Hydrogen Spillover-Bridged Volmer/Tafel Processes Enabling Ampere-Level Current Density Alkaline Hydrogen Evolution Reaction under Low Overpotential.

Water-alkaline electrolysis holds a great promise for industry-scale hydrogen production but is hindered by the lack of enabling hydrogen evolution reaction electrocatalysts to operate at ampere-level current densities under low overpotentials. Here, we report the use of hydrogen spillover-bridged water dissociation/hydrogen formation processes occurring at the synergistically hybridized Ni3S2/Cr2S3 sites to incapacitate the inhibition effect of high-current-density-induced high hydrogen coverage at the water dissociation site and concurrently promote Volmer/Tafel processes. The mechanistic insights critically important to enable ampere-level current density operation are depicted from the experimental and theoretical studies. The Volmer process is drastically boosted by the strong H2O adsorption at Cr5c sites of Cr2S3, the efficient H2O* dissociation via a heterolytic cleavage process (Cr5c-H2O* + S3c(#) → Cr5c-OH* + S3c-H#) on the Cr5c/S3c sites in Cr2S3, and the rapid desorption of OH* from Cr5c sites of Cr2S3 via a new water-assisted desorption mechanism (Cr5c-OH* + H2O(aq) → Cr5c-H2O* + OH-(aq)), while the efficient Tafel process is achieved through hydrogen spillover to rapidly transfer H# from the synergistically located H-rich site (Cr2S3) to the H-deficient site (Ni3S2) with excellent hydrogen formation activity. As a result, the hybridized Ni3S2/Cr2S3 electrocatalyst can readily achieve a current density of 3.5 A cm-2 under an overpotential of 251 ± 3 mV in 1.0 M KOH electrolyte. The concept exemplified in this work provides a useful means to address the shortfalls of ampere-level current-density-tolerant Hydrogen evolution reaction (HER) electrocatalysts.

Understanding the Dynamic Potential Distribution at the Electrode Interface by Stochastic Collision Electrochemistry.

The potential distribution at the electrode interface is a core factor in electrochemistry, and it is usually treated by the classic Gouy-Chapman-Stern (G-C-S) model. Yet the G-C-S model is not applicable to nanosized particles collision electrochemistry as it describes steady-state electrode potential distribution. Additionally, the effect of single nanoparticles (NPs) on potential should not be neglected because the size of a NP is comparable to that of an electrode. Herein, a theoretical model termed as Metal-Solution-Metal Nanoparticle (M-S-MNP) is proposed to reveal the dynamic electrode potential distribution at the single-nanoparticle level. An explicit equation is provided to describe the size/distance-dependent potential distribution in single NPs stochastic collision electrochemistry, showing the potential distribution is influenced by the NPs. Agreement between experiments and simulations indicates the potential roles of the M-S-MNP model in understanding the charge transfer process at the nanoscale.

CATKINAS: A large-scale catalytic microkinetic analysis software for mechanism auto-analysis and catalyst screening.

As an effective method to analyze complex catalytic reaction networks, microkinetic modeling is gaining increasing popularity in the catalytic activity evaluation and rational design of heterogeneous catalysts. An automated simulator with stable and reliable performance is especially useful and in great request. Here we introduce the CATKINAS package developed for large-scale microkinetic modeling and analysis. Featuring with a multilevel solver and a multifunctional analyzer, CATKINAS can provide both accurate solutions and various quantitative and automatic analysis for a wide range of catalytic systems. The structure and the basic workflow are overviewed with the multilevel solver particularly illustrated. Also, we take the CO methanation reaction as an example to illustrate the application and efficiency of the CATKINAS package.

Potential Role of Methanogens in Microbial Reductive Dechlorination of Organic Chlorinated Pollutants In Situ.

