bluuebunny/crossref_metadata_2025_split huggingface dataset:
Standard Approaches to XPS and AES Quantification—A Summary of ISO 18118:2024 on the Use of Relative Sensitivity Factors
Authors: Cant David J. H. , Gorham Justin M. , Clifford Charles A. , Shard Alexander G. 
Published: November 2024

ABSTRACTQuantitative analysis of electron spectroscopy data from techniques such as Auger electron spectroscopy (AES) and X‐ray photoelectron spectroscopy (XPS) typically requires the use of relative sensitivity factors (RSFs). Virtually, all routine XPS experiments attempt to turn peak intensities into elemental or chemical compositions using RSFs. The comparability and reproducibility of surface chemical analysis by electron spectroscopies therefore require the standardisation of RSFs and their use. RSFs may be determined either from theoretical calculations based on photo‐ionisation cross‐sections or empirically through direct measurement of homogeneous reference samples of known composition. The accurate use of sensitivity factors, even empirically determined RSFs, requires accounting for the effect of material differences on relative intensities, the so‐called ‘matrix effects’. ISO 18118:2024 is the most recently revised version of the ISO standard for the use and determination of empirical RSFs for quantitative analysis of homogeneous materials by AES and XPS. This article provides a summary of the new, updated standard and the methods described therein, noting in particular the revisions made since the previous edition, ISO 18118:2015.
Instrumental Variable Model Average With Applications in Nonlinear Causal Inference
Authors: Chen Dong , Wang Yuquan , Shi Dapeng , Cao Yunlong , Hu Yue‐Qing 
Published: November 2024

ABSTRACTThe instrumental variable method is widely used in causal inference research to improve the accuracy of estimating causal effects. However, the weak correlation between instruments and exposure, as well as the direct impact of instruments on the outcome, can lead to biased estimates. To mitigate the bias introduced by such instruments in nonlinear causal inference, we propose a two‐stage nonlinear causal effect estimation based on model averaging. The model uses different subsets of instruments in the first stage to predict exposure after a nonlinear transformation with the help of sliced inverse regression. In the second stage, adaptive Lasso penalty is applied to instruments to obtain the estimation of causal effect. We prove that the proposed estimator exhibits favorable asymptotic properties and evaluate its performance through a series of numerical studies, demonstrating its effectiveness in identifying nonlinear causal effects and its capability to handle scenarios with weak and invalid instruments. We apply the proposed method to the Atherosclerosis Risk in Communities dataset to investigate the relationship between BMI and hypertension.
Using Wearable Technology to Objectively Investigate Physical Behaviour and Determine Health Outcomes of a Physical Activity Intervention in Patients with Psoriasis
Authors: Sheppard Rory , Gan Weh K. , Onambele-Pearson Gladys L. , Young Helen S. 
Published: November 2024

Abstract

Background
Sedentary behaviour has a detrimental effect on health independent of the amount of physical activity undertaken. Despite the association of cardiometabolic and psychosocial comorbidities with psoriasis, how physical behaviour influences health outcomes in patients with psoriasis is poorly understood.

Objectives
We hypothesized that objective measurement of physical behaviour, using wearable digital technology, would have utility in understanding the clinical impact of an exercise intervention designed in partnership with patients with psoriasis.

Methods
Fourteen patients with psoriasis completed a 20-week study. During weeks 1–10, participants followed an incrementally progressive, exercise intervention, followed by independent activities during weeks 11–20. Accelerometers (GENEActiv Original) and pedometers recorded physical behaviour. Evaluation at week-0, -10, and -20 included assessment of psoriasis, cardiometabolic disease/risk, psychological health and functional capacity.

Results
Our intervention supported significantly increased physical activity, including moderate–vigorous physical activity (p = 0.04) and step count (p = 0.04). We also observed a significant association between physical activity and psoriasis area and severity index (PASI)-50 response (p = 0.01) and psychosocial functioning (p = 0.029) together with a significant negative correlation between step count and psoriasis severity (p = 0.012). We observed no change in total waking hour sedentary behaviour.

