We demonstrate instrumentation and methods to enable fluorescence-detected photothermal infrared (F-PTIR) microscopy and then demonstrate the utility of F-PTIR to characterize the composition within phase-separated domains of model amorphous solid dispersions (ASDs) induced by water sorption. In F-PTIR, temperature-dependent changes in fluorescence quantum efficiency are shown to sensitively report on highly localized absorption of mid-infrared radiation. The spatial resolution with which infrared spectroscopy can be performed is dictated by fluorescence microscopy, rather than the infrared wavelength. Intrinsic ultraviolet autofluorescence of tryptophan and protein microparticles enabled label-free F-PTIR microscopy. Following proof of concept F-PTIR demonstration on model systems of polyethylene glycol (PEG) and silica gel, F-PTIR enabled the characterization of chemical composition within inhomogeneous ritonavir/polyvinylpyrrolidone-vinyl acetate (PVPVA) amorphous dispersions. Phase separation is implicated in the observation of critical behaviors in ASD dissolution kinetics, with the results of F-PTIR supporting the formation of phase-separated drug-rich domains upon water sorption in spin-cast films.

Fourier transform fluorescence recovery after photobleaching (FT-FRAP) with patterned illumination is theorized and demonstrated for quantitatively evaluating normal and anomalous diffusion. Diffusion characterization is routinely performed to assess mobility in cell biology, pharmacology, and food science. Conventional FRAP is noninvasive, has low sample volume requirements, and can rapidly measure diffusion over distances of a few micrometers. However, conventional point-bleach measurements are complicated by signal-to-noise limitations, the need for precise knowledge of the bleach beam profile, potential for bias due to sample heterogeneity, and poor compatibility with multi-photon excitation due to local heating. In FT-FRAP with patterned illumination, the time-dependent fluorescence recovery signal is concentrated to puncta in the spatial Fourier domain through patterned bleaching, with substantial improvements in signal-to-noise, mathematical simplicity, representative sampling, and multiphoton compatibility. A custom nonlinear-optical beam-scanning microscope enabled patterned illumination for photobleaching through two-photon excitation. Measurements in the spatial Fourier domain removed dependence on the bleach profile, suppressing bias from imprecise knowledge of the point spread function. For normal diffusion, the fluorescence recovery produced a simple single-exponential decay in the spatial Fourier domain, in excellent agreement with theoretical predictions. Simultaneous measurement of diffusion at multiple length scales was enabled through analysis of multiple spatial harmonics of the bleaching pattern. Anomalous diffusion was characterized by FT-FRAP through a nonlinear fit to multiple spatial harmonics of the fluorescence recovery. Constraining the fit to describe diffusion over multiple length scales resulted in higher confidence in the recovered fitting parameters. Additionally, phase analysis in FT-FRAP was shown to inform on flow/sample translation. Statement of SignificanceFourier transform fluorescence recovery after photobleaching (FT-FRAP) with patterned illumination greatly improves the accuracy of diffusion assessments and simultaneously accesses information on both normal and anomalous diffusion in a single experiment..

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.

Stochastic phase transformations within individual crystalline particles were recorded by integration of second harmonic generation (SHG) imaging with differential scanning calorimetry (DSC). The SHG activity of a crystal is highly sensitive to the specific molecular packing arrangement within a noncentrosymmetric lattice, providing access to information otherwise unavailable by conventional imaging approaches. Consequently, lattice transformations associated with dehydration/desolvation events were readily observed by SHG imaging and directly correlated to the phase transformations detected by the DSC measurements. Following studies of a model system (urea), stochastic differential scanning calorimetry (SDSC) was performed on trehalose dihydrate, which has a more complex phase behavior. From these measurements, SDSC revealed a broad diversity of single-particle thermal trajectories and direct evidence of a “cold phase transformation” process not observable by the DSC measurements alone.

The use of periodically structured illumination coupled with spatial Fourier-transform fluorescence recovery after photobleaching (FT-FRAP) was shown to support diffusivity mapping within segmented domains of arbitrary shape. Periodic "comb-bleach" patterning of the excitation beam during photobleaching encoded spatial maps of diffusion onto harmonic peaks in the spatial Fourier transform. Diffusion manifests as a simple exponential decay of a given harmonic, improving the signal to noise ratio and simplifying mathematical analysis. Image segmentation prior to Fourier transformation was shown to support pooling for signal to noise enhancement for regions of arbitrary shape expected to exhibit similar diffusivity within a domain. Following proof-ofconcept analyses based on simulations with known ground-truth maps, diffusion imaging by FT-FRAP was used to map spatiallyresolved diffusion differences within phase-separated domains of model amorphous solid dispersion spin-cast thin films. Notably, multi-harmonic analysis by FT-FRAP was able to definitively discriminate and quantify the roles of internal diffusion and exchange to higher mobility interfacial layers in modeling the recovery kinetics within thin amorphous/amorphous phase-separated domains, with interfacial diffusion playing a critical role in recovery. These results have direct implications for the design of amorphous systems for stable storage and efficacious delivery of therapeutic molecules.