Previous studies often attribute microbial reductive dechlorination to organohalide-respiring bacteria (OHRB) or cometabolic dechlorination bacteria (CORB). Even though methanogenesis frequently occurs during dechlorination of organic chlorinated pollutants (OCPs) in situ, the underestimated effect of methanogens and their interactions with dechlorinators remains unknown. We investigated the association between dechlorination and methanogenesis, as well as the performance of methanogens involved in reductive dechlorination, through the use of meta-analysis, incubation experiment, untargeted metabolomic analysis, and thermodynamic modeling approaches. The meta-analysis indicated that methanogenesis is largely synchronously associated with OCP dechlorination, that OHRB are not the sole degradation engineers that maintain OCP bioremediation, and that methanogens are fundamentally needed to sustain microenvironment functional balance. Laboratory results further confirmed that Methanosarcina barkeri (M. barkeri) promotes the dechlorination of γ-hexachlorocyclohexane (γ-HCH). Untargeted metabolomic analysis revealed that the application of γ-HCH upregulated the metabolic functioning of chlorocyclohexane and chlorobenzene degradation in M. barkeri, further confirming that M. barkeri potentially possesses an auxiliary dechlorination function. Finally, quantum analysis based on density functional theory (DFT) indicated that the methanogenic coenzyme F430 significantly reduces the activation barrier to dechlorination. Collectively, this work suggests that methanogens are highly involved in microbial reductive dechlorination at OCP-contaminated sites and may even directly favor OCP degradation.

SSIA: A sensitivity-supervised interlock algorithm for high-performance microkinetic solving.

Microkinetic modeling has drawn increasing attention for quantitatively analyzing catalytic networks in recent decades, in which the speed and stability of the solver play a crucial role. However, for the multi-step complex systems with a wide variation of rate constants, the often encountered stiff problem leads to the low success rate and high computational cost in the numerical solution. Here, we report a new efficient sensitivity-supervised interlock algorithm (SSIA), which enables us to solve the steady state of heterogeneous catalytic systems in the microkinetic modeling with a 100% success rate. In SSIA, we introduce the coverage sensitivity of surface intermediates to monitor the low-precision time-integration of ordinary differential equations, through which a quasi-steady-state is located. Further optimized by the high-precision damped Newton's method, this quasi-steady-state can converge with a low computational cost. Besides, to simulate the large differences (usually by orders of magnitude) among the practical coverages of different intermediates, we propose the initial coverages in SSIA to be generated in exponential space, which allows a larger and more realistic search scope. On examining three representative catalytic models, we demonstrate that SSIA is superior in both speed and robustness compared with its traditional counterparts. This efficient algorithm can be promisingly applied in existing microkinetic solvers to achieve large-scale modeling of stiff catalytic networks.

Synergy of Single-Atom Ni1 and Ru1 Sites on CeO2 for Dry Reforming of CH4.

Heterogeneous catalysis performs on specific sites of a catalyst surface even if specific sites of many catalysts during catalysis could not be identified readily. Design of a catalyst by managing catalytic sites on an atomic scale is significant for tuning catalytic performance and offering high activity and selectivity at a relatively low temperature. Here, we report a synergy effect of two sets of single-atom sites (Ni1 and Ru1) anchored on the surface of a CeO2 nanorod, Ce0.95Ni0.025Ru0.025O2. The surface of this catalyst, Ce0.95Ni0.025Ru0.025O2, consists of two sets of single-atom sites which are highly active for reforming CH4 using CO2 with a turnover rate of producing 73.6 H2 molecules on each site per second at 560 °C. Selectivity for producing H2 at this temperature is 98.5%. The single-atom sites Ni1 and Ru1 anchored on the CeO2 surface of Ce0.95Ni0.025Ru0.025O2 remain singly dispersed and in a cationic state during catalysis up to 600 °C. The two sets of single-atom sites play a synergistic role, evidenced by lower apparent activation barrier and higher turnover rate for production of H2 and CO on Ce0.95Ni0.025Ru0.025O2 in contrast to Ce0.95Ni0.05O2 with only Ni1 single-atom sites and Ce0.95Ru0.05O2 with only Ru1 single-atom sites. Computational studies suggest a molecular mechanism for the observed synergy effects, which originate at (1) the different roles of Ni1 and Ru1 sites in terms of activations of CH4 to form CO on a Ni1 site and dissociation of CO2 to CO on a Ru1 site, respectively and (2) the sequential role in terms of first forming H atoms through activation of CH4 on a Ni1 site and then coupling of H atoms to form H2 on a Ru1 site. These synergistic effects of the two sets of single-atom sites on the same surface demonstrated a new method for designing a catalyst with high activity and selectivity at a relatively low temperature.