Conclusion
Objective measurement of physical behaviour, using wearable digital technologies, offers a mechanism to further understand the clinical impact of lifestyle behaviour interventions. Crucially, despite increased levels of physical activity, we observed no change in total waking hour sedentary time. Further investigation is required to establish how modification of physical behaviour could offer an adjuvant management strategy for patients with psoriasis.
Multifunctional Effect of Carbon Matrix with Sulfur Dopant Inspires Mixed Metal Sulfide as Freestanding Anode in Sodium‐Ion Batteries
Authors: Zhang Peilin , Huang Kang , Zhu Siyu , Xiu Mingzhen , Cao Xun , Huang Chen , Huang Yizhong , Chen Luyang 
Published: November 2024

AbstractAs active materials for large‐radius Na+ storage, metal sulfide (MS) anodes still face several challenges, including poor intrinsic electric conductivity, severe volume change along with the shuttle and dissolution effects of discharge‐produced polysulfides. In this work, the mixed nickel‐manganese sulfides (NiMnS) in the morphology of uniform 2D ultrathin nanosheets are derived from layered metal‐organic frameworks (MOFs), where the NiS‐MnS heterojunction are accommodated in carbon matrix with S dopant (NiMnS/SC). Through experiments and density functional theory (DFT) simulations, it is revealed that the carbon matrix with S dopant has multifunctional effects on active NiMnS during the charge/discharge process, including conductive intermediary, electrochemical active site, physical barrier, and chemical adsorption. This work may promote the design of MS‐based electrodes in SIBs and extend the application of carbon material with S dopant in Na‐S batteries.
Size‐Dependent Effects of ZIF‐8 Derived Cathode Materials on Performance of Zinc‐Ion Capacitors
Authors: Li Jiaxin , Hua Yumeng , Gao Yanshen , Li Shiyun , Kędzierski Tomasz , Mijowska Ewa , Chu Paul K. , Holze Rudolf , He Yi , Bi Wuguo , Chen Xuecheng 
Published: November 2024

AbstractZinc‐ion capacitors (ZICs) have attracted great attention due to a series of advantages. However, the cathode materials are still the bottleneck for high‐performance ZICs to be achieved. Therefore, ZIF‐8‐derived porous carbons are one of the most promising candidates but ZIF‐8 nanoparticles with different sizes exhibited various electrochemical performances in ZICs. Herein, a series of monodispersed ZIF‐8 nanoparticles are first prepared by a temperature‐controlled process to fabricate the corresponding ZIF‐8‐based porous carbon nanoparticles with pre‐designed sizes. The as‐prepared materials have been tested as cathode materials in ZICs. Thus, their size effect allowed us to disclose its correlation with other factors such as ion transport/storage and capacitance. The results reveal that the optimal‐sized porous carbon particles can effectively shorten the ion transport distance and accelerate the ion diffusion rate, resulting in lower electrical resistance, larger ion diffusion coefficients, and faster electron transport. The presented findings can facilitate the design of new advanced cathode materials paving the way for the development of high‐performance cathode materials for ZICs in the future.
EGCG‐enabled Deep Tumor Penetration of Phosphatase and Acidity Dual‐responsive Nanotherapeutics for Combinatory Therapy of Breast Cancer
Authors: Zhou Mengxue , Zhou Chuang , Geng Huan , Huang Zhiwei , Lin Zhiyuan , Wang Ying , Zhu Yin , Shi Jiang , Tan Junfeng , Guo Li , Zhao Yanni , Zhang Yue , Peng Qunhua , Yu Haijun , Dai Weidong , Lv Haipeng , Lin Zhi 
Published: November 2024

AbstractThe presence of dense collagen fibers is a typical characteristic of triple‐negative breast cancer (TNBC). Although these fibers hinder drug penetration and reduce treatment efficacy, the depletion of the collagen matrix is associated with tumor metastasis. To address this issue, epigallocatechin‐3‐gallate (EGCG) is first exploited for disrupting the dense collagenous stroma and alleviate fibrosis by specifically blocking the TGF‐β/Smad pathway in fibroblasts and tumor cells when intraperitoneally administrated in TNBC tumor‐bearing mice. A methotrexate (MTX)‐loaded dual phosphate‐ and pH‐responsive nanodrug (pHA@MOF‐Au/MTX) is next engineered by integrating Fe‐based metal–organic frameworks and gold nanoparticles for improved chemo/chemodynamic therapy of TNBC. Surface modification with pH (low)‐insertion peptide substantially enhanced the binding of the nanodrug to 4T1 cells owing to tumor stroma remodeling by EGCG. High‐concentration EGCG inhibited glutathione peroxidase by regulating mitochondrial glutamine metabolism, thus facilitating tumor cell ferroptosis. Furthermore, sequential EGCG and pHA@MOF‐Au/MTX treatment showed remarkable anti‐tumor effects in a mouse model of TNBC, with a tumor growth inhibition rate of 79.9%, and a pulmonary metastasis rate of 96.8%. Altogether, the combination strategy developed in this study can improve the efficacy of chemo/chemodynamic therapy in TNBC and represents an innovative application of EGCG.
Cutting‐Edge Developments in Metal Halide Perovskites Core/Shell Heterocrystals: from Photodetectors to Biomedical Applications
Authors: Saleem Muhammad Imran , Batool Attia , Hur Jaehyun 
Published: November 2024

