ACS Physical Chemistry Au最新文献

Today, we witness how our scientific ecosystem tries to accommodate a new form of intelligence, artificial intelligence (AI). To make the most of AI in materials science, we need to make the data from computational and laboratory experiments machine-readable, but while that works well for computational experiments, integrating laboratory hardware into a digital workflow seems to be a formidable barrier toward that goal. This paper explores measurement services as a way to lower this barrier. I envision the Entity for Multivariate Material Analysis (EMMA), a centralized service that offers measurement bundles tailored for common research needs. EMMA’s true strength, however, lies in its software ecosystem to treat, simulate, and store the measured data. Its close integration of measurements and their simulation not only produces metadata-rich experimental data but also provides a self-consistent framework that links the sample with a snapshot of its digital twin. If EMMA was to materialize, its database of experimental data connected to digital twins could serve as the fuel for physics-informed machine learning and a trustworthy horizon of expectations for material properties. This drives material innovation since knowing the statistics helps find the exceptional. This is the EMMA approach: fast-tracking material innovation by integrated measurement and software services.

Quantifying the rapid conformational dynamics of biological systems is fundamental to understanding the mechanism. However, biomolecules are complex, often containing static and dynamic heterogeneity, thus motivating the use of single-molecule methods, particularly those that can operate in solution. In this study, we measure microsecond conformational dynamics of solution-phase DNA hairpins at the single-molecule level using an anti-Brownian electrokinetic (ABEL) trap. Different conformational states were distinguished by their fluorescence lifetimes, and kinetic parameters describing transitions between these states were determined using two-dimensional fluorescence lifetime correlation (2DFLCS) analysis. Rather than combining fluorescence signals from the entire data set ensemble, long observation times of individual molecules allowed ABEL-2DFLCS to be performed on each molecule independently, yielding the underlying distribution of the system’s kinetic parameters. ABEL-2DFLCS on the DNA hairpins resolved an underlying heterogeneity of fluorescence lifetimes and provided signatures of two-state exponential dynamics with rapid (<millisecond) transition times between states without observation of the substantially stretched exponential kinetics that had been observed in previous measurements on diffusing molecules. Numerical simulations were performed to validate the accuracy of this technique and the effects the underlying heterogeneity has on the analysis. Finally, ABEL-2DFLCS was performed on a mixture of hairpins and used to resolve their kinetic data.

Spectroscopic techniques, especially Raman spectroscopy, cover a large subset in the teaching and research domain of physical chemistry. Raman spectroscopy, and other Raman based techniques, establishes itself as a powerful analytical tool with diverse applications across scientific, industrial, and natural science (including biology and pharmacy) fields and helps in the progress of physical chemistry. Recent advancements and future prospects in Raman spectroscopy, focusing on key areas of innovation and potential directions for research and development, have been highlighted here along with some of the challenges that need to be addressed to prepare Raman based techniques for the future. Significant progress has been made in enhancing the sensitivity, spatial resolution, and time resolution of Raman spectroscopy techniques. Raman spectroscopy has applications in all areas of research but especially in biomedical applications, where Raman spectroscopy holds a great promise for noninvasive or minimally invasive diagnosis, tissue imaging, and drug monitoring. Improvements in instrumentation and laser technologies have enabled researchers to achieve higher sensitivity levels, investigate smaller sample areas with improved spatial resolution, and capture dynamic processes with high temporal resolution. These advancements have paved the way for a deeper understanding of molecular structure, chemical composition, and dynamic behavior in various materials and biological systems. It is high time that we consider whether Raman based techniques are ready to be improved based on the strength of the current era of AI/ML and quantum technology.

We report efforts to quantify the loading of cell-sized lipid vesicles using in-line digital holographic microscopy. This method does not require fluorescent reporters, fluorescent tracers, or radioactive tracers. A single-color LED light source takes the place of conventional illumination to generate holograms rather than bright field images. By modeling the vesicle’s scattering in a microscope with a Lorenz–Mie light scattering model and comparing the results to data holograms, we are able to measure the vesicle’s refractive index and thus loading. Performing the same comparison for bulk light scattering measurements enables the retrieval of vesicle loading for nanoscale vesicles.

There has been a recent interest in quantum algorithms for the modeling and prediction of nonunitary quantum dynamics using current quantum computers. The field of quantum biology is one area where these algorithms could prove to be useful as biological systems are generally intractable to treat in their complete form but amenable to an open quantum systems approach. Here, we present the application of a recently developed singular value decomposition (SVD) algorithm to two systems in quantum biology: excitonic energy transport through the Fenna–Matthews–Olson complex and the radical pair mechanism for avian navigation. We demonstrate that the SVD algorithm is capable of capturing accurate short- and long-time dynamics for these systems through implementation on a quantum simulator and conclude that while the implementation of this algorithm is beyond the reach of current quantum computers, it has the potential to be an effective tool for the future study of systems relevant to quantum biology.