bluuebunny/crossref_metadata_2025_split huggingface dataset:
LiF‐Rich Alloy‐Doped SEI Enabling Ultra‐Stable and High‐Rate Li Metal Anode
Authors: Zhuang Huifeng , Xiao Hong , Zhang Tengfei , Zhang Fanchao , Han Pinyu , Xu Mengyuan , Dai Wenjing , Jiao Junrong , Jiang Lei , Gao Qiuming 
Published: May 2024

AbstractLi metal is regarded as the “Holy Grail” in the next generation of anode materials due to its high theoretical capacity and low redox potential. However, sluggish Li ions interfacial transport kinetics and uncontrollable Li dendrites growth limit practical application of the energy storage system in high‐power device. Herein, separators are modified by the addition of a coating, which spontaneously grafts onto the Li anode interface for in situ lithiation. The resultant alloy possessing of strong electron‐donating property promotes the decomposition of lithium bistrifluoromethane sulfonimide in the electrolyte to form a LiF‐rich alloy‐doped solid electrolyte interface (SEI) layer. High ionic alloy solid solution diffusivity and electric field dispersion modulation accelerate Li ions transport and uniform stripping/plating, resulting in a high‐power dendrite‐free Li metal anode interface. Surprisingly, the formulated SEI layer achieves an ultra‐long cycle life of over 8000 h (20,000 cycles) for symmetric cells at a current density of 10 mA cm−2. It also ensures that the NCM(811)//PP@Au//Li full cell at ultra‐high currents (40 C) completes the charging/discharging process in only 68 s to provide high capacity of 151 mAh g−1. The results confirm that this scalable strategy has great development potential in realizing high power dendrite‐free Li metal anode.
Interfacial Domino Effect Triggered by β‐Alanine Cations Realized Highly Reversible Zinc‐Metal Anodes
Authors: Guo Gaozhi , Ji Chenchen , Lin Jiadong , Wu Tianlong , Luo Yulu , Sun Chaorui , Li Mengjun , Mi Hongyu , Sun Lixian , Seifert Hans Jürgen 
Published: May 2024

AbstractRealizing durative dense, dendrite‐free, and no by‐product deposition configuration on Zn anodes is crucial to solving the short circuit and premature failure of batteries, which is simultaneously determined by the Zn interface chemistry, electro‐reduction kinetics, mass transfer process, and their interaction. Herein, this work unmasks a domino effect of the β‐alanine cations (Ala+) within the hydrogel matrix, which effectively triggers the subsequent electrostatic shielding and beneficial knock‐on effects via the specifical adsorption earliest event on the Zn anode surface. The electrostatic shielding effect regulates the crystallographic energetic preference of Zn deposits and retards fast electro‐reduction kinetics, thereby steering stacked stockier block morphology and realizing crystallographic optimization. Meanwhile, the mass transfer rate of Zn2+ ions was accelerated via the SO42− anion immobilized caused by Ala+ in bulk electrolyte, finally bringing the balance between electroreduction kinetics and mass transfer process, which enables dendrite‐free Zn deposition behavior. Concomitantly, the interfacial adsorbed Ala+ cations facilitate the electrochemical reduction of interfacial SO42− anions to form the inorganic‐organic hybrid solid electrolyte interphase layer. The above domino effects immensely improve the utilization efficiency of Zn anodes and long‐term stability, as demonstrated by the 12 times longer life of Zn||Zn cells (3650 h) and ultrahigh Coulombic efficiency (99.4 %).
Do environmental and climate scores for financial institutions reflect lending and underwriting activity? A case study of global banks
Authors: Cregan Charlie , Kelly J. Andrew , Clinch J. Peter 
Published: May 2024

