Whole‐cell photobiocatalytic H2 production is constrained by hydrogenase O2 sensitivity, photosynthetic electron allocation, and cellular physiology. We present a systematic framework to identify and alleviate these interacting limitations using Synechocystis sp. PCC 6803 combined with Pseudomonas taiwanensis VLB120 for extracellular O2 microenvironment control. Rapid O2 diffusion over micrometer distances enabled efficient extracellular O2 scavenging, lowering local O2 exposure of the intracellular native [NiFe] hydrogenase and sustaining light‐driven H2 production from water. We systematically evaluated genetic, physiological, and reaction parameters affecting H2 production. The ndhB mutant lacking functional NDH‐1 complexes achieved the highest rates, indicating that suppressing competing electron sinks, including NDH‐1‐dependent cyclic electron flow and CO2 uptake, increases electron availability for H2 evolution. Optimization of pH, nitrogen availability, and cellular physiology enhanced performance. Under optimized conditions, H2 production was sustained for >72 h, reaching ~1500 µmol H2 gCDW−1, averaging ~0.35 µmol H2 gCDW−1 min−1, at least fivefold above a current Synechocystis benchmark, with estimated ~1% light‐to‐H2 efficiency. Inhibition of Photosystem II abolished H2 formation, demonstrating that water oxidation, rather than carbon metabolism, supplied the predominant electrons. Nitrogen limitation further reduced H2 uptake and stabilized net H2 accumulation. This workflow provides a transferable methodology for systematically optimizing complex whole‐cell photobiocatalytic systems.
Proton nuclear magnetic resonance spectra are perennially overcrowded because the signals of chemically different protons span a narrow range of frequencies, and interactions between them yield multiple signals per proton. It has been known for 50 years that separating these two effects, chemical shift and scalar coupling, into different spectral dimensions could greatly improve spectral resolution, and hence facilitate structure elucidation. Experimental implementations to date have failed because they cannot measure the spin behavior for negative times. We show here, for a wide range of samples, that the missing information can be reconstructed from the experimental evidence. This yields both cleanly resolved two‐dimensional spectra, and a highly effective way to obtain simple “pure shift” one‐dimensional proton spectra without homonuclear multiplet structure.
Electrophotocatalysis is the latest offshoot of two of the most innovative synthetic approaches in modern organic chemistry: on one hand, photoredox catalysis, which harnesses light to activate small molecules and construct new carbon–carbon and carbon–heteroatom bonds; and on the other, electrochemistry, which allows fine control over oxidation states while avoiding the use of stoichiometric redox reagents. The combination of light and electricity as energy sources has enabled access to high‐energy excited states with considerable redox potentials, opening new synthetic avenues. In this review, the key aspects of this methodology are discussed, and the most relevant recent applications of both homogeneous and heterogeneous catalysis are presented. Furthermore, the prominent role of polycyclic aromatic compounds as electrophotocatalysts is highlighted, supported by several illustrative examples.
Chemical imaging is a powerful branch of modern microscopy encumbered by a lack of flexible, high‐throughput analysis tools. Bespoke analytical pipelines typically perform ratiometric analysis on two layers in a multispectral image to describe the relative composition of molecules in a sample. This strategy has been implemented across fields, spanning histopathology, cell biology, environmental science, and materials science. The commercialization of chemical imaging microscopes has facilitated the collection of large multispectral datasets, necessitating accessible ways to process them. This paper describes multispectral analysis graphical user interface (msaGUI), a desktop graphical user interface to analyze individual and batch datasets of multispectral images. Data are loaded as CSV, TSV, or TIFFs and processed through a user‐defined sequence of modular image operations that can be flexibly combined, e.g., to reduce spectral crosstalk or background noise. After analysis, data are visualized as exportable images, histograms, and statistics. To yield publication‐quality figures, output images are fully customizable. Written in Python with open‐source libraries, the msaGUI program is packaged into an executable for Windows and Mac for a fully no‐code application. Other operating systems are supported via the Python source code. In summary, msaGUI provides a rapid, graphical solution for analyzing and visualizing multispectral data.
