Ion Irradiation-Induced Coordinatively Unsaturated Zn Sites for Enhanced CO Hydrogenation.

Defect engineering critically influences metal oxide catalysis, yet controlling coordinatively unsaturated metal sites remains challenging due to their inherent instability under reaction conditions. Here, we demonstrate that high-flux argon ion (Ar) irradiation above recrystallization temperatures generated well-defined coordinatively unsaturated Zn (CUZ) sites on ZnO(101̅0) surfaces that exhibited enhanced stability and activity for CO hydrogenation. Combining low-temperature scanning probe microscopy, ambient pressure X-ray photoelectron spectroscopy, and surface-ligand infrared spectroscopy with density functional theory calculations, we identified <12̅10> step edges exposing CUZ sites as the dominant active sites.

Insight into photocatalytic CO reduction on TiO-supported Cu nanorods: a DFT study on the reaction mechanism and selectivity.

Photoreduction of CO into hydrocarbons is a potential strategy for reducing atmospheric CO and effectively utilizing carbon resources. Cu-deposited TiO photocatalysts stand out in this area due to their good photocatalytic activity and potential methanol selectivity. However, the underlying mechanism and factors controlling product selectivity remain less understood.

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/TiO(110) triphase interface, has a drastic influence on optimizing the OER kinetics.

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).

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.

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).

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.

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.

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.

Mining Whole-liver Information with Deep Learning for Preoperatively Predicting HCC Recurrence-free Survival.

Hepatocellular carcinoma (HCC) is globally a leading cause of cancer death. Non-invasive pre-operative prediction of HCC recurrence-free survival (RFS) after resection is essential but remains challenging. Previous models based on medical imaging focus only on tumor area while neglecting the whole liver condition.

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.

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.

Multiscale unsupervised domain adaptation for automatic pancreas segmentation in CT volumes using adversarial learning.

Purpose: Computer-aided automatic pancreas segmentation is essential for early diagnosis and treatment of pancreatic diseases. However, the annotation of pancreas images requires professional doctors and considerable expenditure. Due to imaging differences among various institution population, scanning devices, imaging protocols, and so on, significant degradation in the performance of model inference results is prone to occur when models trained with domain-specific (usually institution-specific) datasets are directly applied to new (other centers/institutions) domain data.

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 NiS/CrS 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.

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.

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

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) , 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.

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.

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.

Automatic Pancreas Segmentation in CT Images With Distance-Based Saliency-Aware DenseASPP Network.

Pancreas identification and segmentation is an essential task in the diagnosis and prognosis of pancreas disease. Although deep neural networks have been widely applied in abdominal organ segmentation, it is still challenging for small organs (e.g.

Iridium single-atom catalyst on nitrogen-doped carbon for formic acid oxidation synthesized using a general host-guest strategy.

Single-atom catalysts not only maximize metal atom efficiency, they also display properties that are considerably different to their more conventional nanoparticle equivalents, making them a promising family of materials to investigate. Herein we developed a general host-guest strategy to fabricate various metal single-atom catalysts on nitrogen-doped carbon (M/CN, M = Pt, Ir, Pd, Ru, Mo, Ga, Cu, Ni, Mn). The iridium variant Ir/CN electrocatalyses the formic acid oxidation reaction with a mass activity of 12.

Exploring dynamic interactions of single nanoparticles at interfaces for surface-confined electrochemical behavior and size measurement.

With the development of new instruments and methodologies, the highly dynamic behaviors of nanoparticle at the liquid-solid interface have been studied. However, the dynamic nature of the electrochemical behavior of individual nanoparticles on the electrode interface is still poorly understood. Here, we generalize scaling relations to predict nanoparticle-electrode interactions by examining the adsorption energy of nanoparticles at an ultramicroelectrode interface.