AbstractIn recent years, the performance of metal halide perovskite (MHP)‐based detectors (photon, biomedical, and X‐ray detection) has significantly improved, resulting in higher carrier mobilities, longer carrier diffusion lengths, and excellent absorption coefficients. However, the widespread adoption of halide perovskites has been hindered by issues related to their stability and toxicity. Various strategies have been adopted to address these challenges, focusing on enhancing ambient stability and reducing toxicity by encapsulating MHPs within stable and robust host materials, such as silicon compounds, metal oxides, chalcogenides, and lead‐free perovskites. This review focuses on recent developments in hybrid nanostructure‐based detectors (photon, biomedical, and X‐ray), particularly core/shell architectures, and provides a comprehensive analysis of techniques for mitigating degradation due to light and oxygen exposure, UV irradiance, and thermal effects. This review enhances the understanding of current advancements in core/shell‐based detectors.
Fine‐Tuning Conformation of PEDOT Chains Enables Simultaneously Enhanced Conductivity, Work Function, Transmittance, and Waterproofness in PEDOT:PSS Interlayer for Highly Efficient and Stable Organic Photovoltaics
Authors: Zhang Jingyao , Dong Yunqiao , Xie Jiefeng , Li Zhenye 
Published: November 2024

AbstractPoly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) is widely utilized as the hole transport layer (HTL) inorganic photovoltaics (OPVs) because of its low‐temperature solution processing peculiarity, high optical transmittance, and excellent mechanical flexibility. However, the core–shell structure of PSS coated PEDOT results in relatively low conductivity, work function, transmittance and waterproofness of PEDOT:PSS interlayer, limiting the photovoltaic performance and stability of OPVs. Here, the conformation of PEDOT chains are regulated from helical benzoyl to linear quinone structure via incorporation of 2D Cd0.85PS3Li0.15H0.15dopant into the conventional PEDOT:PSS interlayer, promoting an interpenetrating network structure in PEDOT:PSS interlayer and forming an efficient hole transport channel from active layer to ITO electrode. Such features significantly improve the electrical conductivity, work function, and transmittance of PEDOT:PSS interlayer. In consequence, the maximum power conversion efficiency (PCE) of D18:L8‐BO, PBDB‐T:ITIC, as well as PTzBI‐dF:L8‐BO based OPVs ameliorated from 18.37%, 8.94%, and 15.80% to 19.26%, 10.00%, and 16.83%, respectively. The application of Cd0.85PS3Li0.15H0.15 doping PEDOT:PSS strategy demonstrates great potential for the development of strongly conductive, large‐work‐function, highly transparent, and excellent‐waterproof PEDOT:PSS interlayer toward highly efficient and stable OPVs.
Decoupled Water Reduction and Hydrazine Oxidation by Fast Proton Transport MoO3 Redox Mediator for Hydrogen Production
Authors: Song AJing , Wei Yuan , Jin Xin , Ma Yuanyuan , Wang Yonggang , Yang Jianping 
Published: November 2024

AbstractWater electrolysis powered by renewable energy is a green and sustainable method for hydrogen production. Decoupled water electrolysis with the aid of solid‐state redox mediator could separate the hydrogen and oxygen production in time and space without the use of the membrane, showing high flexibility. Herein, a MoO3 electrode with fast proton transport property is employed as a solid‐state redox mediator to construct a membrane‐free decoupled acidic electrolytic system. The MoO3 electrode exhibits high specific capacity (204.3 mAh g−1 at 5 A g−1) and excellent rate performance (92.8 mAh g−1 at 150 A g−1) in the acidic environment. Due to the dense oxide‐ion arrays, MoO3 still exhibits excellent performance under high mass‐loading. In addition, a hybrid decoupled electrolysis system is also constructed by combining water reduction and hydrazine oxidation, which can not only generate high‐purity H2 but also remove hydrazine hazards in acidic wastewater with lower energy consumption.
Garnet‐Type Solid‐State Electrolytes: Crystal‐Phase Regulation and Interface Modification for Enhanced Lithium Metal Batteries
Authors: Wu Jialong , Chen Weiheng , Hao Bin , Jiang Zhong‐Jie , Jin Guangri , Jiang Zhongqing 
Published: November 2024

