Theoretical study of the effects of surface Cu coordination environment on CO2 hydrogenation to CH3OH.

The coordination environment of Cu (the coordination number and arrangement of surface atoms) plays an important role in CO2 hydrogenation to CH3OH. Compared with the extensive studies of the effects of coordination number, the comprehensive effects of coordination number and arrangement of surface atoms were seldom explored in literature. To unravel the effects of surface Cu coordination environment on CO2 hydrogenation to CH3OH, the adsorption and reaction behaviors of H2 and CO2 on Cu(111), (100), (110) and (211) with different coordination numbers and arrangement of surface Cu were systematically calculated by density functional theory (DFT) and kinetic Monte Carlo (kMC) simulation. It was found that the adsorption energies of intermediates in CO2 hydrogenation on Cu surfaces increase with the decrease of coordination number. When the Cu coordination numbers are similar, the charge density on the open surface derived from the different atom arrangement becomes larger and leads to stronger interaction with intermediates than that on the compact one. DFT calculation and kMC simulation indicate that methanol formation pathway follows CO2*→HCOO*→HCOOH*→H2COOH*→H2CO*→CH3O*→CH3OH* on four Cu facets; CO formation is via CO2 direct dissociation on Cu(111), (100) and (110) but COOH* dissociation on (211). The low-coordinated surface Cu with more openness on Cu(211) is the highly active site for CO2 hydrogenation to CH3OH with high turnover of frequency (3.71 × 10-4 s-1) and high selectivity (87.17 %) at 600 K, PCO2 = 7.5 atm and PH2 = 22.5 atm, which is much higher than those on Cu(111), (100) and (110). This work unravels the effects of coordination environment on CO2 hydrogenation at the molecular level and provides an important insight into the design and development of catalysts with high performance in CO2 hydrogenation to CH3OH.

Conformational equilibria in acrolein-CO2: the crucial contribution of n → π* interactions unveiled by rotational spectroscopy.

Using gas phase Fourier-transform microwave spectroscopy complemented by theoretical analysis, this study delivers a comprehensive depiction of the physical origin of the 'n → π* interaction' between CO2 and acrolein, one of the most reactive aldehydes. Three distinct isomers of the acrolein-CO2 complex, linked through a C⋯O tetrel bond (or n → π* interaction) and a C-H⋯O hydrogen bond, have been unambiguously identified in the pulsed jet. Relative intensity measurements allowed estimation on the population ratio of the three isomers to be T1/T2/C1 ≈ 25/5/1. Advanced theoretical analyses were employed to elucidate the intricacies of the noncovalent interactions within the examined complex. This study not only sheds light on the molecular underpinnings of n → π* interactions but also paves the way for future exploration in carbon dioxide capture and utilization, leveraging the fundamental principles uncovered in the study of acrolein-carbon dioxide interactions.

A graminan type fructan from Achyranthes bidentata prevents the kidney injury in diabetic mice by regulating gut microbiota.

Diabetic kidney disease (DKD) is the main cause of end-stage renal disease, and few therapeutic options are available. The root of Achyranthis bidentatae (AB) is commonly used for DKD treatment in Traditional Chinese medicine. However, its mechanisms are still unclear. Here, a graminan type fructan ABPW1 with molecular weight of 3998 Da was purified from AB. It was composed of ß-1,2-linked Fruf, ß-2,6-linked-Fruf and ß-1,2,6-linked-Fruf backbone, and terminated with T-Glcp and 2-Fruf residues. ABPW1 protected against kidney injuries and intestinal barrier disruption in Streptozotocin (STZ)/High fat diet (HFD) mice. It could modulate gut microbiota composition, evidenced by a rise in the abundance of Bacteroide and decreases of Rikenella, Alistipes, Laedolimicola and Faecalibaculum. ABPW1 intervention promoted short chain fatty acids (SCFAs) production in STZ/HFD mice, especially propionate and isobutyric acid. Antibiotic treatment further demonstrated the key role of gut microbiota in the renal protective action of ABPW1. In addition, in vitro simulated digestion and fermentation together with in vivo fluorescent labeling studies demonstrated ABPW1 was indigestible in upper digestive tract but could reach the colon and be degraded into SCFAs by gut microbiota there. Overall, these data suggested ABPW1 has the potential application on DKD prevention.