AbstractAchieving international climate targets may require more than $8 trillion in annual investments to 2030. We investigate the extent to which third‐party environmental scores for banks reflect lending and underwriting in fossil‐fuel and low‐carbon industries, and how ratings are influenced by outward signals of commitment to climate action. We provide empirical evidence on the performance of leading Environmental, Social and Governance (ESG) ratings providers and offer actionable guidance as to how ESG ratings may be improved in this context. We find that banks' environmental scores are most strongly influenced by signals of future intention regarding climate action, rather than by prior and current lending and underwriting behaviour. Our analysis highlights the need for rating providers, when constructing environmental scores for banks, to place more weight on key capital allocation decisions, and less on future intentions. We recommend that banks disclose breakdowns of their financing activities in key carbon‐intensive and low‐carbon industries.
The Potential of Self‐Activating Au(I) Complexes in Gold Catalysis
Authors: Pérez‐Sánchez Juan Carlos , Herrera Raquel P. , Gimeno M. Concepción 
Published: May 2024

AbstractGold catalysis has emerged as a groundbreaking field in synthetic chemistry, revolutionizing numerous organic transformations. Despite the significant achieved advancements, the mechanistic understanding behind many gold‐catalyzed reactions remains elusive. This Concept article covers the so‐called “self‐activating” Au(I) complexes, sorting out their pivotal role in gold catalysis. We comment on how Au(I) complexes can undergo self‐activation, triggering diverse catalytic transformations without the need for external additives. The most important examples reported so far that underlie the catalytic activity of these species are discussed. This intrinsic reactivity represents a paradigm shift in gold catalysis, offering new avenues for the design of efficient and sustainable catalytic systems. Furthermore, we explore the factors influencing the stability, reactivity, and selectivity of these Au(I) complexes, providing insights into their synthetic utility and potential applications. This area of research not only advances our fundamental understanding of gold catalysis but also paves the way for the development of novel catalytic strategies with broad implications in organic synthesis and the chemical industry.
Iron‐Catalyzed Oxidative Deconstruction of Polyethylene Terephthalate to Terephthalic Acid under O2
Authors: Wang Sichang , Wang Liting , Xue Ting , Zhang Guoxiang , Ke Congyu , Zeng Rong 
Published: May 2024

Comprehensive SummaryIt is urgent to recycle polyethylene terephthalate (PET) effectively, since it is the most consumed synthetic polyester and its improper disposal has caused significant environmental pollution. The existing chemical recycling methods highly rely on the nucleophilic substitutions and hydrogenative depolymerizations, which typically require the use of excess of nucleophiles, excess strong acids or bases, expensive metal catalysts, and explosive gas atmosphere. Here, we demonstrate a mild and efficient protocol for oxidative depolymerization of PET to terephthalic acid using only an O2 balloon. Terephthalic acid can be recycled from PET‐containing materials including a series of plastic products in daily life. The employing of relatively low loading of iron complex, the most earth‐abundant transition metal, as the catalyst and the preliminary results on the large‐scale reaction using 38 g of PET waste demonstrate the practical feasibility of this degradation method. This method can be also applicable for selective degradation of PET from mixed plastics. This work represents a rare example of a selective oxidative depolymerization and demonstrates the great potentials of such a concept in polyester recycling.
Design, Synthesis and Biological Evaluation of C3‐Indolyl/(3‐chloro‐indolyl)‐C4‐aryl/heteroaryl‐azetidin‐2‐ones
Authors: Yadav Pooja , Fatimah Nasreen , Sahoo S. C. , Kumari Sumeeta , Berry Shiwani , Reenu  , Kumar Pinnaka Anil , Bhalla Aman 
Published: May 2024

