Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning

Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental times. We present a proof‐of‐concept of the application of deep learning and neural networks for high‐quality, reliable, and very fast NMR spectra reconstruction from limited experimental data. We show that the neural network training can be achieved using solely synthetic NMR signals, which lifts the prohibiting demand for a large volume of realistic training data usually required for a deep learning approach.     

Designing Sub‐2 nm Organosilica Nanohybrids for Far‐Field Super‐Resolution Imaging

Stimulated emission depletion (STED) microscopy enables ultrastructural imaging of biological samples with high spatiotemporal resolution. STED nanoprobes based on fluorescent organosilica nanohybrids featuring sub‐2 nm size and near‐unity quantum yield are presented. The spin–orbit coupling (SOC) of heavy‐atom‐rich organic fluorophores is mitigated through a silane‐molecule‐mediated condensation/dehalogenation process, resulting in bright fluorescent organosilica nanohybrids with multiple emitters in one hybrid nanodot. When harnessed as STED nanoprobes, these fluorescent nanohybrids show intense photoluminescence, high biocompatibility, and long‐term photostability. Taking advantage of the low‐power excitation (0.5 μW), prolonged singlet‐state lifetime, and negligible depletion‐induced re‐excitation, these STED nanohybrids present high depletion efficiency (>96 %), extremely low saturation intensity (0.54 mW, ca. 0.188 MW cm^?2), and ultra‐high lateral resolution (ca. λ_em/28).     

Rhodium‐Catalyzed Remote C(sp3)−H Borylation of Silyl Enol Ethers

A rhodium‐catalyzed remote C(sp³)?H borylation of silyl enol ethers (SEEs, E/Z mixtures) by alkene isomerization and hydroboration is reported. The reaction exhibits mild reaction conditions and excellent functional‐group tolerance. This method is compatible with an array of SEEs, including linear and branched SEEs derived from aldehydes and ketones, and provides direct access to a broad range of structurally diverse 1,n‐borylethers in excellent regioselectivities and good yields. These compounds are precursors to various valuable chemicals, such as 1,n‐diols and aminoalcohols.     

Rare‐Earth Single Erbium Atoms for Enhanced Photocatalytic CO2 Reduction

The solar‐driven photocatalytic reduction of CO₂ (CO₂RR) into chemical fuels is a promising route to enrich energy supplies and mitigate CO₂ emissions. However, low catalytic efficiency and poor selectivity, especially in a pure‐water system, hinder the development of photocatalytic CO₂RR owing to the lack of effective catalysts. Herein, we report a novel atom‐confinement and coordination (ACC) strategy to achieve the synthesis of rare‐earth single erbium (Er) atoms supported on carbon nitride nanotubes (Er₁/CN‐NT) with a tunable dispersion density of single atoms. Er₁/CN‐NT is a highly efficient and robust photocatalyst that exhibits outstanding CO₂RR performance in a pure‐water system. Experimental results and density functional theory calculations reveal the crucial role of single Er atoms in promoting photocatalytic CO₂RR.     

Maximization of Spatial Charge Density: An Approach to Ultrahigh Energy Density of Capacitive Charge Storage

Capacitive energy storage has advantages of high power density, long lifespan, and good safety, but is restricted by low energy density. Inspired by the charge storage mechanism of batteries, a spatial charge density (SCD) maximization strategy is developed to compensate this shortage by densely and neatly packing ionic charges in capacitive materials. A record high SCD (ca. 550 C cm^?3) was achieved by balancing the valance and size of charge‐carrier ions and matching the ion sizes with the pore structure of electrode materials, nearly five times higher than those of conventional ones (ca. 120 C cm^?3). The maximization of SCD was confirmed by Monte Carlo calculations, molecular dynamics simulations, and in situ electrochemical Raman spectroscopy. A full‐cell supercapacitor was further constructed; it delivers an ultrahigh energy density of 165 Wh L^?1 at a power density of 150 WL^?1 and retains 120 Wh L^?1 even at 36 kW L^?1, opening a pathway towards high‐energy‐density capacitive energy storage.     