United under stress: High-speed transport network emerging at bacterial living edge.

Individuals tend to move freely when there is enough room but would act collectively for their survival under external stress. In the case of living cells, for instance, when a drop of low-density flagellated bacterial solution is transferred onto the agar surface, the initially disordered movement of individual bacteria would be replaced with coordinated cell swarming after a lag phase of a few hours. Here, we study how such cooperation is established while overcoming the disorder at the onset of the lag phase with single nanoparticle tracking. Upon the spreading of the droplet, the bacteria in the solution cluster and align near the almost immobilized contact line confining the drop, forming a narrow ring of cells. As individual cells move in and out of the ring continuously, certain flow patterns emerge in the inter-bacterial fluid. We reveal high-speed long-distance unidirectional flows with definite chirality along the outside of the ring, along the inside of the ring and across the ring. We speculate that these flows enable the fast and efficient transport, facilitating the communication and unification of the bacterial community.

Stabilized ε-Fe2C catalyst with Mn tuning to suppress C1 byproduct selectivity for high-temperature olefin synthesis.

Accurately controlling the product selectivity in syngas conversion, especially increasing the olefin selectivity while minimizing C1 byproducts, remains a significant challenge. Epsilon Fe2C is deemed a promising candidate catalyst due to its inherently low CO2 selectivity, but its use is hindered by its poor high-temperature stability. Herein, we report the successful synthesis of highly stable ε-Fe2C through a N-induced strategy utilizing pyrolysis of Prussian blue analogs (PBAs). This catalyst, with precisely controlled Mn promoter, not only achieved an olefin selectivity of up to 70.2% but also minimized the selectivity of C1 byproducts to 19.0%, including 11.9% CO2 and 7.1% CH4. The superior performance of our ε-Fe2C-xMn catalysts, particularly in minimizing CO2 formation, is largely attributed to the interface of dispersed MnO cluster and ε-Fe2C, which crucially limits CO to CO2 conversion. Here, we enhance the carbon efficiency and economic viability of the olefin production process while maintaining high catalytic activity.

Fully exposed Pt clusters for efficient catalysis of multi-step hydrogenation reactions.

For di-nitroaromatics hydrogenation, it is a challenge to achieve the multi-step hydrogenation with high activity and selectivity due to the complexity of the process involving two nitro groups. Consequently, many precious metal catalysts suffer from low activity for this multi-step hydrogenation reaction. Herein, we employ a fully exposed Pt clusters catalyst consisting of an average of four Pt atoms on nanodiamond@graphene (Ptn/ND@G), demonstrating excellent catalytic performance for the multi-step hydrogenation of 2,4-dinitrotoluene. The TOF (40647 h-1) of Ptn/ND@G is significantly superior to that of single Pt atoms catalyst, Pt nanoparticles catalyst, and even all the known catalysts. Density functional theory calculations and absorption experiments reveal that the synergetic interaction between the multiple active sites of Ptn/ND@G facilitate the co-adsorption/activation of reactants and H2, as well as the desorption of intermediates/products, which is the key for the higher catalytic activity than single Pt atoms catalyst and Pt nanoparticles catalyst.

DFT Study on Effect of Metal Type and Coordination Environment on CO2 ECR to C1 Products over M-N-C Catalysts.

Electrocatalytic reduction (ECR) of CO2 to chemical products is an important carbon emission reduction method. This work uses DFT to study the stability of N-doped graphene-supported four metal single-atom catalysts (M-N-C) and the effects of the coordination environment and metal centers on the selectivity of CO2 ECR to C1 products. The results show that Fe, Co, Ni, and Cu have good stability. The coordination environment has a significant modulating effect on product selectivity, and the change of the number of ligand nitrogen atoms will affect the size of the potential-limiting step of each product. When the number of nitrogen ligands is the same, the different metal centers of the M-N-C catalyst have a significant effect on the selectivity of different products. In addition, the introduction of nitrogen atom ligands can adjust the electronic structure of the graphene-supported metal center, increase the d-band center of most metals, and improve the reaction activity.

Machine Learning-Accelerated First-Principles Study of Atomic Configuration and Ionic Diffusion in Li10GeP2S12 Solid Electrolyte.

