Nanocavity in hollow sandwiched catalysts as substrate regulator for boosting hydrodeoxygenation of biomass-derived carbonyl compounds.

Hydrodeoxygenation of oxygen-rich molecules toward hydrocarbons is attractive yet challenging in the sustainable biomass upgrading. The typical supported metal catalysts often display unstable catalytic performances owing to the migration and aggregation of metal nanoparticles (NPs) into large sizes under harsh conditions. Here, we develop a crystal growth and post-synthetic etching method to construct hollow chromium terephthalate MIL-101 (named as HoMIL-101) with one layer of sandwiched Ru NPs as robust catalysts. Impressively, HoMIL-101@Ru@MIL-101 exhibits the excellent activity and stability for hydrodeoxygenation of biomass-derived levulinic acid to gamma-valerolactone under 50°C and 1-megapascal H2, and its activity is about six times of solid sandwich counterparts, outperforming the state-of-the-art heterogeneous catalysts. Control experiments and theoretical simulation clearly indicate that the enrichment of levulinic acid and H2 by nanocavity as substrate regulator enables self-regulating the backwash of both substrates toward Ru NPs sandwiched in MIL-101 shells for promoting reaction with respect to solid counterparts, thus leading to the substantially enhanced performance.

Self-assembled PtNi layered metallene nanobowls for pH-universal electrocatalytic hydrogen evolution.

Compared with layered materials such as graphite and transition metal disulfide compounds with highly anisotropic in-plane covalent bonds, it is inherently more challenging to obtain independent metallic two-dimensional films with atomic thickness. In this study, PtNi layered metallene nanobowls (LMBs) with multilayer atomic-scale nanosheets and bowl-like structures have been synthesized in one step using structural and electronic effects. The material has the advantage of catalyzing pH-universal hydrogen evolution reaction (HER). Compared with Pt/C, PtNi LMBs exhibited excellent HER activity and stability under all pH conditions. The overpotentials of 10 mA cm-2 at 0.5 M H2SO4, 1.0 M phosphate buffer and 1.0 M KOH were 14.8, 20.3, and 34.0 mV, respectively. Under acidic, neutral and alkaline conditions, the HER Faraday efficiencies reach 98.97%, 98.85%, and 99.04%, respectively. This study provides an example for the preparation of unique multilayer nanobowls, and also provides a basic research platform for the development of special HER materials.

Predicting lncRNA-protein interactions through deep learning framework employing multiple features and random forest algorithm.

RNA-protein interaction (RPI) is crucial to the life processes of diverse organisms. Various researchers have identified RPI through long-term and high-cost biological experiments. Although numerous machine learning and deep learning-based methods for predicting RPI currently exist, their robustness and generalizability have significant room for improvement. This study proposes LPI-MFF, an RPI prediction model based on multi-source information fusion, to address these issues. The LPI-MFF employed protein-protein interactions features, sequence features, secondary structure features, and physical and chemical properties as the information sources with the corresponding coding scheme, followed by the random forest algorithm for feature screening. Finally, all information was combined and a classification method based on convolutional neural networks is used. The experimental results of fivefold cross-validation demonstrated that the accuracy of LPI-MFF on RPI1807 and NPInter was 97.60% and 97.67%, respectively. In addition, the accuracy rate on the independent test set RPI1168 was 84.9%, and the accuracy rate on the Mus musculus dataset was 90.91%. Accordingly, LPI-MFF demonstrated greater robustness and generalization than other prevalent RPI prediction methods.

Distinctive p-d Orbital Hybridization in CuSb Porous Nanonetworks for Enhanced Nitrite Electroreduction to Ammonia.