AbstractDue to their substantial energy density, rapid charging and discharging rates, and extended lifespan, lithium‐ion batteries have attained broad application across various industries. However, their limited theoretical capacity struggles to meet the growing demand for battery capacity in consumer electronics, automotive, and aerospace applications. As a promising substitute, solid‐state lithium‐metal batteries (SSLBs) have emerged, utilizing a lithium‐metal anode that boasts a significant theoretical specific capacity and non‐flammable solid‐state electrolytes (SSEs) to address energy density limitations and safety concerns. For SSLBs to attain large‐scale commercial viability, SSEs require heightened ionic‐conductivity, improved mechanical characteristics, and enhanced chemical and electrochemical stability. Furthermore, tackling the challenges related to interfacial contacts between SSEs and the lithium‐metal anode is imperative. This review comprehensively overviews the primary methods used to prepare garnet SSEs and summarizes doping strategies for various sites on Li7La3Zr2O12 (LLZO) garnet SSEs, aiming to optimize the crystal phase to achieve more favorable properties in SSE applications. Additionally, it discusses strategies for modifying the interfacial contact between the lithium‐metal anode and SSEs, classifying them into three areas: surface modification, interlayer‐modification, and composite anodes. This review aims to serve as a valuable reference for future researchers working on high‐performance garnet SSEs and effective interfacial‐modification strategies.
Impact of Interfaces on the Performance of Covalent Organic Frameworks for Photocatalytic Hydrogen Production
Authors: Wang Lin , Zhang Yong 
Published: November 2024

AbstractThe rise in global temperatures and environmental contamination resulting from traditional fossil fuel usage has prompted a search for alternative energy sources. Utilizing solar energy to drive the direct splitting of water for hydrogen production has emerged as a promising solution to these challenges. Covalent organic frameworks (COFs) are ordered, crystalline materials made up of organic molecules linked by covalent bonds, featuring permanent porosity and a wide range of structural topologies. COFs serve as suitable platforms for solar‐driven water splitting to produce hydrogen, as their building blocks can be tailored to possess adjustable band gaps, charge separation capabilities, porosity, wettability, and chemical stability. Here, the impact of the interface in the context of the photocatalytic reaction is focused and propose strategies to enhance the hydrogen production performance of COFs photocatalysis. In particular, how hybrid photocatalytic interfaces affect photocatalytic performance is focused.
A Process Model for AI‐Enabled Software Development: A Synthesis From Validation Studies in White Literature
Authors: Gurgen Erdogan Tugba , Altunel Haluk , Tarhan Ayça Kolukısa 
Published: November 2024

ABSTRACTContextWith the fast advancement of techniques in artificial intelligence (AI) and of the target infrastructures in the last decades, AI software is becoming an undeniable part of software system projects. As in most cases in history, however, development methods and guides follow the advancements in technology with phase differences.PurposeWith an aim to elicit and integrate available evidence from AI software development practices into a process model, this study synthesizes the contributions of the validation studies reported in scientific literature.MethodWe applied a systematic literature review to retrieve, select, and analyze the primary studies. After a comprehensive and rigorous search and scoping review, we identified 82 studies that make various contributions in relation to AI software development practices. To increase the effectiveness of the synthesis and the usefulness of the outcome, for detailed analysis, we selected 14 primary studies (out of 82) that empirically validated their contributions.ResultsWe carefully reviewed the selected studies that validate proposals on approaches/models, methods/techniques, tasks/phases, lessons learned/best practices, or workflows. We mapped the steps/activities in these proposals with the knowledge areas in SWEBOK, and using the evidence in this mapping and the primary studies, we synthesized a process model that integrates activities, artifacts, and roles for AI‐enabled software system development.ConclusionTo the best of our knowledge, this is the first study that proposes such a process model by eliciting and gathering the contributions of the validation studies in a bottom‐up manner. We expect that the output of this synthesis will be input for further research to validate or improve the process model.
Label‐Free Prediction of Tumor Metastatic Potential via Ramanome
Authors: Zhang Yuxing , Zhang Yanmei , Gong Ruining , Liu Xiaolan , Zhang Yu , Sun Luyang , Ma Qingyue , Wang Jia , Lei Ke , Ren Linlin , Zhao Chenyang , Zheng Xiaoshan , Xu Jian , Ren He 
Published: November 2024