The solid electrolyte Li10GeP2S12 (LGPS) plays a crucial role in the development of all-solid-state batteries and has been widely studied both experimentally and theoretically. The properties of solid electrolytes, such as thermodynamic stability, conductivity, band gap, and more, are closely related to their ground-state structures. However, the presence of site-disordered co-occupancy of Ge/P and defective fractional occupancy of lithium ions results in an exceptionally large number of possible atomic configurations (structures). Currently, the electrostatic energy criterion is widely used to screen favorable candidates and reduce computational costs in first-principles calculations. In this study, we employ the machine learning- and active-learning-based LAsou method, in combination with first-principles calculations, to efficiently predict the most stable configuration of LGPS as reported in the literature. Then, we investigate the diffusion properties of Li ions within the temperature range of 500-900 K using ab initio molecular dynamics. The results demonstrate that the atomic configurations with different skeletons and Li ion distributions significantly affect the Li ions' diffusion. Moreover, the results also suggest that the LAsou method is valuable for refining experimental crystal structures, accelerating theoretical calculations, and facilitating the design of new solid electrolyte materials in the future.

Ruthenium-Cobalt Solid-Solution Alloy Nanoparticles for Enhanced Photopromoted Thermocatalytic CO2 Hydrogenation to Methane.

Bimetallic alloy nanoparticles have garnered substantial attention for diverse catalytic applications owing to their abundant active sites and tunable electronic structures, whereas the synthesis of ultrafine alloy nanoparticles with atomic-level homogeneity for bulk-state immiscible couples remains a formidable challenge. Herein, we present the synthesis of RuxCo1-x solid-solution alloy nanoparticles (ca. 2 nm) across the entire composition range, for highly efficient, durable, and selective CO2 hydrogenation to CH4 under mild conditions. Notably, Ru0.88Co0.12/TiO2 and Ru0.74Co0.26/TiO2 catalysts, with 12 and 26 atom % of Ru being substituted by Co, exhibit enhanced catalytic activity compared with the monometallic Ru/TiO2 counterparts both in dark and under light irradiation. The comprehensive experimental investigations and density functional theory calculations unveil that the electronic state of Ru is subtly modulated owing to the intimate interaction between Ru and Co in the alloy nanoparticles, and this effect results in the decline in the CO2 conversion energy barrier, thus ultimately culminating in an elevated catalytic performance relative to monometallic Ru and Co catalysts. In the photopromoted thermocatalytic process, the photoinduced charge carriers and localized photothermal effect play a pivotal role in facilitating the chemical reaction process, which accounts for the further boosted CO2 methanation performance.

Portable one-step effervescence tablet-based microextraction combined with smartphone digital image colorimetry: Toward field and rapid detection of trace nickel ion.

In this study, the one-step switchable hydrophilic solvent (SHS)-based effervescence tablet microextraction (ETME) was coupled with smartphone digital image colorimetry (SDIC) for the field detection of nickel ion (Ni2+) for the first time. Both extractant and CO2 were generated in situ when the novel SHS-based effervescence tablet was placed in the sample solution. The complexant 1-(2-pyridinylazo)-2-naphthaleno (PAN) dissolved from the effervescence tablet to form a stable complex with Ni2+, and the extractant was uniformly dispersed in the sample solution under the action of CO2 and fully in contact with Ni-PAN, which enabled efficient extraction of Ni2+. The color changes of the extraction phase were captured by smartphone, then a quantitative relationship between the concentrations of Ni2+ and color intensity of images captured using a smartphone was established by customized applet WASDIC, which realized quantitative analysis of Ni2+ in different samples. Under optimal conditions, the enhancement factor (EF) of the proposed method was 65.1, the limit of detection (LOD) and limit of quantification (LOQ) were 1.69 and 5.64 µg L-1, respectively. The developed method was successfully applied to the detection of trace Ni2+ in the environmental samples and natural medicines. And the applicability of the method for use in field analysis was validated.

Bird's-Eye View of the Activity Distribution on a Catalyst Surface via a Machine Learning-Driven Adequate Sampling Algorithm.