AbstractHerein, trans‐ and cis‐azetidin‐2‐ones 3–6 were strategically synthesized, capitalizing on the bioactivity of azetidin‐2‐ones and indole pharmacophore, followed by a comprehensive characterization using a diverse array of spectroscopic techniques. The sixteen azetidin‐2‐ones were examined for antimicrobial activities against both Gram‐negative (P. aeruginosa, E. coli, A. baumannii) and Gram‐positive bacteria (S. aureus, E. faecium, B. cereus), as well as against C. albicans and C. tropicalis fungal strains. The highly potent compounds (5 a, 6 b, 6 d) demonstrated maximum inhibition against all multidrug‐resistant strains, with minimum inhibitory concentrations ranging from 0.97–3.9 μg/mL, surpassing the potency of standard ampicillin (MIC: 3.12–50 μg/mL). Moreover, 6 b and 6 d exhibited significant inhibitory effects on C. albicans (MIC: 0.97 μg/mL), comparable to fluconazole. The presence of C3‐(3‐chloro‐indolyl) scaffold, combined with diverse electronic effects at N1/C4‐centers, particularly the inclusion of thiophen‐2‐yl motif, greatly influenced the activity of target compounds. Assessment of 4 d, 4 i–k and 6 d on THLE‐2 cell lines revealed their preferential safety. Molecular docking studies revealed seven compounds with active dual targeting of DNA GyrB and PBP2a proteins, demonstrating a potent broad‐spectrum antibacterial effect. In silico ADME analysis affirms positive drug‐likeness and favorable pharmacokinetic characteristics of indole‐derived hybrids, indicating a promising potential for addressing challenges in evolving multidrug resistance.
SharpEdge: A QoS‐driven task scheduling scheme with blockchain in mobile edge computing
Authors: Gu Ji , Liu Yushi , Xu Xiaolong 
Published: May 2024

SummaryMobile edge computing (MEC) promotes the development and popularity of Internet of Things (IoT) devices with higher connectivity and ultra‐low latency through network topology optimization and real‐time data analysis. With the exponential growth of data traffic generated by IoT devices, it is essential to prevent edge servers (ESs) from overloading through efficient task scheduling to ensure the quality of service (QoS). However, the lack of trust and incentive mechanisms between ESs deployed and managed by different infrastructure providers makes it challenging to perform peer‐offloaded tasks. Moreover, to meet the low‐latency requirement of many IoT applications, the execution efficiency of the task scheduling scheme in MEC needs to be improved. To address the above challenges systematically, a QoS‐driven task scheduling scheme with blockchain in MEC named SharpEdge is proposed. In SharpEdge, ESs publish tasks with rewards and then select the most reliable executors through a historical performance‐based reputation mechanism to perform peer offloading. The performance of the executor will be recorded into the blockchain after the task is completed. In addition, a concurrent consensus mechanism using sharding technology is designed, improving task scheduling efficiency. We implement SharpEdge based on Hyperledger Fabric and verify its performance in a simulated MEC environment.
Disturbance theory for ecosystem ecologists: A primer
Authors: Gough Christopher M. , Buma Brian , Jentsch Anke , Mathes Kayla C. , Fahey Robert T. 
Published: May 2024

AbstractUnderstanding what regulates ecosystem functional responses to disturbance is essential in this era of global change. However, many pioneering and still influential disturbance‐related theorie proposed by ecosystem ecologists were developed prior to rapid global change, and before tools and metrics were available to test them. In light of new knowledge and conceptual advances across biological disciplines, we present four disturbance ecology concepts that are particularly relevant to ecosystem ecologists new to the field: (a) the directionality of ecosystem functional response to disturbance; (b) functional thresholds; (c) disturbance–succession interactions; and (d) diversity‐functional stability relationships. We discuss how knowledge, theory, and terminology developed by several biological disciplines, when integrated, can enhance how ecosystem ecologists analyze and interpret functional responses to disturbance. For example, when interpreting thresholds and disturbance–succession interactions, ecosystem ecologists should consider concurrent biotic regime change, non‐linearity, and multiple response pathways, typically the theoretical and analytical domain of population and community ecologists. Similarly, the interpretation of ecosystem functional responses to disturbance requires analytical approaches that recognize disturbance can promote, inhibit, or fundamentally change ecosystem functions. We suggest that truly integrative approaches and knowledge are essential to advancing ecosystem functional responses to disturbance.
Cognitive impairment in young and middle‐aged patients with acute heart failure
Authors: Miao Fengyu , Tian Aoxi , Wang Bin , Li Jing 
Published: May 2024