Electrochemical nitrite reduction reaction ( NO 2 - RR ${\mathrm{NO}}_{\mathrm{2}}^{\mathrm{ - }}{\mathrm{RR}}$ ), as a green and sustainable ammonia synthesis technology, has broad application prospects and environmental friendliness. Herein, an unconventional p-d orbital hybridization strategy is reported to realize the fabrication of defect-rich CuSb porous nanonetwork (CuSb PNs) electrocatalyst for NO 2 - RR ${\mathrm{NO}}_{\mathrm{2}}^ - {\mathrm{RR}}$ . The crystalline/amorphous heterophase structure is cleverly introduced into the porous nanonetworks, and this defect-rich structure exposes more atoms and activated boundaries. CuSb PNs exhibit a large NH3 yield ( r N H 3 ${{r}_{{\mathrm{N}}{{{\mathrm{H}}}_{\mathrm{3}}}}}$ ) of 946.1 µg h-1 m cat - 1 ${\mathrm{m}}_{{\mathrm{cat}}}^{ - {\mathrm{1}}}$ and a high faradaic efficiency (FE) of 90.7%. Experimental and theoretical studies indicate that the excellent performance of CuSb PNs results from the defect-rich porous nanonetworks structure and the p-d hybridization of Cu and Sb elements. This work describes a powerful pathway for the fabrication of p-d orbital hybrid defect-rich porous nanonetworks catalysts, and provides hope for solving the problem of nitrogen oxide pollution in the field of environment and energy.

CRBSP:Prediction of CircRNA-RBP Binding Sites Based on Multimodal Intermediate Fusion.

Circular RNA (CircRNA) is widely expressed and has physiological and pathological significance, regulating post-transcriptional processes via its protein-binding activity. However, whereas much work has been done on linear RNA and RNA binding protein (RBP), little is known about the binding sites of CircRNA. The current report is on the development of a medium-term multimodal data fusion strategy, CRBSP, to predict CircRNA-RBP binding sites. CRBSP represents the CircRNA trinucleotide semantic, location, composition and frequency information as the corresponding coding methods of Word to vector (Word2vec), Position-specific trinucleotide propensity (PSTNP), Pseudo trinucleotide composition (PseTNC) and Trinucleotide nucleotide composition (TNC), respectively. CNN (Convolution Neural Networks) was used to extract global information and BiLSTM (bidirectional Long- and Short-Term Memory network) encoder and LSTM (Long- and Short-Term Memory network) decoder for local sequence information. Enhancement of the contributions of key features by the self-attention mechanism was followed by mid-term fusion of the four enhanced features. Logistic Regression (LR) classifier showed that CRBSP gives a mean AUC value of 0.9362 through 5-fold Cross Validation of all 37 datasets, a performance which is superior to five current state-of-the-art models. Similar evaluation of linear RNA-RBP binding sites gave an AUC value of 0.7615 which is also higher than other prediction methods, demonstrating the robustness of CRBSP.

Metal nanoparticles capped with plant polyphenol for oxygen reduction electrocatalysis.

The development of a convenient and universal strategy for the synthesis of inorganic-organic hybrid nanomaterials with phenolic coating on the surface is of special significance for the preparation of electrocatalysts. In this work, we report an environmentally friendly, practical, and convenient method for one-step reduction and generation of organically capped nanocatalysts using natural polyphenol tannic acid (TA) as reducing agents and coating agents. TA coated metal (Pd, Ag and Au) nanoparticles are prepared by this strategy, among which TA coated Pd nanoparticles (PdTA NPs) show excellent oxygen reduction reaction activity and stability under alkaline conditions. Interestingly, the TA in the outer layer makes PdTA NPs methanol resistant, and TA acts as molecular armor against CO poisoning. We propose an efficient interfacial coordination coating strategy, which opens up new way to regulate the interface engineering of electrocatalysts reasonably and has broad application prospects.

Ni-doped MoS2 embedded in natural wood containing porous cellulose for piezo-catalytic degradation of tetracycline.

