Efficient Screening of Pharmaceutical Cocrystals by Microspacing In-Air Sublimation.

Cocrystal screening and single-crystal growth remain the primary obstacles in the development of pharmaceutical cocrystals. Here, we present a new approach for cocrystal screening, microspacing in-air sublimation (MAS), to obtain new cocrystals and grow high-quality single crystals of cocrystals within tens of minutes. The method possesses the advantages of strong designable ability of devices, user-friendly control, and compatibility with materials, especially for the thermolabile molecules. A novel drug-drug cocrystal of favipiravir (FPV) with salicylamide (SAA) was first discovered by this method, which shows improved physiochemical properties. Furthermore, this method proved effective in cultivating single crystals of FPV-isonicotinamide (FPV-INIA), FPV-urea, FPV-nicotinamide (FPV-NIA), and FPV-tromethamine (FPV-Tro) cocrystals, and the structures of these cocrystals were determined for the first time. By adjusting the growth temperature and growth distance precisely, we also achieved single crystals of 10 different paracetamol (PCA) cocrystals and piracetam (PIR) cocrystals, which underscores the versatility and efficiency of this method in pharmaceutical cocrystal screening.

Influence factors of CO adsorption on C2N-supported dual-atom catalysts unveiled by machine learning and twofold feature engineering.

Dual-atom catalysts (DACs) have emerged as a compelling frontier in the realm of the electrochemical carbon dioxide reduction reaction (CO2RR). However, elucidating the intrinsic properties of dual-atom pairs and their direct correlation with catalytic activity poses significant challenges. Herein, we investigate CO adsorption on 248 kinds of C2N-supported DACs and analyze the underlying structure-activity relationships of dual transition metal (TM) atoms based on density functional theory (DFT) calculations and machine learning (ML) models. Compared to the direct input of atomic features in the decision tree model of ML, we confirm that extra feature engineering with the introduction of the arithmetic combination of atomic features can better reflect the correlation of dual TM atoms on C2N-based DACs. Further feature importance analysis reveals a strong relationship between the last one occupied orbital radius (rv), group number (G) for dual TM atoms and the CO binding strength, as well as a potential connection with the d band centre (εd). Our work provides deeper insights into the design of DACs and highlights the significance of twofold feature engineering for the synergistic effects between dual TM atoms.

Ligand-Assisted Coupling Manipulation for Efficient and Stable FAPbI3 Colloidal Quantum Dot Solar Cells.

For emerging perovskite quantum dots (QDs), understanding the surface features and their impact on the materials and devices is becoming increasingly urgent. In this family, hybrid FAPbI3 QDs (FA: formamidium) exhibit higher ambient stability, near-infrared absorption and sufficient carrier lifetime. However, hybrid QDs suffer from difficulty in modulating surface ligand, which is essential for constructing conductive QD arrays for photovoltaics. Herein, assisted by an ionic liquid formamidine thiocyanate, we report a facile surface reconfiguration methodology to modulate surface and manipulate electronic coupling of FAPbI3 QDs, which is exploited to enhance charge transport for fabricating high-quality QD arrays and photovoltaic devices. Finally, a record-high efficiency approaching 15 % is achieved for FAPbI3 QD solar cells, and they retain over 80 % of the initial efficiency after aging in ambient environment (20-30 % humidity, 25 °C) for over 600 h.

First-principles materials simulation and design for alkali and alkaline metal ion batteries accelerated by machine learning.

The challenge of regeneration of batteries requires a performance improvement in the alkali/alkaline metal ion battery (AMIB) materials, whereas the traditional research paradigm fully based on experiments and theoretical simulations needs massive research and development investment. During the last decade, machine learning (ML) has made breakthroughs in many complex disciplines, which testifies to their high processing speed and ability to capture relationships. Inspired by these achievements, ML has also been introduced to bring a new paradigm for shortening the development of AMIB materials. In this Perspective, the focus will be on how this new ML technology solves the key problems of redox potentials, ionic conductivity and stability parameters in first-principles materials' simulation and design for AMIBs. It is found that ML not only accelerates the property prediction, but also gives physicochemical insights into AMIB materials' design. In addition, the final part of this paper summarizes current achievements and looks forward to the progress of a novel paradigm in direct/inverse design with the increasing number of databases, skills, and ML technologies for AMIBs.

Carbon-Dot-Based White-Light-Emitting Diodes with Adjustable Correlated Color Temperature Guided by Machine Learning.