Spatial relationship between climatic diversity and biodiversity conservation value.

Capturing the full range of climatic diversity in a reserve network is expected to improve the resilience of biodiversity to climate change. Therefore, a study on systematic conservation planning for climatic diversity that explicitly or implicitly hypothesizes that regions with higher climatic diversity support greater biodiversity is needed. However, little is known about the extent and generality of this hypothesis. We used the case of Yunnan, southwest China, to quantitatively classify climatic units and modeled 4 climatic diversity indicators, including the variety (VCU), rarity (RCU), endemism (ECU) of climatic units, and a composite index of climatic diversity (CICD). We used 5 schemes that reliably identify priority conservation areas (PCAs) to identify areas with high biodiversity conservation value. We then investigated the spatial relationships between the 4 climatic diversity indicators and the results of the 5 PCA schemes and assessed the representation of climatic diversity within the existing nature reserves. The CICD was the best indicator of areas with high conservation value, followed by ECU and RCU. Contrary to conventional knowledge, VCU was not positively associated with biodiversity conservation value. The rarer or more endemic climatic units tended to have higher reserve coverage than the more common units. However, only 28 units, covering 10.5% of the land in Yunnan, had >17% of their areas protected. In addition to climatic factors, topography and human disturbances also significantly affected the relationship between climatic diversity and biodiversity conservation value. Our results suggest that climatic diversity can be an effective surrogate for establishing a more robust reserve network under climate change in Yunnan. Our study improves understanding of the relationship between climatic diversity and biodiversity and helps build an evidence-based foundation for systematic conservation planning that targets climatic diversity in response to climate change.

Feedback Kinetics in Mechanochemistry: The Importance of Cohesive States.

Although mechanochemical synthesis is becoming more widely applied and even commercialised, greater basic understanding is needed if the field is to progress on less of a trial-and-error basis. We report that a mechanochemical reaction in a ball mill exhibits unusual sigmoidal feedback kinetics that differ dramatically from the simple first-order kinetics for the same reaction in solution. An induction period is followed by a rapid increase in reaction rate before the rate decreases again as the reaction goes to completion. The origin of these unusual kinetics is found to be a feedback cycle involving both chemical and mechanical factors. During the reaction the physical form of the reaction mixture changes from a powder to a cohesive rubber-like state, and this results in the observed reaction rate increase. The study reveals that non-obvious and dynamic rheological changes in the reaction mixture must be appreciated to understand how mechanochemical reactions progress.

Enhancing Metal-Support Interactions by Molybdenum Carbide: An Efficient Strategy toward the Chemoselective Hydrogenation of α,ß-Unsaturated Aldehydes.

Metal-support interactions are desired to optimize the catalytic turnover on metals. Herein, the enhanced interactions by using a Mo2C nanowires support were utilized to modify the charge density of an Ir surface, accomplishing the selective hydrogenation of α,ß-unsaturated aldehydes on negatively charged Ir(δ-) species. The combined experimental and theoretical investigations showed that the Ir(δ-) species derive from the higher work function of Ir (vs. Mo2C) and the consequently electron transfer. In crotonaldehyde hydrogenation, Ir/Mo2C delivered a crotyl alcohol selectivity as high as 80%, outperforming those of counterparts (<30%) on silica. Moreover, such electronic metal-support interactions were also confirmed for Pt and Au, as compared with which, Ir/Mo2C was highlighted by its higher selectivity as well as the better activity. Additionally, the efficacy for various substrates further verified our Ir/Mo2C system to be competitive for chemoselective hydrogenation.