Superwetting Patterned Membranes with an Anisotropy/Isotropy Transition: Towards Signal Expression and Liquid Permeation

Superwetting membranes with responsive properties have attracted heightened attention because of their fine‐tunable surface wettability. However, their functional diversity is severely limited by the “black‐or‐white” wettability transition. Herein, we describe a coating strategy to fabricate multifunctional responsive superwetting membranes with SiO₂/octadecylamine patterns. The adjustable patterns in the responsive region are the key factor for functional diversity. Specifically, the coated part of the membrane displayed a superhydrophobicity/superhydrophilicity transition at different pH values, whereas the uncoated part exhibited invariant superhydrophilicity. On the basis of this anisotropy/isotropy transition, the membranes can serve as either responsive permeable membranes or signal‐expression membranes, thus enabling the responsive separation and permeation of liquids with satisfactory separation efficiency (>99.90 %) and flux (ca. 60 L m^?2 h), as well as real‐time liquid signal expression with alterable signals.     

Combining convolutional neural networks and in‐line near‐infrared spectroscopy for real‐time monitoring of the chromatographic elution process in commercial production of notoginseng total saponins

The chromatographic elution process is a key step in the production of notoginseng total saponins. Due to quality variability of loading samples and resin capacity decreasing over cycle time, saponins, especially the five main saponins of notoginseng total saponins, need to be monitored in real time during the elution process. In this study, convolutional neural networks, one of the most popular deep learning methods, were used to develop quantitative calibration models based on in‐line near‐infrared spectroscopy for notoginsenoside R₁, ginsenosides Rg₁, Re, Rb₁ and Rd, and their sum concentration, with root mean square error of prediction values of 0.87, 2.76, 0.60, 1.57, 0.28, and 4.99 mg/mL, respectively. Partial least squares calibration models were also developed for model performance comparison. Results show predicted concentration profiles outputted by both the convolutional neural network models and partial least squares models show agreements with the real trends defined by reference measurements, and can be used for elution process monitoring and endpoint determination. To the best of our knowledge, this is the first reported case study of combining convolutional neural networks and in‐line near‐infrared spectroscopy for monitoring of the chromatographic elution process in commercial production of botanical drug products.     

CuO/Cu2O nanowire array photoelectrochemical biosensor for ultrasensitive detection of tyrosinase

Photoelectrochemical(PEC) biosensors have shown great promise in bioanalysis and diagnostic applications in recent years. In this work, the CuO/Cu_2O nanowire array(CuO/Cu_2O Nanowire) supported on copper foam was prepared as a photocathode for detection of tyrosinase though quinone-chitosan conjugation chemistry method. The in-situ generated quinones that were the catalytic product of tyrosinase acted as electron acceptors, which were captured by the chitosan deposited on the surface of the electrode. Direct immobilization of electron acceptor on the electrode surface improved the photocurrent conversion efficiency and thus sensitivity. The as-prepared biosensor can realize a rapid response in a wide linear range of 0.05 U/mL to 10 U/mL with the detection limit as low as 0.016 U/mL of tyrosinase. The current work provides a new perspective to design and develop highly sensitive and selective PEC biosensor.     

Characterization,Classification, and Authentication of Polygonatum sibiricum Samples by Volatile Profiles and Flavor Properties

The importance of monitoring key aroma compounds as food characteristics to solve sample classification and authentication is increasing. The rhizome of Polygonatum sibiricum (PR, Huangjing in Chinese) has great potential to serve as an ingredient of functional foods owing to its tonic effect and flavor properties. In this study, we aimed to characterize and classify PR samples obtained from different processing levels through their volatile profiles and flavor properties by using electronic nose, electronic tongue, and headspace gas chromatography-mass spectrometry. Nine flavor indicators (four odor indicators and five taste indicators) had a strong influence on the classification ability, and a total of 54 volatile compounds were identified in all samples. The traditional Chinese processing method significantly decreased the contents of aldehydes and alkanes, while more ketones, nitrogen heterocycles, alcohols, terpenoids, sulfides, and furans/pyrans were generated in the processing cycle. The results confirmed the potential applicability of volatile profiles and flavor properties for classification of PR samples, and this study provided new insights for determining the processing level in food and pharmaceutical industries based on samples with specific flavor characteristics.