AbstractAssessing metastatic potential is crucial for cancer treatment strategies. However, current methods are time‐consuming, labor‐intensive, and have limited sample accessibility. Therefore, this study aims to investigate the urgent need for rapid and accurate approaches by proposing a Ramanome‐based metastasis index (RMI) using machine learning of single‐cell Raman spectra to rapidly and accurately assess tumor cell metastatic potential. Validation with various cultured tumor cells and a mouse orthotopic model of pancreatic ductal adenocarcinoma show a Kendall rank correlation coefficient of 1 compared to Transwell experiments and histopathological assessments. Significantly, lipid‐related Raman peaks are most influential in determining RMI. The lipidomic analysis confirmed strong correlations between metastatic potential and phosphatidylcholine, phosphatidylethanolamine, cholesteryl ester, ceramide, and bis(monoacylglycero)phosphate, crucial in cell membrane composition or signal transduction. Therefore, RMI is a valuable tool for predicting tumor metastatic potential and providing insights into metastasis mechanisms.
A Comprehensive Review of Analytical Techniques for Quantifying Cyclin‐dependent Kinase 4 and 6 Inhibitors in Biological Samples, Bulk, and Pharmaceutical Samples
Authors: Rathore Seema S. , Jenita J. Josephine Leno , D Manjula 
Published: November 2024

ABSTRACTCyclin‐dependent kinase 4 and 6 (CDK4/6) inhibitors are utilized as a remedy for advanced hormone receptor‐positive, HER2‐negative breast cancer (BC). By targeting CDK4/6, these drugs inhibit the phosphorylation of the retinoblastoma protein during the early G1 phase, leading to cell cycle arrest, disruption of deoxyribonucleic acid replication, and effective suppression of cancer cell proliferation. Recently, there has been an increasing focus on the advancement of analytical techniques for the determination of CDK4/6 inhibitors. These analytical methods are essential in the enhancement and refinement of CDK4/6 inhibitors for the management of advanced BC. This review provides a comprehensive overview of the various analytical techniques used for the analysis of palbociclib, ribociclib, and abemaciclib, along with their metabolites in biological fluids (such as blood and urine samples), commercial products, Ringer's solution, and simultaneous estimation. High‐performance liquid chromatography and liquid chromatography‐mass spectrometry are the primary analytical techniques commonly employed for the analysis of CDK4/6 inhibitors. This analytical review also provides insights into pharmacokinetics and stability studies, as well as the use of analytical techniques in detecting impurities and determining the structure of degraded products.
On Alleviating Spatial Confounding Issues With the Bayesian Lasso
Authors: Briz‐Redón Álvaro 
Published: November 2024

ABSTRACTSpatial confounding usually refers to an issue that often arises in the context of fitting a spatial linear mixed model: the collinearity between fixed and spatial random effects. As a consequence, this can lead to erroneous covariate–response associations, with the resulting scientific or social impact. Different methods have been proposed in the literature to alleviate spatial confounding. However, recent research suggests that restricted spatial regression, the most commonly used, produces anticonservative confidence intervals and hence performs worse than the original spatial linear mixed model in terms of coverage. In this paper, we propose the use of the Bayesian Lasso for alleviating spatial confounding. Specifically, we propose a joint model that includes the fixed‐effects modeling process and the spatial linear mixed modeling framework subject to a penalty on the effect of the covariate on the response. The model proposed is tested for the Slovenia dataset, which is typically used for the study of this topic. We show that the model yields a covariate–response association relatively coherent with that inferred from the fixed‐effects model, while allowing for the inclusion of spatial random effects, thus performing better in terms of goodness‐of‐fit and spatial smoothing than the fixed‐effects model. A second application is also shown for a COVID‐19 dataset corresponding to the Greater London area, which provides similar insights about the proposed methodology.