AbstractAimsThis study aimed to investigate the prevalence, risk factors and prognostic implications of cognitive impairment in young and middle‐aged patients with acute heart failure (HF).MethodsIn a prospective cohort of patients with acute HF, we assessed cognitive function by the Mini‐Cog, predictors of the cognitive impairment and its associations with 30 day and 1 year cardiovascular death or HF rehospitalization among young and middle‐aged patients (<65 years old).ResultsAmong 1958 young and middle‐aged patients, the prevalence of cognitive impairment was 19.6%. Predictors of cognitive impairment included older age, females, lower education levels and prior strokes. Compared with patients having normal cognitive function, cognitive impairment was associated with a higher risk of 30 day cardiovascular death or HF rehospitalization [hazard ratio (HR), 1.52, 95% confidence interval (CI), 1.07–2.17, P = 0.02], but not for 1 year cardiovascular death or HF rehospitalization (HR, 1.06, 95% CI, 0.87–1.30, P = 0.55).ConclusionsCognitive impairment is present in a notable proportion of young and middle‐aged patients with acute HF and is associated with an increased risk of short‐term adverse outcomes. Strategies for screening and intervention for cognitive impairment at a younger age are necessary, particularly for those at high risk.
Gastrointestinal pain: A systematic review of temporal summation of pain paradigms and outcomes
Authors: Huisman Danielle , Mansfield Michael , Cummins Tatum M. , Moss‐Morris Rona , McMahon Stephen B. , Bannister Kirsty 
Published: May 2024

AbstractBackground and ObjectiveSince targeted treatment for gastrointestinal pain is elusive, identifying the mechanistic underpinning of this pain type is important. Facilitation of spinal neuronal responses underpins certain pain types, and the psychophysical temporal summation of pain (TSP) paradigm provides a proxy measure of spinal facilitatory processes. Our aim was to systematically review whether facilitated TSP is a feature of gastrointestinal pain in patients with, or pain‐free people experiencing experimentally induced, gastrointestinal pain.Databases and Data TreatmentEMBASE, MEDLINE, PsychInfo, CINAHL, and Web of Science were systematically searched, from inception to July 2023, for human studies reporting TSP paradigm outcomes in the context of gastrointestinal pain. The Appraisal tool for Cross‐Sectional studies was used for quality assessment and applied independently by two researchers.ResultsFifteen papers consisting of cross‐sectional (n = 6), case–control (n = 8), and retrospective cohort (n = 1) studies, were included. Thirteen studies investigated TSP in people with gastrointestinal pain with (n = 5) or without (n = 8) defined pathology. Two studies evoked TSP by repetitive gut stimulation in people undergoing abdominal medical procedures. Preliminary evidence showed that facilitated TSP correlated with the presence of functional gastrointestinal pain in women, and those with a history of trauma. No effect was observed in people with inflammatory bowel disease, although it was often unclear if they experienced pain.ConclusionsIt is not possible to conclude whether facilitated TSP is a feature of gastrointestinal pain. We recommend that subgroup findings are corroborated and that TSP paradigms are standardized in order that direct comparisons between studies may be made.Significance StatementEvidence indicated that pain facilitatory processes, as evidenced by a facilitated TSP outcome, contribute to functional gastrointestinal pain in women and those with a history of trauma. However, heterogeneity of study populations and paradigms precluded statistical synthesis and findings would need be corroborated. Studies exploring facilitatory processes in people with inflammatory bowel diseases did not report significant results, but pain is not a given in these conditions and, conversely, may be driven by peripheral inflammation during active disease. This should be taken in consideration in future explorations.Registration ReviewPROSPERO CRD42022341845.
Enhancing outlier detection in air quality index data using a stacked machine learning model
Authors: Diallo Abdoul Aziz , Nderu Lawrence , Malenje Bonface Miya , Kikuvi Gideon Mutie 
Published: May 2024