Here, we show the fabrication of the carbon dots (CDs) with green and orange emissions from PTCDA (perylene-3,4,9,10-tetracarboxylic dianhydride). Using these CDs as emitters, the orange (or green) CDs LEDs were fabricated, which show electroluminescence (EL) spectra centered at 560 nm (or 498 nm) with an external quantum efficiency (EQE) of 1.98 % (1.76 %) adhering a luminescence of 626 cd m-2 (or 519 cd m-2 ). The machine learning was successfully used to predict PL CCT value. With the model, the white photoluminescence (PL) emission with adjustable correlated color temperature (CCT) from 3093 to 11018 K via combining blue, green, and orange CDs was achieved. Then, we obtained the warm white CDs LEDs with CCT of 3107, 4071 and 4548 K, and cold white CDs LEDs with CCT of 5632 (CIE coordinates of (0.33, 0.33), EQE: 1.18 %, luminescence: 598 cd m-2 ) and 6034 K accurately.

An Efficient Interfacial Synthesis of Two-Dimensional Metal-Organic Framework Nanosheets for Electrochemical Hydrogen Peroxide Production.

Two-dimensional (2D) metal-organic framework nanosheets (MOF NSs) play a vital role in catalysis, but the most preparation is ultrasonication or solvothermal. Herein, a liquid-liquid interfacial synthesis method has been developed for the efficient fabrication of a series of 2D Ni MOF NSs. The active sites could be modulated by readily tuning the ratios of metal precursors and organic linkers (RM/L ). The Ni MOF NSs display highly RM/L dependent activities towards 2e oxygen reduction reaction (ORR) to hydrogen peroxide (H2 O2 ), where the Ni MOF NSs with the RM/L of 6 exhibit the optimal near-zero overpotential, ca. 98 % H2 O2 selectivity and production rate of ca. 80 mmol gcat -1 h-1 in 0.1 M KOH. As evidenced by X-ray absorption fine structure spectroscopy, the coordination environment of active sites changed from saturation to unsaturation, and the partially unsaturated metal atoms are crucial to create optimal sites for enhancing the electrocatalysis.

Metal-Free Photocatalytic Hydrogenation Using Covalent Triazine Polymers.

Photocatalytic hydrogenation of biomass-derived organic molecules transforms solar energy into high-energy-density chemical bonds. Reported herein is the preparation of a thiophene-containing covalent triazine polymer as a photocatalyst, with unique donor-acceptor units, for the metal-free photocatalytic hydrogenation of unsaturated organic molecules. Under visible-light illumination, the polymeric photocatalyst enables the transformation of maleic acid into succinic acid with a production rate of about 2 mmol g-1 h-1 , and furfural into furfuryl alcohol with a production rate of about 0.5 mmol g-1 h-1 . Great catalyst stability and recyclability are also measured. Given the structural diversity of polymeric photocatalysts and their readily tunable optical and electronic properties, metal-free photocatalytic hydrogenation represents a highly promising approach for solar energy conversion.

Identification of novel regulatory GRE-binding elements in the porcine IP3R1 gene promoter and their transcriptional activation under glucocorticoid stimulation.

Inositol 1,4,5-trisphosphate receptor 1 (IP3R1) is a type of ligand-gated calcium channel that is expressed predominantly in mammalian skeletal muscle, where it acts as a key regulator of calcium homeostasis. In meat, calcium disequilibrium is accompanied by the deterioration of meat quality. Here we show that serum cortisol concentration was higher and the IP3R1 gene expression level increased markedly in pigs exposed to high stress. In porcine primary muscle cells, dexamethasone (DEX, a synthetic glucocorticoid) increased the protein levels of porcine IP3R1 and GRα, and cell apoptosis, and the specific GRα inhibitor RU486 attenuated these effects. DEX also increased the expression of IP3R1 at both the gene and protein levels, and this expression was attenuated by RU486, siRNA against GRα, and the transcriptional inhibitor actinomycin D. DEX significantly reduced cell viability and increased the intracellular calcium concentration, and these effects were attenuated by siRNA against GRα. Bioinformatics analyses predicted a potential glucocorticoid response element (GRE) located in the region -326 to -309 upstream of the IP3R1 promoter and highly conserved in pigs and other mammalian species. Promoter analysis showed that this region containing the GRE was critical for transcriptional activity of porcine IP3R1 under DEX stimulation. This was confirmed by deletion and site-mutation methods. EMSA and ChIP assays showed that this potential GRE bound specifically to GRα and this complex activated the transcription of the IP3R1 gene. Taken together, these data suggest that DEX-mediated induction of IP3R1 influences porcine muscle cells through the transcriptional activation of a mechanism involving interactions between GRα and a GRE present in the proximal IP3R1 promoter. This process can lead to an imbalance in intracellular calcium concentration, which may subsequently activate the apoptosis signal and decrease cell activity, and cause deterioration of meat quality.

A comprehensive transformer-based approach for high-accuracy gas adsorption predictions in metal-organic frameworks.