Rational design of catalysts relies on a deep understanding of the active centers. The structure and activity distribution of active centers on a surface are two of the central issues in catalysis and important targets of theoretical and experimental investigations. Herein, we report a machine learning-driven adequate sampling (MLAS) framework for obtaining a statistical understanding of the chemical environment near catalyst sites. Combined strategies were implemented to achieve highly efficient sampling, including the decomposition of degrees of freedom, stratified sampling, Gaussian process regression, and well-designed constraint optimization. The MLAS framework was applied to the rate-determining step in NH3 synthesis, namely the N2 activation process. We calculated the produced population function, PA, which provides a comprehensive and intuitive understanding of active centers. The MLAS framework can be broadly applied to other more complicated catalyst materials and reaction networks.

The effect of Tai Chi/Qigong on depression and anxiety symptoms in adults with Cancer: A systematic review and meta-regression.

OBJECTIVE: We expand on prior systematic reviews of Tai chi/Qigong (TCQ) practice on depression or anxiety symptoms in adults with cancer to estimate the mean effect of TCQ on depression and anxiety in randomized controlled trials. Additionally, we perform moderator analysis to examine whether effects vary based on patient features, TCQ stimuli properties, or characteristics of research design. METHODS: Guided by PRISMA guidelines, we located articles published before August 31, 2023 using a combination of electronic database search and a complementary manual search through reference lists of articles and published reviews. Two separate multilevel meta-analyses with random-effects model were employed to estimate the overall effect of TCQ on depression and anxiety respectively. Further, multilevel meta-regression analysis was utilized to examine moderating effects based on moderators derived from patient features, TCQ stimuli properties, or characteristics associated with research design. Meta-analyses were performed in R4.0.0 and certainty of evidence with GRADEpro software. RESULTS: The TCQ intervention yielded a standardized mean effect size of 0.29 (95% CI, 0.18 to 0.40) for anxiety, indicating homogeneity among the included studies. Conversely, for depression, the standardized mean effect size was 0.35 (95% CI, 0.14 to 0.55), signifying heterogeneity: reductions were larger when the trial primary outcome, predominantly function-related outcomes, changed significantly between the TCQ and control group. CONCLUSIONS: TCQ practice exhibits small-to-moderate efficacy in alleviating depression and anxiety symptoms among cancer patients and survivors. Moreover, patients with depressive symptoms for whom TCQ intervention coupled with improvements in function-related outcomes manifested greater antidepressant effect.

TEMPO-conjugated tobacco mosaic virus as a magnetic resonance imaging contrast agent for detection of superoxide production in the inflamed liver.

Superoxide, an anionic dioxygen molecule, plays a crucial role in redox regulation within the body but is implicated in various pathological conditions when produced excessively. Efforts to develop superoxide detection strategies have led to the exploration of organic-based contrast agents for magnetic resonance imaging (MRI). This study compares the effectiveness of two such agents, nTMV-TEMPO and kTMV-TEMPO, for detecting superoxide in a mouse liver model with lipopolysaccharide (LPS)-induced inflammation. The study demonstrates that kTMV-TEMPO, with a strategically positioned lysine residue for TEMPO attachment, outperforms nTMV-TEMPO as an MRI contrast agent. The enhanced sensitivity of kTMV-TEMPO is attributed to its more exposed TEMPO attachment site, facilitating stronger interactions with water protons and superoxide radicals. EPR kinetics experiments confirm kTMV-TEMPO's faster oxidation and reduction rates, making it a promising sensor for superoxide in inflamed liver tissue. In vivo experiments using healthy and LPS-induced inflamed mice reveal that reduced kTMV-TEMPO remains MRI-inactive in healthy mice but becomes MRI-active in inflamed livers. The contrast enhancement in inflamed livers is substantial, validating the potential of kTMV-TEMPO for detecting superoxide in vivo. This research underscores the importance of optimizing contrast agents for in vivo imaging applications. The enhanced sensitivity and biocompatibility of kTMV-TEMPO make it a promising candidate for further studies in the realm of medical imaging, particularly in the context of monitoring oxidative stress-related diseases.

Investigation of natural deep eutectic solvent for the extraction of crude polysaccharide from Polygonatum kingianum and influence of metal elements on its immunomodulatory effects.