The origin of high activity but low CO(2) selectivity on binary PtSn in the direct ethanol fuel cell.

The most active binary PtSn catalyst for direct ethanol fuel cell applications has been studied at 20 °C and 60 °C, using variable temperature electrochemical in situ FTIR. In comparison with Pt, binary PtSn inhibits ethanol dissociation to CO(a), but promotes partial oxidation to acetaldehyde and acetic acid. Increasing the temperature from 20 °C to 60 °C facilitates both ethanol dissociation to CO(a) and then further oxidation to CO2, leading to an increased selectivity towards CO2; however, acetaldehyde and acetic acid are still the main products. Potential-dependent phase diagrams for surface oxidants of OH(a) formation on Pt(111), Pt(211) and Sn modified Pt(111) and Pt(211) surfaces have been determined using density functional theory (DFT) calculations. It is shown that Sn promotes the formation of OH(a) with a lower onset potential on the Pt(111) surface, whereas an increase in the onset potential is found upon modification of the (211) surface. In addition, Sn inhibits the Pt(211) step edge with respect to ethanol C-C bond breaking compared with that found on the pure Pt, which reduces the formation of CO(a). Sn was also found to facilitate ethanol dehydrogenation and partial oxidation to acetaldehyde and acetic acid which, combined with the more facile OH(a) formation on the Pt(111) surface, gives us a clear understanding of the experimentally determined results. This combined electrochemical in situ FTIR and DFT study provides, for the first time, an insight into the long-term puzzling features of the high activity but low CO2 production found on binary PtSn ethanol fuel cell catalysts.

A Multi-Resolution Denoising Method for Low-Dose CT Based on the Reconstruction of Wavelet High-Frequency Channel.

Computed tomography (CT) is widely applied in contemporary clinic. Due to the radiation risks carried by X-rays, the imaging and post-processing methods of low-dose CT (LDCT) become popular topics in academia and industrial community. Generally, LDCT presents strong noise and artifacts, while existing algorithms cannot completely overcome the blurring effects and meantime reduce the noise. The proposed method enables CT extend to independent frequency channels by wavelet transformation, then two separate networks are established for low-frequency denoising and high-frequency reconstruction. The clean signals from high-frequency channel are reconstructed through channel translation, which is essentially effective in preserving detailed structures. The public dataset from Mayo Clinic was used for model training and testing. The experiments showed that the proposed method achieves a better quantitative result (PSNR: 37.42dB, SSIM: 0.8990) and details recovery visually, which demonstrates our framework can better restore the detailed features while significantly suppressing the noise.

Multimodality Fusion Method Based on Multiview Subspace Clustering for Pulmonary Embolism Diagnosis.

Pulmonary embolism (PE) is an important clinical disorder that will result in lung tissue damage or low blood oxygen levels, which need early diagnosis and timely treatment. While computed tomographic pulmonary angiography (CTPA) is the gold standard to diagnose PE, previous studies have verified the effectiveness of combing CTPA and EMR data in computer-aided PE detection or diagnosis. In this paper, we proposed a multimodality fusion method based on multi-view subspace clustering guided feature selection (MSCUFS). The extracted high-dimensional image and EMR features are firstly selected and fused by the MSCUFS, and then are feed into different machine learning models with different fusion strategy to construct the PE classifier. The experiment results showed that the joint fusion strategy with MSCUFS achieved best AUROC of 0.947, surpassing other early fusion and late fusion models. The comparison between single modality and multimodality also illustrated the effectiveness of the proposed method.