Gas separation is crucial for industrial production and environmental protection, with metal-organic frameworks (MOFs) offering a promising solution due to their tunable structural properties and chemical compositions. Traditional simulation approaches, such as molecular dynamics, are complex and computationally demanding. Although feature engineering-based machine learning methods perform better, they are susceptible to overfitting because of limited labeled data. Furthermore, these methods are typically designed for single tasks, such as predicting gas adsorption capacity under specific conditions, which restricts the utilization of comprehensive datasets including all adsorption capacities. To address these challenges, we propose Uni-MOF, an innovative framework for large-scale, three-dimensional MOF representation learning, designed for multi-purpose gas prediction. Specifically, Uni-MOF serves as a versatile gas adsorption estimator for MOF materials, employing pure three-dimensional representations learned from over 631,000 collected MOF and COF structures. Our experimental results show that Uni-MOF can automatically extract structural representations and predict adsorption capacities under various operating conditions using a single model. For simulated data, Uni-MOF exhibits remarkably high predictive accuracy across all datasets. Additionally, the values predicted by Uni-MOF correspond with the outcomes of adsorption experiments. Furthermore, Uni-MOF demonstrates considerable potential for broad applicability in predicting a wide array of other properties.

Different Dissolution Molecular Pathways of Azilsartan Crystals with Different Forms Revealed by In Situ Atomic Force Microscopy.

Here, using in situ atomic force microscopy (AFM), the dissolution behaviors and dissolution molecular pathways of two azilsartan crystals, the isopropanol solvate (AZ-IPA), and form I (AZ-I), in pure water and 6-30% poly(ethylene glycol) (PEG) aqueous solutions are revealed. The dissolution behaviors of step retreat and etch pit formation are observed on the (100) faces of the two crystals, with a single step corresponding to one molecular monolayer in crystal structures. Etching rates of pits increase with PEG concentration. Furthermore, our results show that AZ-IPA dissolves by the direct detachment of molecules from the step front to solution. Such a mechanism remains even when the PEG concentration changes. However, AZ-I dissolves primarily by the surface diffusion mechanism involving molecular detachment from the step front at first and then diffusion over the terraces before desorption into solution. PEG promotes the dissolution of AZ-I crystals by favoring the molecular detachment from the step front.

On-surface synthesis of 2D COFs via molecular assembly directed photocycloadditions: a first-principles investigation.

Over the past decades, the rational synthesis of two-dimensional covalent organic framework (2D COFs) monolayer via on-surface chemistry has been widely explored. Herein, we propose the [2 + 2] photocycloaddition as a novel strategy for large-scale fabrication of COFs from theoretical perspective. Thanks to the symmetry forbidden of thermal [2 + 2] cycloaddition, the molecular precursors carrying vinyl groups will not chemically interact with each other during thermal annealing, which is essential to achieve molecular assembly. The subsequent photocycloaddition of these precursors may produce large-scale 2D COFs at low temperatures, in which the symmetry of molecular assembly remains unchanged. Our results show that 2D COFs can be produced via [2 + 2] photocycloadditions directed from self-assembled precursors, in which alkylbenzene molecules with vinyl groups on side chains exhibit appropriate intermolecular distances. By performing high-throughput calculations, several promising molecular precursors are proposed to achieve large-scale 2D COFs. This work provides an applicable strategy for the large-scale synthesis of 2D carbon materials.

Capturing the Trajectory of Psychological Status and Analyzing Online Public Reactions During the Coronavirus Disease 2019 Pandemic Through Weibo Posts in China.

When a major, sudden infectious disease occurs, people tend to react emotionally and display reactions such as tension, anxiety, fear, depression, and somatization symptoms. Social media played a substantial awareness role in developing countries during the outbreak of coronavirus disease 2019 (COVID-19). This study aimed to analyze public opinion regarding COVID-19 and to explore the trajectory of psychological status and online public reactions to the COVID-19 pandemic by examining online content from Weibo in China. This study consisted of three steps: first, Weibo posts created during the pandemic were collected and preprocessed on a large scale; second, public sentiment orientation was classified as "optimistic/pessimistic/neutral" orientation via natural language processing and manual determination procedures; and third, qualitative and quantitative analyses were conducted to reveal the trajectory of public psychological status and online public reactions during the COVID-19 pandemic. Public psychological status differed in different periods of the pandemic (from December 2019 to May 2020). The newly confirmed cases had an almost 1-month lagged effect on public psychological status. Among the 15 events with high impact indexes or related to government decisions, there were 10 optimism orientation > pessimism orientation (OP) events (2/3) and 5 pessimism orientation > optimism orientation (PO) events (1/3). Among the top two OP events, the high-frequency words were "race against time" and "support," while in the top two PO events, the high-frequency words were "irrationally purchase" and "pass away." We proposed a hypothesis that people developed negative self-perception when they received PO events, but their cognition was developed by how these external stimuli were processed and evaluated. These results offer implications for public health policymakers on understanding public psychological status from social media. This study demonstrates the benefits of promoting psychological healthcare and hygiene activity in the early period and improving risk perception for the public based on public opinion and the coping abilities of people. Health managers should focus on disseminating socially oriented strategies to improve the policy literacy of Internet users, thereby facilitating the disease prevention work for the COVID-19 pandemic and other major public events.