Wood is a natural material with low cost and easy recovery, which porous, layered, excellent structure and mechanical properties make it possible to apply in wastewater treatment. We have successfully grown MoS2 on natural wood containing porous cellulose and introduced the high conductivity circuit path provided by Ni nanoparticles to construct a new piezoelectric three-dimensional wood block for the efficient degradation of tetracycline. Ni/MoS2/Wood exhibited excellent piezo-catalytic degradation performance, and the degradation rate of tetracycline reached 95.96 % (k = 0.0411 min-1) under ultrasonic vibration. After 5 cycles, the degradation rate still reached 90.20 %. In addition, Ni/MoS2/Wood was used as the reactor filler to degrade tetracycline through piezoelectric response triggered by hydrodynamic force, and the degradation rate reached 90.27 % after 60 min. Further, the mechanism and the possible degradation pathways of tetracycline degradation were proposed. This low-cost, recyclable and stable three-dimensional wood block piezoelectric material provides a new idea for the practical application of wastewater treatment.

CapsNet-LDA: predicting lncRNA-disease associations using attention mechanism and capsule network based on multi-view data.

Cumulative studies have shown that many long non-coding RNAs (lncRNAs) are crucial in a number of diseases. Predicting potential lncRNA-disease associations (LDAs) can facilitate disease prevention, diagnosis and treatment. Therefore, it is vital to develop practical computational methods for LDA prediction. In this study, we propose a novel predictor named capsule network (CapsNet)-LDA for LDA prediction. CapsNet-LDA first uses a stacked autoencoder for acquiring the informative low-dimensional representations of the lncRNA-disease pairs under multiple views, then the attention mechanism is leveraged to implement an adaptive allocation of importance weights to them, and they are subsequently processed using a CapsNet-based architecture for predicting LDAs. Different from the conventional convolutional neural networks (CNNs) that have some restrictions with the usage of scalar neurons and pooling operations. the CapsNets use vector neurons instead of scalar neurons that have better robustness for the complex combination of features and they use dynamic routing processes for updating parameters. CapsNet-LDA is superior to other five state-of-the-art models on four benchmark datasets, four perturbed datasets and an independent test set in the comparison experiments, demonstrating that CapsNet-LDA has excellent performance and robustness against perturbation, as well as good generalization ability. The ablation studies verify the effectiveness of some modules of CapsNet-LDA. Moreover, the ability of multi-view data to improve performance is proven. Case studies further indicate that CapsNet-LDA can accurately predict novel LDAs for specific diseases.

Liquid-liquid equilibrium experiment and mechanism analysis of menthol-based deep eutectic solvents extraction for separation of fuel additive tert-butanol.

Deep eutectic solvents (DESs) were synthesized using menthol as hydrogen bond acceptor (HBA) and different carbon chain carboxylic acids as hydrogen bond donors (HBD). The liquid equilibrium (LLE) experiment was used to determine the distribution coefficient (ß) and slectivity (S) at standard atmospheric pressure and temperature. The effect of DESs on the separation efficiency was discussed by changing the proportion. Non-random two fluid (NRTL) model was used to correlate the experimental data. The molecular dynamics (MD) simulation method was used to investigate the micro mechanism of the extraction process. The results show van der Waals force plays a leading role in the interaction between solvents and tert-butyl alcohol (TBA) and week force with water. Compared with experimental and simulation results, the interaction between DESs and TBA would also be affected by the change of the number of HBD carbon chains, and DESs with decanoic acid as HBD has the best separation effect, which verifies the feasibility of separating high alcohol compounds from water by DESs and then treating them by DESs.

Comparative analysis of life cycle water accounting of the Lurgi low-pressure methanol production process with biomass or coal as raw materials.