Dual-Attention Model Fusing CNN and Transformer for Pancreas Segmentation.

Pancreatic cancer is a highly malignant cancer of the digestive tract and is rapidly progressing and spreading clinically. Automatic and accurate pancreatic tissue segmentation in abdominal CT images is essential for the early diagnosis of pancreatic-related diseases. It is challenging that the pancreas is small in size and complex in morphology. To address this problem, we propose a dual-attention model fusing CNN and Transformer to effectively activate pancreas-related features expression. The CNN structure weights the importance of pancreas-related features at the channel level and weakens the background information. Transformer feature aggregation module constructs spatial correlations among long-distance pixels from a global perspective. This study is validated on the NIH-TCIA dataset and achieved a mean Dice Similarity Coefficient of 85.82%, which is outperforming than the state-of-the-art methods. The visualization of surface distance also demonstrates the effective segmentation of pancreas boundary details by the proposed model.

Bubble-water/catalyst triphase interface microenvironment accelerates photocatalytic OER via optimizing semi-hydrophobic OH radical.

Photocatalytic water splitting (PWS) as the holy grail reaction for solar-to-chemical energy conversion is challenged by sluggish oxygen evolution reaction (OER) at water/catalyst interface. Experimental evidence interestingly shows that temperature can significantly accelerate OER, but the atomic-level mechanism remains elusive in both experiment and theory. In contrast to the traditional Arrhenius-type temperature dependence, we quantitatively prove for the first time that the temperature-induced interface microenvironment variation, particularly the formation of bubble-water/TiO2(110) triphase interface, has a drastic influence on optimizing the OER kinetics. We demonstrate that liquid-vapor coexistence state creates a disordered and loose hydrogen-bond network while preserving the proton transfer channel, which greatly facilitates the formation of semi-hydrophobic •OH radical and O-O coupling, thereby accelerating OER. Furthermore, we propose that adding a hydrophobic substance onto TiO2(110) can manipulate the local microenvironment to enhance OER without additional thermal energy input. This result could open new possibilities for PWS catalyst design.

Position Prior Attention Network for Pancreas Tumor Segmentation.

Segmentation of pancreatic tumors on CT images is essential for the diagnosis and treatment of pancreatic cancer. However, low contrast between the pancreas and the tumor, as well as variable tumor shape and position, makes segmentation challenging. To solve the problem, we propose a Position Prior Attention Network (PPANet) with a pseudo segmentation generation module (PSGM) and a position prior attention module (PPAM). PSGM and PPAM maps pancreatic and tumor pseudo segmentation to latent space to generate position prior attention map and supervises location classification. The proposed method is evaluated on pancreatic patient data collected from local hospital and the experimental results demonstrate that our method can significantly improve the tumor segmentation results by introducing the position information in the training phase.

Whole-Liver Based Deep Learning for Preoperatively Predicting Overall Survival in Patients with Hepatocellular Carcinoma.

Survival prediction is crucial for treatment decision making in hepatocellular carcinoma (HCC). We aimed to build a fully automated artificial intelligence system (FAIS) that mines whole-liver information to predict overall survival of HCC. We included 215 patients with preoperative contrast-enhance CT imaging and received curative resection from a hospital in China. The cohort was randomly split into developing and testing subcohorts. The FAIS was constructed with convolutional layers and full-connected layers. Cox regression loss was used for training. Models based on clinical and/or tumor-based radiomics features were built for comparison. The FAIS achieved C-indices of 0.81 and 0.72 for the developing and testing sets, outperforming all the other three models. In conclusion, our study suggest that more important information could be mined from whole liver instead of only the tumor. Our whole-liver based FAIS provides a non-invasive and efficient overall survival prediction tool for HCC before the surgery.

Partial Label Multi-organ Segmentation based on Local Feature Enhancement.