In this study, natural deep eutectic solvent (NADES) was used to extract Polygonatum kingianum crude polysaccharide (PKCP) and response surface methodology (RSM) was designed to optimize the extraction procedure. The immunomodulatory effect of PKCP and the influence of metal elements on its immunomodulatory effect were further discussed. The optimum conditions for PKCP extraction were obtained by RSM optimization: NADES were synthesized with a 1:2 choline chloride-glycerol molar ratio, then extracted at a liquid-solid ratio of 16.6 mL g-1 and water content of 31.2 % for 60 min at 60 °C. This method was used for the extraction of PKCP, and the extraction efficiency was 29.69 %, which was 2.5 times greater than the conventional method of water extraction. In the concentration range of 200-800 µg mL-1, PKCP could activate macrophages, promoting NO secretion and mRNA expression of interleukin-6 (IL-6) and inducible nitric oxide synthase (iNOS) in a dose-dependent way. NO secretion and cytokine expression were not affected when the metal elements were spiked to the equivalent of the metal elements contained in Polygonatum kingianum. When the content of metal elements was higher, the secretion of NO and the gene expression of iNOS were both decreased, which may affect the immunomodulatory effect of Polygonatum kingianum.

Artificial intelligence-assisted system for the assessment of Forrest classification of peptic ulcer bleeding: a multicenter diagnostic study.

BACKGROUND: Inaccurate Forrest classification may significantly affect clinical outcomes, especially in high risk patients. Therefore, this study aimed to develop a real-time deep convolutional neural network (DCNN) system to assess the Forrest classification of peptic ulcer bleeding (PUB). METHODS: A training dataset (3868 endoscopic images) and an internal validation dataset (834 images) were retrospectively collected from the 900th Hospital, Fuzhou, China. In addition, 521 images collected from four other hospitals were used for external validation. Finally, 46 endoscopic videos were prospectively collected to assess the real-time diagnostic performance of the DCNN system, whose diagnostic performance was also prospectively compared with that of three senior and three junior endoscopists. RESULTS: The DCNN system had a satisfactory diagnostic performance in the assessment of Forrest classification, with an accuracy of 91.2% (95%CI 89.5%-92.6%) and a macro-average area under the receiver operating characteristic curve of 0.80 in the validation dataset. Moreover, the DCNN system could judge suspicious regions automatically using Forrest classification in real-time videos, with an accuracy of 92.0% (95%CI 80.8%-97.8%). The DCNN system showed more accurate and stable diagnostic performance than endoscopists in the prospective clinical comparison test. This system helped to slightly improve the diagnostic performance of senior endoscopists and considerably enhance that of junior endoscopists. CONCLUSION: The DCNN system for the assessment of the Forrest classification of PUB showed satisfactory diagnostic performance, which was slightly superior to that of senior endoscopists. It could therefore effectively assist junior endoscopists in making such diagnoses during gastroscopy.

New insight into the catalytic mechanism of ester hydrogenation over the Cu/ZnO catalyst: the contribution of hydrogen spillover.

The dimethyl maleate hydrogenation activity of Cu, ZnO-X and physically mixed Cu+ZnO-X samples was systematically investigated to probe the essential role of ZnO in ester hydrogenation processes. Cu samples exhibited high CC bond hydrogenation ability with dimethyl succinate as the main product. Comparatively, ZnO was inactive in hydrogenation due to its weak ability to dissociate hydrogen while the CO group could be activated and adsorbed on the ZnO surface. Interestingly, physical mixing with ZnO significantly improved the CO hydrogenation activity of Cu samples. The H2-TPD results reveal the origin of "Cu-ZnO synergy": hydrogen atoms formed on the copper surface can spill over to the ZnO surface and react with the adsorbed CO group.

Machine learning predicts atomistic structures of multielement solid surfaces for heterogeneous catalysts in variable environments.

Solid surfaces usually reach thermodynamic equilibrium through particle exchange with their environment under reactive conditions. A prerequisite for understanding their functionalities is detailed knowledge of the surface composition and atomistic geometry under working conditions. Owing to the large number of possible Miller indices and terminations involved in multielement solids, extensive sampling of the compositional and conformational space needed for reliable surface energy estimation is beyond the scope of ab initio calculations. Here, we demonstrate, using the case of iron carbides in environments with varied carbon chemical potentials, that the stable surface composition and geometry of multielement solids under reactive conditions, which involve large compositional and conformational spaces, can be predicted at ab initio accuracy using an approach that combines the bond valence model, Gaussian process regression, and ab initio thermodynamics. Determining the atomistic structure of surfaces under working conditions paves the way toward identifying the true active sites of multielement catalysts in heterogeneous catalysis.

