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Short-wave infrared (SWIR) light, 0.9–2.5 μm wavelengths, has widespread applications, including inspection processes, nighttime imaging, and machine vision. As such, there is increasing demand for practical and effective SWIR detectors. Many current SWIR photodetectors are based on high-cost materials and require cryogenic cooling. Perovskite materials, including CsPbI3, have been effectively used as photodetectors in the UV to near IR ranges, but their large bandgaps limit their use for lower energy SWIR light. In this report we introduce an all-inorganic perovskite photodetector based on CsPbI3 with heterojunction engineering for efficient and practical detection in the SWIR range at room temperature. The devices undergo a simple, solution-based fabrication process which includes spin-coating under ambient conditions and moderate annealing temperatures. Without additional cooling, the SWIR devices produce excellent results at room temperature with responsivity of 1.65 × 103 A W−1 and a specific detectivity of 8.0 × 1010 Jones under 0.28 mW cm−2 of 1310 nm light and bias of −5 V. This material shows not only high response but also high sensitivity, making it stand out in the field of SWIR photodetection with the additional benefits of low-cost production and room temperature operation. 

Bulk 2D electronic–vibrational (2D-EV) and 2D vibrational–electronic spectroscopies (2D-VE) were previously developed to correlate the electronic and vibrational degrees of freedom simultaneously, which allow for the study of couplings between electronic and vibrational transitions in photo-chemical systems. Such bulk-dominated methods have been used to extensively study molecular systems, providing unique information such as coherence sensitivity, molecular configurations, enhanced resolution, and correlated states and their dynamics. However, the analogy of interfacial 2D spectroscopy has fallen behind. Our recent work presented interface-specific 2D-EV spectroscopy (i2D-EV). In this work, we develop interface-specific two-dimensional vibrational–electronic spectroscopy (i2D-VE). The fourth-order spectroscopy is based on a Mach–Zehnder IR interferometer that accurately controls the time delay of an IR pump pulse pair for vibrational transitions, followed by broadband interface second-harmonic generation to probe electronic transitions. We demonstrate step-by-step how a fourth-order i2D-VE spectrum of AP3 molecules at the air/water interface was collected and analyzed. The line shape and signatures of i2D-VE peaks reveal solvent correlations and the spectral nature of vibronic couplings. Together, i2D-VE and i2D-EV spectroscopy provide coupling of different behaviors of the vibrational ground state or excited states with electronic states of molecules at interfaces and surfaces. The methodology presented here could also probe dynamic couplings of electronic and vibrational motions at interfaces and surfaces, extending the usefulness of the rich data that are obtained. 

Copper-catalyzed radical C(sp³)‒N coupling has become a major focus in synthetic catalysis over the past decade. However, achieving this reaction manifold by using enzymes has remained elusive. In this study, we introduce a photobiocatalytic approach for radical benzylic C(sp³)‒N coupling using a copper-substituted nonheme enzyme. Using rhodamine B as a photoredox catalyst, we identified a copper-substituted phenylalanine hydroxylase that facilitates enantioconvergent decarboxylative amination betweenN-hydroxyphthalimide esters and anilines. Directed evolution remodeled the active site, resulting in high enantioselectivities for most substrates. On the basis of molecular modeling and mechanistic studies, we propose that the enzyme accommodates a copper-anilide complex that reacts with a benzylic radical. This study expands the scope of non-natural biocatalytic transition metal catalysis to copper-catalyzed radical coupling. 

This paper presents the Machine Learned Diffusion Coefficient Estimator, a comprehensive machine learning framework designed to predict diffusion coefficients in impure metallic (IM) and multi-component alloy (MCA) media. The framework incorporates five machine learning models, each tailored to specific diffusion modes: (1) impurity and (2) self-diffusion in IM media, and (3) self, (4) impurity, and (5) chemical diffusion in MCA media. These models use statistical aggregations of atomic descriptors for both the diffusing elements and the diffusion media, along with the temperature of the diffusion process, as features. Models are trained using the random forest and deep neural network algorithms, with performance evaluated through the coefficient of determination (R2), mean squared error (MSE), and uncertainty estimates. The models within this framework achieve an impressive R2 score above 0.90 with MSE less than 10−16 m2/s, demonstrating high predictive accuracy and reliability for diffusion coefficient.