Water and energy are both essential for methanol production. This study focuses on the two processes of coal to methanol and biomass to methanol, and analyzes the water footprint of the methanol production process in the life cycle. The results indicate that the water footprint of biomass to methanol is 1707.54 L/MJ, and the dominant factor was the water consumption in the growth stage of biomass, accounting for over 95 % of the total water consumption. The water footprint of the coal to methanol process is 161.40 L/MJ. The main contributor to this process was the methanol stage, which accounted for 99.75 % of the total water footprint. However, the water consumption of the biomass to the methanol stage accounted for only 51.6 % of that of the coal to methanol stage. Based on the power situation of 30 provinces, the indirect water consumption caused by power generation in different regions was calculated, resulting in greater changes in the total water footprint of the biomass to methanol process. Through a sensitivity analysis, the effects of 24 influencing factors and main inputs on the total water consumption were investigated. This study provides the relevant water consumption of the two methanol production processes within the standard range, and the results emphasize the importance of biomass utilization and water conservation.

A large-scale multi-label 12-lead electrocardiogram database with standardized diagnostic statements.

Deep learning approaches have exhibited a great ability on automatic interpretation of the electrocardiogram (ECG). However, large-scale public 12-lead ECG data are still limited, and the diagnostic labels are not uniform, which increases the semantic gap between clinical practice. In this study, we present a large-scale multi-label 12-lead ECG database with standardized diagnostic statements. The dataset contains 25770 ECG records from 24666 patients, which were acquired from Shandong Provincial Hospital (SPH) between 2019/08 and 2020/08. The record length is between 10 and 60 seconds. The diagnostic statements of all ECG records are in full compliance with the AHA/ACC/HRS recommendations, which aims for the standardization and interpretation of the electrocardiogram, and consist of 44 primary statements and 15 modifiers as per the standard. 46.04% records in the dataset contain ECG abnormalities, and 14.45% records have multiple diagnostic statements. The dataset also contains additional patient demographics.

MAGCNSE: predicting lncRNA-disease associations using multi-view attention graph convolutional network and stacking ensemble model.

BACKGROUND: Many long non-coding RNAs (lncRNAs) have key roles in different human biologic processes and are closely linked to numerous human diseases, according to cumulative evidence. Predicting potential lncRNA-disease associations can help to detect disease biomarkers and perform disease analysis and prevention. Establishing effective computational methods for lncRNA-disease association prediction is critical. RESULTS: In this paper, we propose a novel model named MAGCNSE to predict underlying lncRNA-disease associations. We first obtain multiple feature matrices from the multi-view similarity graphs of lncRNAs and diseases utilizing graph convolutional network. Then, the weights are adaptively assigned to different feature matrices of lncRNAs and diseases using the attention mechanism. Next, the final representations of lncRNAs and diseases is acquired by further extracting features from the multi-channel feature matrices of lncRNAs and diseases using convolutional neural network. Finally, we employ a stacking ensemble classifier, consisting of multiple traditional machine learning classifiers, to make the final prediction. The results of ablation studies in both representation learning methods and classification methods demonstrate the validity of each module. Furthermore, we compare the overall performance of MAGCNSE with that of six other state-of-the-art models, the results show that it outperforms the other methods. Moreover, we verify the effectiveness of using multi-view data of lncRNAs and diseases. Case studies further reveal the outstanding ability of MAGCNSE in the identification of potential lncRNA-disease associations. CONCLUSIONS: The experimental results indicate that MAGCNSE is a useful approach for predicting potential lncRNA-disease associations.

Interpatient ECG Arrhythmia Detection by Residual Attention CNN.

The precise identification of arrhythmia is critical in electrocardiogram (ECG) research. Many automatic classification methods have been suggested so far. However, efficient and accurate classification is still a challenge due to the limited feature extraction and model generalization ability. We integrate attention mechanism and residual skip connection into the U-Net (RA-UNET); besides, a skip connection between the RA-UNET and a residual block is executed as a residual attention convolutional neural network (RA-CNN) for accurate classification. The model was evaluated using the MIT-BIH arrhythmia database and achieved an accuracy of 98.5% and F 1 scores for the classes S and V of 82.8% and 91.7%, respectively, which is far superior to other approaches.

