gaussian orbitals. J. Chem. Phys. 114:9758-9763. (b) Iyengar, S.S., H.B. Schlegel, J.M. Millam, G.A. Voth, G.E. Scuseria, and M.J. Frisch. 2001. Ab initio molecular dynamics: Propagating the density matrix with Gaussian orbitals. II. Generalizations based on mass-weighting, idempotency, energy conservation and choice of initial conditions. J. Chem. Phys. 115:10291-10302.
OCR for page 178
Visualizing Chemistry: The Progress and Promise of Advanced Chemical Imaging (c) Schlegel, H.B., S.S. Iyengar, X. Li, J.M. Millam, G.A. Voth, G.E. Scuseria, and M.J. Frisch. 2002. Ab initio molecular dynamics: Propagating the density matrix with Gaussian orbitals. III. Comparison with Born-Oppenheimer dynamics. J. Chem. Phys. 117:8694-8704. 343. (a) Yip, S. 2003. Synergistic materials science. Nat. Mat. 2:3. (b) Yip, S., ed. 2005. Handbook of Materials Modeling. New York: Springer. 344. Ibid.
Representative terms from entire chapter: