The integration of artificial intelligence (AI) with high-throughput experimentation (HTE) techniques is revolutionizing catalyst design, addressing challenges in efficiency, cost, and scalability. This review explore...The integration of artificial intelligence (AI) with high-throughput experimentation (HTE) techniques is revolutionizing catalyst design, addressing challenges in efficiency, cost, and scalability. This review explores the synergistic application of AI and HTE, highlighting their role in accelerating catalyst discovery, optimizing reaction parameters, and understanding structure-performance relationships. HTE facilitates the rapid preparation, characterization, and evaluation of diverse catalyst formulations, generating large datasets essential for AI model training. Machine learning algorithms, including regression models, neural networks, and active learning frameworks, analyze these datasets to uncover the underlying relationships between the data, predict performance, and optimize experimental workflows in real-time. Case studies across heterogeneous, homogeneous, and electrocatalysis demonstrate significant advancements, including improved reaction selectivity, enhanced material stability, and shorten discovery cycles. The integration of AI with HTE has significantly accelerated discovery cycles, enabling the optimization of catalyst formulations and reaction conditions. Despite these achievements, challenges remain, including reliance on researcher expertise, real-time adaptability, and the complexity of large-scale data analysis. Addressing these limitations through refined experimental protocols, standardized datasets, and interpretable AI models will unlock the full potential of AI-HTE integration.展开更多
Bipolar disorder(BD)is a severe mood disorder characterized by recurrent episodes of mania and depression,and it is prone to delayed diagnosis,which can lead to worsened outcomes,including more frequent mood episodes,...Bipolar disorder(BD)is a severe mood disorder characterized by recurrent episodes of mania and depression,and it is prone to delayed diagnosis,which can lead to worsened outcomes,including more frequent mood episodes,greater functional impairment,and comorbidities.Early diagnosis of BD remains a significant challenge,although recent advances offer promising insights,such as research in molecular biomarkers,neuroimaging,exosomes,genetics,and epigenetics.This mini-review highlights their potential for providing earlier,more accurate identification of BD and discusses the underlying reasons why current research has not yet succeeded.For instance,the high heterogeneity of symptomatic presentations leads to low consistency in study participants;delayed BD diagnosis results in the inclusion of potential BD patients in the depression group;low specificity of biomarkers stems from limited understanding of BD pathophysiology;and there is a possibility that BD is not innate but develops over the course of the disease.Deepening our understanding of BD pathology,identifying more specific biomarkers,and integrating multiomics approaches for validation studies in well-defined homogeneous cohorts hold promise for significant breakthroughs.展开更多
The effect of the presence of trace SO_(2)in industrial flue gas on the amine-scrubbing-based absorption process for CO_(2)capture has been a matter of concern.This study aimed to investigate the effect of trace SO_(2...The effect of the presence of trace SO_(2)in industrial flue gas on the amine-scrubbing-based absorption process for CO_(2)capture has been a matter of concern.This study aimed to investigate the effect of trace SO_(2)on the CO_(2)capture process using piperazine-based amine absorbents,focusing on SO_(2)-resistance capability,SO_(2)/CO_(2)absorption selectivity,and cyclic stability.The presence of trace SO_(2)not only restrains CO_(2)absorption,but also promotes the formation of carbamate within the piperazine-based amine absorbents.Remarkably,the incorporation of aminoethyl group in piperazine-based amine absorbents can enhance the SO_(2)-resistance capability by promoting the formation of carbamate,while piperazine-based amine absorbents with hydroxyethyl group can promote the formation of bicarbonate to reduce the SO_(2)-resistance capability.The work offers valuable insights into the efficient application of novel amine absorbents for CO_(2)capture from practical industrial flue gas.展开更多
Recently,weak supervision has received growing attention in the field of salient object detection due to the convenience of labelling.However,there is a large performance gap between weakly supervised and fully superv...Recently,weak supervision has received growing attention in the field of salient object detection due to the convenience of labelling.However,there is a large performance gap between weakly supervised and fully supervised salient object detectors because the scribble annotation can only provide very limited foreground/background information.Therefore,an intuitive idea is to infer annotations that cover more complete object and background regions for training.To this end,a label inference strategy is proposed based on the assumption that pixels with similar colours and close positions should have consistent labels.Specifically,k-means clustering algorithm was first performed on both colours and coordinates of original annotations,and then assigned the same labels to points having similar colours with colour cluster centres and near coordinate cluster centres.Next,the same annotations for pixels with similar colours within each kernel neighbourhood was set further.Extensive experiments on six benchmarks demonstrate that our method can significantly improve the performance and achieve the state-of-the-art results.展开更多
基金supported by the Special Project of National Natural Science Foundation(42341204)the the National Natural Science Foundation of China(W2411009).
文摘The integration of artificial intelligence (AI) with high-throughput experimentation (HTE) techniques is revolutionizing catalyst design, addressing challenges in efficiency, cost, and scalability. This review explores the synergistic application of AI and HTE, highlighting their role in accelerating catalyst discovery, optimizing reaction parameters, and understanding structure-performance relationships. HTE facilitates the rapid preparation, characterization, and evaluation of diverse catalyst formulations, generating large datasets essential for AI model training. Machine learning algorithms, including regression models, neural networks, and active learning frameworks, analyze these datasets to uncover the underlying relationships between the data, predict performance, and optimize experimental workflows in real-time. Case studies across heterogeneous, homogeneous, and electrocatalysis demonstrate significant advancements, including improved reaction selectivity, enhanced material stability, and shorten discovery cycles. The integration of AI with HTE has significantly accelerated discovery cycles, enabling the optimization of catalyst formulations and reaction conditions. Despite these achievements, challenges remain, including reliance on researcher expertise, real-time adaptability, and the complexity of large-scale data analysis. Addressing these limitations through refined experimental protocols, standardized datasets, and interpretable AI models will unlock the full potential of AI-HTE integration.
基金Supported by Research Plan Project of Tianjin Municipal Education Commission,No.2022KJ264.
文摘Bipolar disorder(BD)is a severe mood disorder characterized by recurrent episodes of mania and depression,and it is prone to delayed diagnosis,which can lead to worsened outcomes,including more frequent mood episodes,greater functional impairment,and comorbidities.Early diagnosis of BD remains a significant challenge,although recent advances offer promising insights,such as research in molecular biomarkers,neuroimaging,exosomes,genetics,and epigenetics.This mini-review highlights their potential for providing earlier,more accurate identification of BD and discusses the underlying reasons why current research has not yet succeeded.For instance,the high heterogeneity of symptomatic presentations leads to low consistency in study participants;delayed BD diagnosis results in the inclusion of potential BD patients in the depression group;low specificity of biomarkers stems from limited understanding of BD pathophysiology;and there is a possibility that BD is not innate but develops over the course of the disease.Deepening our understanding of BD pathology,identifying more specific biomarkers,and integrating multiomics approaches for validation studies in well-defined homogeneous cohorts hold promise for significant breakthroughs.
基金supported by the Major Science and Technology Project of Anhui Province(201903a07020004)the National Natural Science Foundation of China(22208078)the Fundamental Research Funds for the Central Universities(JZ2023HGTB0226).
文摘The effect of the presence of trace SO_(2)in industrial flue gas on the amine-scrubbing-based absorption process for CO_(2)capture has been a matter of concern.This study aimed to investigate the effect of trace SO_(2)on the CO_(2)capture process using piperazine-based amine absorbents,focusing on SO_(2)-resistance capability,SO_(2)/CO_(2)absorption selectivity,and cyclic stability.The presence of trace SO_(2)not only restrains CO_(2)absorption,but also promotes the formation of carbamate within the piperazine-based amine absorbents.Remarkably,the incorporation of aminoethyl group in piperazine-based amine absorbents can enhance the SO_(2)-resistance capability by promoting the formation of carbamate,while piperazine-based amine absorbents with hydroxyethyl group can promote the formation of bicarbonate to reduce the SO_(2)-resistance capability.The work offers valuable insights into the efficient application of novel amine absorbents for CO_(2)capture from practical industrial flue gas.
文摘Recently,weak supervision has received growing attention in the field of salient object detection due to the convenience of labelling.However,there is a large performance gap between weakly supervised and fully supervised salient object detectors because the scribble annotation can only provide very limited foreground/background information.Therefore,an intuitive idea is to infer annotations that cover more complete object and background regions for training.To this end,a label inference strategy is proposed based on the assumption that pixels with similar colours and close positions should have consistent labels.Specifically,k-means clustering algorithm was first performed on both colours and coordinates of original annotations,and then assigned the same labels to points having similar colours with colour cluster centres and near coordinate cluster centres.Next,the same annotations for pixels with similar colours within each kernel neighbourhood was set further.Extensive experiments on six benchmarks demonstrate that our method can significantly improve the performance and achieve the state-of-the-art results.
