N-methyl-D-aspartate (NMDA) receptors are the main calcium-permeable excitatory receptors in the mammalian central nervous system. The NMDA receptor gating is complex, exhibiting multiple closed, open, and desensitized states; however, the central questions regarding the conformations and energetics of the transmembrane domains as they relate to the gating states are still unanswered. Here, using single molecule Förster Resonance Energy Transfer (smFRET), we map the energy landscape of the first transmembrane segment of the Rattus norvegicus NMDA receptor under resting and various liganded conditions. These results show kinetically and structurally distinct changes associated with apo, agonist-bound, and inhibited receptors linked by a linear mechanism of gating at this site. Furthermore, the smFRET data suggest that allosteric inhibition by zinc occurs by an uncoupling of the agonist-induced changes at the extracellular domains from the gating motions leading to an apo-like state, while dizocilpine, a pore blocker, stabilizes multiple closely packed transmembrane states.

Super-resolution microscopy with phase masks is a promising technique for 3D imaging and tracking. Due to the complexity of the resultant point spread functions, generalized recovery algorithms are still missing. We introduce a 3D super-resolution recovery algorithm that works for a variety of phase masks generating 3D point spread functions. A fast deconvolution process generates initial guesses, which are further refined by least squares fitting. Overfitting is suppressed using a machine learning determined threshold. Preliminary results on experimental data show that our algorithm can be used to super-localize 3D adsorption events within a porous polymer film and is useful for evaluating potential phase masks. Finally, we demonstrate that parallel computation on graphics processing units can reduce the processing time required for 3D recovery. Simulations reveal that, through desktop parallelization, the ultimate limit of real-time processing is possible. Our program is the first open source recovery program for generalized 3D recovery using rotating point spread functions.

Developing a mechanistic understanding of protein dynamics and conformational changes at polymer interfaces is critical for a range of processes including industrial protein separations. Salting out is one example of a procedure that is ubiquitous in protein separations yet is optimized empirically because there is no mechanistic description of the underlying interactions that would allow predictive modeling. Here, we investigate peak narrowing in a model transferrin–nylon system under salting out conditions using a combination of single-molecule tracking and ensemble separations. Distinct surface transport modes and protein conformational changes at the negatively charged nylon interface are quantified as a function of salt concentration. Single-molecule kinetics relate macroscale improvements in chromatographic peak broadening with microscale distributions of surface interaction mechanisms such as continuous-time random walks and simple adsorption–desorption. Monte Carlo simulations underpinned by the stochastic theory of chromatography are performed using kinetic data extracted from single-molecule observations. Simulations agree with experiment, revealing a decrease in peak broadening as the salt concentration increases. The results suggest that chemical modifications to membranes that decrease the probability of surface random walks could reduce peak broadening in full-scale protein separations. More broadly, this work represents a proof of concept for combining single-molecule experiments and a mechanistic theory to improve costly and time-consuming empirical methods of optimization.

Electron microscopy is often required to correlate the size and shape of plasmonic nanoparticles with their optical properties. Eliminating the need for electron microscopy is one crucial step toward in situ sensing applications, especially for complicated sample conditions such as during irreversible chemical reactions or when particles are embedded in a matrix. Here, we show that a machine learning decision tree can accurately predict gold nanorod dimensions over a wide range of sizes. The model is trained by using ∼450 nanorod geometries and corresponding scattering spectra obtained from finite-difference time-domain simulations. We test the model using a set of experimental spectra and sizes obtained from correlated scanning electron microscopy images, resulting in predictions of the dimensions of gold nanorods within ∼10% of their true values (root-mean-squared percentage error) over a large range of sizes. Analysis of the decision tree structure reveals that a relationship with resonance energy and line width of the localized surface plasmon resonance is sufficient to predict nanorod dimensions, notably outperforming more complicated models. Our findings illustrate the advantages of using machine learning models to infer single particle structural features from their optical spectra.

We investigate the source of Raman background signal commonly misidentified as fluorescence in nonaqueous electrolytes via a variety of spectroscopies (Raman, fluorescence, NMR) and find evidence of hydrogen-bonding interactions. This hydrogen bonding gives rise to broadband anharmonic vibrational modes and suggests that anions play an important and underappreciated role in the structure of nonaqueous electrolytes. Controlling electrolyte structure has important applications in advancing in operando spectroscopy measurements as well as understanding the stability of high concentration electrolytes for next-generation electrochemical energy storage devices.