Towards understanding the lower CH4 selectivity of HCP-Co than FCC-Co in Fischer-Tropsch synthesis.

In Fischer-Tropsch synthesis (FTS), the cobalt catalyst has higher C5+ and lower CH4 selectivity in the hcp phase than in the fcc phase. However, a detailed explanation of the intrinsic mechanism is still missing. The underlying reason was explored combining density functional theory, Wulff construction, and a particle-level descriptor based on the slab model of surfaces that are prevalent in the Wulff shape to provide single-particle level understanding. Using a particle-level indicator of the reaction rates, we have shown that it is more difficult to form CH4 on hcp-Co than on fcc-Co, due to the larger effective barrier difference of CH4 formation and C-C coupling on hcp-Co particles, which leads to the lower CH4 selectivity of hcp-Co in FTS. Among the exposed facets of fcc-Co, the (311) surface plays a pivotal role in promoting CH4 formation. The reduction of CH4 selectivity in cobalt-based FTS is achievable through phase engineering of Co from fcc to hcp or by tuning the temperature and size of the particles.

Operando Mobile Catalysis for Reverse Water Gas Shift Reaction.

Metal atoms on the support serve as active sites for many heterogeneous catalysts. However, the active metal sites on the support are conventionally described as static, and the intermediates adsorbed on the support far away from the active metal sites cannot be transformed. Herein, we report the first example of operando mobile catalysis to promote catalytic efficiency by enhancing the collision probability between active sites and reactants or reaction intermediates. Specifically, ligand-coordinated Pt single atoms (isolated MeCpPt- species) are bonded on CeO2 and transformed into mobile MeCpPt(H)CO complexes during the reverse water gas shift reaction for operando mobile catalysis. This strategy enables the conversion of inert carbonate intermediates on the CeO2 support. A turnover frequency (TOF) of 6358 mol CO2 molPt -1 ⋅ h-1 and 99 % CO selectivity at 300 °C is obtained for reverse water gas shift reaction, dramatically higher than those of Pt catalysts reported in the literature. Operando mobile catalysis presents a promising strategy for designing high-efficiency heterogeneous catalysts for various chemical reactions and applications.

A novel deep eutectic solvent modified magnetic covalent organic framework for the selective separation and determination of trace copper ion in medicinal plants and environmental samples.

BACKGROUND: Pretreatment techniques should be introduced before metal ion determination because there is very low content of heavy metals in Chinese medicinal plants and environmental samples. Magnetic dispersive micro solid phase extraction (MDMSPE) has been widely used for the separation and adsorption of heavy metal pollutants in medicinal plants and environmental samples. However, the majority of MDMSPE adsorbents have certain drawbacks, including low selectivity, poor anti-interference ability, and small adsorption capacity. Therefore, modifying currently available adsorption materials has gained attention in research. RESULTS: In this study, a novel adsorbent MCOF-DES based on a magnetic covalent organic framework (MCOF) modified by a new deep eutectic solvent (DES) was synthesized for the first time and used as an adsorbent of MDMSPE. The MDMSPE was combined with inductively coupled plasma optical emission spectrometry (ICP-OES) for selective separation, enrichment, and accurate determination of trace copper ion (Cu2+) in medicinal plants and environmental samples. Various characterization results show the successful preparation of new MCOF-DES. Under the optimal conditions, the enrichment factor (EF) of Cu2+ was 30, the limit of detection (LOD) was 0.16 µg L-1, and the limit of quantitation (LOQ) was 0.54 µg L-1. The results for the determination of Cu2+ were highly consistent with those of inductively coupled plasma mass spectrometry (ICP-MS), which verified the accuracy and reliability of the method. SIGNIFICANCE: The established method based on a new adsorption material MCOF-DES has achieved the selective separation and determination of trace Cu2+ in medicinal and edible homologous medicinal materials (Phyllanthus emblica Linn.) and environmental samples (soil and water), which provides a promising, selective, and sensitive approach for the determination of trace Cu2+ in other real samples.