AbstractThe air quality index (AQI) is a commonly employed metric for evaluating air quality across diverse locations and temporal spans. Similar to other environmental datasets, AQI data can exhibit outliers data points markedly divergent from the norm, signifying instances of exceptionally favorable or adverse air quality. This becomes crucial in identifying and comprehending severe pollution episodes with far‐reaching environmental and public health implications. This study utilizes air quality data from January 1, 2014, to January 31, 2021, collected at daily intervals in Shanghai City, China, as the experimental dataset. The dataset includes daily AQI measurements, along with six pollutant concentrations: particulate matter (PM2.5 and PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO). Each pollutant's concentration is measured in micrograms per cubic meter (g/m). The dataset is then preprocessed by cleaning and normalizing it before using K‐means clustering to discover different patterns. A stacked ensemble machine learning model that incorporates K‐means clustering, random forest (RF) and gradient boosting classifier (GBC) is developed and compared to decision tree, support vector machine, K‐nearest neighbor and Naive Bayes algorithms to evaluate its performance in identifying outliers using accuracy, precision, recall, and F1‐score. The stacked model outperformed all other established models based on the accuracy, precision, recall, and F1‐score of 0.99, 0.99, 0.97, and 0.99, respectively.
Sovereign bonds' risk‐based heterogeneity
Authors: Georgoutsos Dimitris A. , Migiakis Petros M. 
Published: May 2024

AbstractAre sovereign risk premia subject to heterogeneous effects from their drivers, associated with the risk class each sovereign bond belongs to? In the paper at hand, effects on sovereign bond risk premia stemming from macroeconomic, fiscal, and volatility factors, are examined by considering the classification of sovereign riskiness. Panel data estimation techniques are used, for 30 countries, with data in quarterly frequency for the period 2001Q1 to 2019Q4. Sovereign spreads are found to be subject to heterogeneous effects associated with their credit ratings; spreads on sovereign bonds considered low‐risk increase with higher growth rates and inflation, while spreads on highly risky bonds decrease with higher growth rates and are more sensitive to idiosyncratic and global volatility. Primary fiscal surpluses indeed lower spreads but cannot counterbalance the effects of volatility episodes and the prospects for low growth. Our results provide support for countercyclical fiscal policies, suggesting that spreads can be expected to be reduced by primary surpluses, under the condition that the economy expands and market volatility is low. Our main findings are robust to various alternative setups, samples, and control variables such as central banks' asset purchases.
Growth potential of machine learning in credit risk predicting of farmers in the industry 4.0 era
Authors: Chai Nana , Abedin Mohammad Zoynul , Wang Xiaoling , Shi Baofeng 
Published: May 2024

AbstractThis paper aims to design a model framework for farmer credit risk assessment based on machine learning. It reduces the degree of credit risk misjudgement caused by the weak correlation between evaluation indicators and default status and imbalanced data. Based on the empirical analysis of 8624 farmers' data from a commercial bank in China, the average rank of the OPSO‐GINI‐FS model designed from the feature dimension is 1.29, which is higher than that of the OPSO‐GINI‐IS model designed from the indicator dimension (1.57). This means that our model has a higher default risk identification ability than the traditional one. And the META‐SAMPLER method of processing imbalanced data is also promising. Moreover, we found the machine learning designed in this paper has a higher ability to identify farmers' loan default than the traditional econometric methods. These findings establish the potential of machine learning in credit risk identification from a micro perspective.
Identity verification of monoclonal antibodies by triple injection capillary zone electrophoresis
Authors: Carlsson Johan A. , Löfgren Maria , Amini Ahmad 
Published: May 2024

This paper presents an approach based on triple injection capillary zone electrophoresis for identification of monoclonal antibodies. The analyte to be identified is injected between two zones of a known reference. The distances between the reference zones (plug I and III) and the target zone (plug II) are adjusted by partial electrophoresis of the first and second injection plugs. The full migration time of the target analyte is calculated from the observed migration time by considering the migration times of the reference in the first and third injection plugs. The relative migration time, that is, the ratio between the full migration time of the analyte and the migration time of the reference in the third injection plug provides the basis for identification. Here, eight monoclonal antibodies, including a pair of biosimilars, were used interchangeably as both analyte and reference to investigate potential of the method. The relative migration time for a preliminary positive identification were found to vary between 0.994 and 1.006 (1.000 ± 0.006, p = 95%). Beside the relative migration time, isoform distribution, peak profiles, and early migrating peaks, originating from components in the pharmaceutical formulations, were successfully used to verify the identity of all tested monoclonal antibodies.