First-principles study of polymer-passivated silicon nanowire outer-shell defects.

Highly effective defect passivation schemes are very important for the improvement of Si nanowire (SiNW) performances, because large numbers of outer-shell-defect states are caused by the high surface-to-volume ratios of nanowires. In this work, a polymer that can be fabricated by a simple, vacuum-free method at low temperatures, Nafion, was studied for the SiNW outer-shell defect passivation using first-principles calculations. Based on adsorption energy calculations, it was found that the Nafion molecule could firmly adsorb on the surfaces of SiNWs along the 〈112〉 direction. The Nafion-passivated SiNW outer-shell exhibited high stability to a chemical environment. Herein, the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) were confined to the center of the SiNW due to being wrapped by the Nafion. The Nafion-passivated SiNWs exhibited an equivalent quantum confinement effect and a larger absorption coefficient compared with the H-passivated SiNWs. This work demonstrated a passivation strategy of SiNW shell defects using functional groups.

Comparative water footprint assessment of fuel cell electric vehicles and compressed natural gas vehicles.

Utilization of renewable energy has become a current energy development trend. In this study, the water footprints of a fuel cell electric vehicle (FCEV) and a compressed natural gas vehicle (CNG) under different fuel scenarios were evaluated. The FCEV exhibits a low water footprint of 27.2 L/100 km under steam methane reforming hydrogen production technology. Hydrogen production using steam methane reforming and water electrolysis via wind can enable the FCEV industry to save more water resources. The percentage difference between different metallic materials in automobiles was analyzed. The water consumption by steel accounted for 73.6% and 80.5%, respectively. The fluctuation law of the water footprint was analyzed based on different power structures and steel water consumption coefficients. It was found that for low steel water consumption coefficient, wind power generation is conducive to slowing down the water consumption during the entire life cycle. In addition, a sensitivity analysis was performed for the FCEV and CNG under different fuel scenarios. Fuel technology and material structure have a significant impact on the total water footprint. The results of this study can provide guidance for the layout of the automobile industry and for water-saving measures in the future.

Shipborne expendable all-optical fiber ocean temperature-depth profile sensor.

Traditional electrical expendable bathythermograph (XBT) is designed to fall at a known rate based on a great deal of experiments so that the depth of the temperature profile can be inferred from the time it enters the water. Unlike the traditional electrical XBT, which derives the depth from fall-rate equations, we propose an all-optical fiber (AOF) XBT (AOF-XBT) based on cascade of two fiber Bragg gratings (FBGs). In the AOF-XBT, the depth data comes from one FBG, which responds in real time to the pressure acting on the diaphragm, and temperature data can be measured via the other FBG simultaneously. First, the pressure and temperature response characteristics of the AOF-XBT are analyzed based on a finite element method. Then, the temperature and pressure calibrations for the AOF-XBT is completed after they are packaged. Results show that the mean-temperature sensitivity of two sensors are 14.765 and 13.705 pm/°C in the range of 5°C-30°C, and the mean-pressure sensitivities are -2.75586 and -3.00472nm/MPa in the range of 0-0.6 MPa, respectively. At last, by comparing the results obtained from the AOF-XBT and the SBE 911plus CTD that tested in the sea area of Weihai, the trends of the temperature-depth profile from the two devices are consistent, which presents a new all-optical technique to provide full ocean temperature-depth profile observations.

Analysis and Model Comparison of Carbon and Nitrogen Concentrations in Sediments of the Yellow Sea and Bohai Sea by Visible-Near Infrared Spectroscopy.

Visible-near infrared spectroscopy is considered an effective method for rapidly determining total carbon (TC) and total nitrogen (TN) in terrestrial soils. However, reports on measuring them by VNIR in marine sediments are limited. This article provides an analysis and spectral model comparison of TC and TN in marine sediments using VNIR. The best TC and TN spectral models were established when using the least square support vector machine algorithm with a wavelength, which extended from 226 nm to 975 nm. The prediction results of TN have a high coefficient of determination and residual predictive deviation, providing accurate quantitative predictions. The TC spectral model comes with a disadvantage might due to its usual high concentrations of organic carbon. Characteristic wavelength extraction may lead to the loss of identification information for the characteristics of TC and TN, and full wavelength spectrum contains more information helps more to the quantification.