The automatic segmentation of abdominal organs from CT images is essential for surgical planning of abdominal diseases. However, each medical institution only annotates some organs according to its own clinical practice. This brings the partial annotation problem to multi-center abdominal multi-organ segmentation. To address this issue, we introduce a 3D local feature enhanced multi-head segmentation network for multi-organ segmentation of abdominal regions in multiple partially labeled datasets. More specifically, our proposed architecture consists of two branches, the global branch with 3D Transformer and U-Net fusion named 3D TransUNet as the backbone, and the local 3D U-Net branch that provides additional abdominal organ structure information to the global branch to generate more accurate segmentation results. We evaluate our method on four publicly available CT datasets with four different partial label. Our experiments show that the proposed approach provides better accuracy and robustness, with 93.01% average Dice-score-coefficient (DSC) and 3.489 mm Hausdorff Distance (HD) outperforming three existing state-of-the-art methods.

Siamese Delta Network with Multimodality Fusion for Systemic Treatment Response Prediction in Pancreatic Cancer.

Systemic treatment is a main way for pancreas cancer patients that are ineligible for surgery. A subgroup of patients showed good response to systemic treatment and the rest received limited benefits. CT images provide a non-invasive way to assess the treatment response. Alternative non-image methods include radiology analysis, tumor marker analysis and combination analysis. To combine the image and non-image data, we propose the Siamese Delta Network with Multimodality Fusion (SDN-MF) to predict systemic treatment response in an end-to-end way. First, a Siamese Delta Network (SDN) is designed to process pre-treatment and pre-surgery CT images and get the image feature changes to predict response. Then, patients' characteristics from EMR and alternative analysis results forms non-image data, which is incorporated into SDN with a multimodality fusion (MF) module. The proposed SDN-MF is evaluated on a private dataset and achieves average AUC value of 0.883 with five cross-validation. Comparison among image-only, non-image-only, and fusion models verifies the superior of multimodality model in predicting systemic treatment response of pancreas cancer patients.

Mass Transport and Electron Transfer at the Electrochemical-Confined Interface.

Single entity measurements based on the stochastic collision electrochemistry provide a promising and versatile means to study single molecules, single particles, single droplets, etc. Conceptually, mass transport and electron transfer are the two main processes at the electrochemically confined interface that underpin the most transient electrochemical responses resulting from the stochastic and discrete behaviors of single entities at the microscopic scale. This perspective demonstrates how to achieve controllable stochastic collision electrochemistry by effectively altering the two processes. Future challenges and opportunities for stochastic collision electrochemistry are also highlighted.

Prediction of New-Onset Diabetes after Pancreatectomy with Subspace Clustering based Multi-view Feature Selection.

The pancreas plays an important role in glucose metabolism, and developing diabetes or long-term glucose metabolism disturbance may be a prevalent sequela after pancreatectomy. Nevertheless, relative factors of new-onset diabetes after pancreatectomy stay unclear. Radiomics analysis is potential to identify image markers for disease prediction or prognosis. Meanwhile, combination of imaging and electronic medical record (EMR) showed superior performance than imaging or EMR alone in previous studies. One critical step is to identity predictors from high-dimensional features, and it is even more challenging to select and fuse imaging and EMR features. In this work, we develop a radiomics pipeline to assess postoperative new-onset diabetes risk of patients undergoing distal pancreatectomy. Specifically, we extract multiscale image features with 3D wavelet transformation, and include patients' characteristics, body composition and pancreas volume information as clinical features. Then, we propose a multi-view subspace clustering guided feature selection method (MSCUFS) for the selection and fusion of image and clinical features. Finally, a prediction model is constructed with classical machine learning classifier. Experimental results on an established distal pancreatectomy cohort showed that the SVM model with combined imaging and EMR features demonstrated good discrimination, with an AUC value of 0.824, which improved the model with image features alone by 0.037 AUC. Compared with state-of-the-art feature selection methods, the proposed MSCUFS has superior performance in fusing image and clinical features.