Nonlinear optical Stokes ellipsometric (NOSE) microscopy was demonstrated for the analysis of collagen-rich biological tissues. NOSE is based on polarization-dependent second harmonic generation imaging. NOSE was used to access the molecular-level distribution of collagen fibril orientation relative to the local fiber axis at every position within the field of view. Fibril tilt-angle distribution was investigated by combining the NOSE measurements with ab initio calculations of the predicted molecular nonlinear optical response of a single collagen triple helix. The results were compared with results obtained previously by scanning electron microscopy, nuclear magnetic resonance imaging, and electron tomography. These results were enabled by first measuring the laboratory-frame Jones nonlinear susceptibility tensor, then extending to the local-frame tensor through pixel-by-pixel corrections based on local orientation.
Thiolated gold nanoclusters (AuNCs), sub-2 nm Au particles capped by Au(I) thiolate complexes, promise to have a myriad of applications in biomedical diagnosis and therapy as well as industrial catalysis, energy production, and monitoring of environmental pollutants. Computational simulations are a valuable tool in elucidating design principles for optimizing application-specific physicochemical properties. However, thiolated AuNCs protected, conjugated, and/or interacting with macromolecules often exceed the limit of computational tractability with present-day quantum chemistry software. To facilitate theoretical studies, a molecular mechanics force field, AuSBio, is presented that reasonably reproduces, and retains, characteristic structural features of perhaps the most intensively studied thiolated AuNC, Au25L18 (L = alkylthiolate), over 2 ns finite temperature molecular dynamics simulations. AuSBio was parametrized within the framework of force fields for (bio)organic simulations to reproduce equilibrium structures and the vibrational density of states for small homoleptic and larger thiolated Au clusters. AuSBio was further validated by the ability to reproduce the experimental structure of Au38L24, as well as bundling of long-chain alkylthiolate ligands, and the nonlinear frequency modulation pattern of a Raman-active vibrational mode, observed experimentally for the Au25 cluster. We envision our AuSBio force field facilitating, in a practical manner, molecular mechanics or hybrid quantum/molecular mechanics simulations on the structure and dynamics of thiolated AuNC bioconjugates and AuNC monolayer-mediated molecular recognition and catalysis events.
A Mueller tensor mathematical framework was applied for predicting and interpreting the second harmonic generation (SHG) produced with an unpolarized fundamental beam. In deep tissue imaging through SHG and multiphoton fluorescence, partial or complete depolarization of the incident light complicates polarization analysis. The proposed framework has the distinct advantage of seamlessly merging the purely polarized theory based on the Jones or Cartesian susceptibility tensors with a more general Mueller tensor framework capable of handling partial depolarized fundamental and/or SHG produced. The predictions of the model are in excellent agreement with experimental measurements of z-cut quartz and mouse tail tendon obtained with polarized and depolarized incident light. The polarization-dependent SHG produced with unpolarized fundamental allowed determination of collagen fiber orientation in agreement with orthogonal methods based on image analysis. This method has the distinct advantage of being immune to birefringence or depolarization of the fundamental beam for structural analysis of tissues.
A mathematical framework to treat partial polarization in second harmonic generation imaging of nonlinear optical susceptibility is described and applied to imaging tissue sections 5, 40, and 70 μm thick, sufficient to introduce significant depolarization of the incident field. Polarization analysis becomes complicated in turbid media, in which scattering can result in degradation of polarization purity. The simplest framework for describing the polarization of purely polarized light is the Jones framework, which has been applied to great effect in the polarization analysis of second harmonic generation. However, the Jones framework lacks the necessary generality to describe a partially polarized electric field, (i.e., ones positioned within the volume of the Poincaré sphere rather than on the surface). Recent work connecting the Jones framework to the Mueller–Stokes framework has enabled interpretation of results with the more intuitive Jones framework while maintaining generality of the Mueller–Stokes method. The magnitude and nature of linear interactions of the tissue with the incident infrared field are discussed. Despite substantial depolarization, the nonlinear optical susceptibility tensor elements of collagen was recoverable at each pixel images of thick tissue utilizing the described framework. For thick and thin tissues, values of the tensor element ratio ρ were recovered in good agreement with previous studies. Both hyperpolarizing and depolarizing effects of SHG were observed, and the mechanism of hyperpolarization was determined to rest upon the interplay of orientation and relative contribution of polarized and depolarized incident light to elicit SHG.
Machine learning tools are emerging to support autonomous science, in which critical decision-making on experimental design is conducted by algorithms rather than by human intervention. This shift from automation to autonomation is enabled by rapid advances in data science and deep neural networks, which provide new strategies for mining the ever-increasing volumes of data produced by modern instrumentation. However, a large number of measurements are intrinsically incompatible with high-throughput analyses, limited by time, the availability of materials, or the measurement architecture itself. Counter-intuitively, strategies developed for big-data challenges have the potential for major impacts in such data-limited problems. Two strategies for leveraging “big data” tools for small data challenges form the central theme of this chapter. In the first, advances in autonomous design of experiments are reviewed, in which algorithms select in real-time the next most informative experiments to perform based on results from previous measurements. Autonomous science enables maximization of confidence in scientific decision-making while simultaneously minimizing the number of measurements required to achieve that confidence. In the second, recent advances in adversarial strategies are reviewed for improving chemical decision-making with limited data. Adversarial attacks can help identify weak-points in classification and dimension reduction approaches that naturally arise in data-sparse training. Once identified, generative adversarial approaches provide a framework for “shoring up” those weak points by optimally leveraging the underlying probability distributions describing the input data. These illustrative examples highlight the rapidly evolving landscape of chemical measurement science enabled by machine learning.
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