Theoretical Design of Dithienopicenocarbazole-Based Molecules by Molecular Engineering of Terminal Units Toward Promising Non-fullerene Acceptors.

Dithienopicenocarbazole (DTPC), as the kernel module in A-D-A non-fullerene acceptors (NFA), has been reported for its ultra-narrow bandgap, high power conversion efficiency, and extremely low energy loss. To further improve the photovoltaic performance of DTPC-based acceptors, molecular engineering of end-capped groups could be an effective method according to previous research. In this article, a class of acceptors were designed via bringing terminal units with an enhanced electron-withdrawing ability to the DTPC central core. Their geometrical structures, frontier molecular orbitals, absorption spectrum, and intramolecular charge transfer and energy loss have been systematically investigated on the basis of density functional theory (DFT) and time-dependent density functional theory (TD-DFT) calculations. Surprisingly, NFA 4 highlights the dominance for its increased open circuit voltages while NFA 2, 7, and 8 exhibit great potential for their enhanced charge transfer and lower energy loss, corresponding to a higher short-circuit current density. Our results also manifest that proper modifications of the terminal acceptor with extensions of π-conjugation might bring improved outcomes for overall properties. Such a measure could become a feasible strategy for the synthesis of new acceptors, thereby facilitating the advancement of organic solar cells.

Preparation, characterization, and phosphate removal and recovery of magnetic MnFe2O4 nano-particles as adsorbents.

Phosphate removal is an important method for controlling eutrophication in bodies of water. Adsorption is an effective phosphate removal approach. In this research, the adsorbent, namely, MnFe2O4, was prepared through the improved co-precipitation method and investigated in terms of phosphate removal. MnFe2O4 was characterized by scanning electron microscopy, vibrating sample magnetometry, X-ray diffraction, and Fourier transform infrared spectroscopy. Phosphate adsorption by MnFe2O4, desorption of adsorbed MnFe2O4 with the regeneration of desorbed MnFe2O4, and phosphate recovery were researched. Experimental results showed that adding the appropriate amount of polyethylene glycol to MnFe2O4 precursors during preparation inhibited the agglomeration of MnFe2O4 between particles because of the magnetic property of MnFe2O4 etc. High crystallinity and strong magnetism were achieved by MnFe2O4 at low temperatures. Average particle size was 5.1 nm. The hysteresis loops confirmed the ferrimagnetic behaviour of MnFe2O4 with a high saturation magnetization (i.e. 26.27 emu/g). The adsorption mechanism of phosphate was mainly physical. The prepared MnFe2O4 had a spinel structure. The proposed technique achieved a phosphate removal rate of 96.06%. A considerable amount of phosphate was desorbed from the adsorbed MnFe2O4 in 15 w/v% NaOH solution. The adsorption capacity of the desorbed MnFe2O4 could be restored to 96.73% in 10 w/v% NaNO3 solution through ion exchange. A sustainable phosphate source was recovered via hydroxyapatite crystallization in the desorption solution, which contained an abundant amount of phosphate as seed for suitable recovery condition. This finding suggested that MnFe2O4 could be a promising adsorbent for efficient phosphate removal.

Elucidating a molecular mechanism that the deterioration of porcine meat quality responds to increased cortisol based on transcriptome sequencing.

Stress response is tightly linked to meat quality. The current understanding of the intrinsic mechanism of meat deterioration under stress is limited. Here, male piglets were randomly assigned to cortisol and control groups. Our results showed that when serum cortisol level was significantly increased, the meat color at 1 h postmortem, muscle bundle ratio, apoptosis rate, and gene expression levels of calcium channel and cell apoptosis including SERCA1, IP3R1, BAX, Bcl-2, and Caspase-3, were notably increased. However, the value of drip loss at 24 h postmortem and serum CK were significantly decreased. Additionally, a large number of differentially expressed genes (DEGs) in GC regulation mechanism were screened out using transcriptome sequencing technology. A total of 223 DEGs were found, including 80 up-regulated genes and 143 down-regulated genes. A total of 204 genes were enriched in GO terms, and 140 genes annotated into in KEGG database. Numerous genes were primarily involved in defense, inflammatory and wound responses. This study not only identifies important genes and signalling pathways that may affect the meat quality but also offers a reference for breeding and feeding management to provide consumers with better quality pork products.