Separation of the Azeotropic Mixture Methanol and Toluene Using Extractive Distillation: Entrainer Determination, Vapor-Liquid Equilibrium Measurement, and Modeling.

For separating the azeotropic mixture methanol and toluene, an extractive distillation is applied with butyl propanoate, triethylamine, and butyl butanoate as the extractive solvents, which were screened by relative volatility, selectivity, and the x-y curve. The vapor-liquid equilibrium data of the binary and ternary systems for (toluene + butyl propanoate), (toluene + triethylamine), (toluene + butyl butanoate), and (methanol + toluene + butyl butanoate) were determined. The reliability for the experimental vapor-liquid equilibrium (VLE) data was assessed with the van Ness method. The measured data was fitted by the UNIQUAC, Wilson, and NRTL models, and the correlated results were consistent with the determined VLE data. In addition, the COSMO-UNIFAC model was used to predict the VLE data for comparison.

Above 15% Efficient Directly Sputtered CIGS Solar Cells Enabled by a Modified Back-Contact Interface.

The Schottky back-contact barrier at the Mo/Cu(In,Ga)Se2 (CIGS) interface is one of the critical issues that restrict the photovoltaic performance of CIGS solar cells. The formation of a MoSe2 intermediate layer can effectively reduce this back-contact barrier leading to efficient hole transport. However, the selenium-free atmosphere is unfavorable for the formation of the desired MoSe2 intermediate layer if the CIGS films are prepared by the commonly used direct sputtering process. In this work, high-efficiency CIGS solar cells with a MoSe2 intermediate layer were fabricated by the direct sputtering process without a selenium atmosphere. This is enabled by an intermediate CIGS layer deposited on the Mo substrate at room temperature before being ramped to a high temperature (600 °C). The room-temperature-deposited amorphous CIGS intermediate layer is Se rich, which reacts with the Mo substrate and forms very thin MoSe2 at the interface during the high-temperature process. The formed MoSe2 decreased the CIGS/Mo barrier height for better hole transport. Consequently, the CIGS solar cell with an 80 nm intermediate layer achieved a power conversion efficiency of up to 15.8%, which is a benchmark efficiency for the direct sputtering process without Se supply. This work provides the industry a new approach for commercialization of directly sputtered CIGS solar cells.

Conversational Image Search.

Conversational image search, a revolutionary search mode, is able to interactively induce the user response to clarify their intents step by step. Several efforts have been dedicated to the conversation part, namely automatically asking the right question at the right time for user preference elicitation, while few studies focus on the image search part given the well-prepared conversational query. In this paper, we work towards conversational image search, which is much difficult compared to the traditional image search task, due to the following challenges: 1) understanding complex user intents from a multimodal conversational query; 2) utilizing multiform knowledge associated images from a memory network; and 3) enhancing the image representation with distilled knowledge. To address these problems, in this paper, we present a novel contextuaL imAge seaRch sCHeme (LARCH for short), consisting of three components. In the first component, we design a multimodal hierarchical graph-based neural network, which learns the conversational query embedding for better user intent understanding. As to the second one, we devise a multi-form knowledge embedding memory network to unify heterogeneous knowledge structures into a homogeneous base that greatly facilitates relevant knowledge retrieval. In the third component, we learn the knowledge-enhanced image representation via a novel gated neural network, which selects the useful knowledge from retrieved relevant one. Extensive experiments have shown that our LARCH yields significant performance over an extended benchmark dataset. As a side contribution, we have released the data, codes, and parameter settings to facilitate other researchers in the conversational image search community.