bluuebunny/crossref_metadata_2025_split huggingface dataset:
Glassy Thermal Transport Triggers Ultra‐High Thermoelectric Performance in GeTe
Authors: Sarkar Debattam , Das Subarna , Taneja Vaishali , Samanta Manisha , Jagadish Koushik , Das Animesh , Bhakar Monika , Matukumilli Prasad V. D. , Perumal Suresh , Sheet Goutam , Sanyal Dirtha , Pal Koushik , Ravishankar N. , Waghmare Umesh V. , Biswas Kanishka 
Published: February 2025

AbstractThe consequences of broken long‐range atomic arrangement in glasses or amorphous solids are reflected in the temperature dependence of lattice thermal conductivity (κlat). However, the appearance of glassy ultralow κlat in a crystalline solid with high electrical transport like metal is unusual but can have a remarkable impact on the thermoelectric performance of a material. Here, an ultra‐high thermoelectric performance is demonstrated with a maximum figure of merit, zT ≈ 2.7 (≈2.92 with Dulong–Petit heat capacity) via achieving glassy thermal transport along with significant electrical conductivity in ball milled BiSe, Pb co‐doped polycrystalline Ge1.03Te followed by spark plasma sintering. The glassy thermal transport results from the inhomogeneous ferroelectric instability developed due to local polar distortions near the dopant sites, which interacts with soft polar optical modes via strain fluctuations. Resulting structural degeneracy and associated soft vibrations sink heat effectively from acoustic phonons, which along with various nanoscale defects, confine the phonon mean free path (MFP) close to the interatomic distance, rendering the thermal transport glassy. However, the material still maintains a high electrical conductivity at ambient condition due to much longer MFP of the charge carriers. A promising output power density of ≈0.8 W cm−2 for ΔT ≈441 K in double‐leg thermoelectric device demonstrate the potential of this material for mid‐temperature thermoelectric applications.
A PD‐L1 siRNA‐Loaded Boron Nanoparticle for Targeted Cancer Radiotherapy and Immunotherapy
Authors: Deng Shaohui , Hu Lijun , Chen Guo , Ye Jujian , Xiao Zecong , Guan Tianwang , Guo Shuai , Xia Wei , Cheng Du , Wan Xiaochun , Cheng Ke , Ou Caiwen 
Published: February 2025

AbstractAlthough the combination of radiotherapy and immunotherapy is regarded as a promising clinical treatment strategy, numerous clinical trials have failed to demonstrate synergistic effects. One of the key reasons is that conventional radiotherapies inevitably damage intratumoral effector immune cells. Boron Neutron Capture Therapy (BNCT) is a precise radiotherapy that selectively kills tumor cells while sparing adjacent normal cells, by utilizing 10B agents and neutron irradiation. Therefore, combinational BNCT‐immunotherapy holds promise for achieving more effective synergistic effects. Here it develops a 10B‐containing polymer that self‐assembled with PD‐L1 siRNA to form 10B/siPD‐L1 nanoparticles for combinational BNCT‐immunotherapy. Unlike antibodies, PD‐L1 siRNA can inhibit intracellular PD‐L1 upregulated by BNCT, activating T‐cell immunity while also suppressing DNA repair. This can enhance BNCT‐induced DNA damage, promoting immunogenic cell death (ICD) and further amplifying the antitumor immune effect. The results demonstrated that BNCT using 10B/siPD‐L1 nanoparticles precisely killed tumor cells while sparing adjacent T cells and induced a potent antitumor immune response, inhibiting distal and metastatic tumors.
Bio‐Inspired, Miniaturized Magnetic Heart Valve System for Superior Performance Cardiovascular Simulator
Authors: Yoo Jeongmin , Chung Gooyoon , Park Yoonseok 
Published: February 2025

AbstractThe demand for accurate vascular simulators is increasing to facilitate effective clinical studies on cardiovascular diseases. The research presents the miniaturized design and precise programable regulation of an artificial magnetic heart valve inspired by the human aortic valve, demonstrating the diverse types of pulsating waves. The heart valve is constructed using an elastomeric silicone composite embedded with neodymium magnetic micro‐particles. This valve system responds rapidly to changes in magnetic fields controlled by miniaturized electromagnets, enabling precise regulation of fluid pressure and flow rate. This allows for the generation of various pressure waveforms and accurately replicates diverse blood pressure changes with a compact design. The design, working mechanism, fabrication process, and optimization of the magnetically controlled biomimetic heart valve are discussed and its performance as a cardiovascular simulator for human and animal models is evaluated. This artificial valve system has the potential to be utilized in humanoid robots to generate heart‐like pressure, thereby paving the way for replicating human physiological characteristics. This research promises significant advancements in cardiovascular clinical trials and biomedical research along with the development of humanoid robots and biomimetic mechanical systems.
Advances and Outlooks for Carbon Nanotube‐Based Thermoelectric Materials and Devices
Authors: Zhou Shanshan , Shi Xiao‐Lei , Li Lan , Liu Qian , Hu Boxuan , Chen Wenyi , Zhang Chenyang , Liu Qingfeng , Chen Zhi‐Gang 
Published: February 2025

AbstractThe unique structure of carbon nanotubes (CNTs) endows them with exceptional electrical and mechanical properties, along with a high surface area, making them highly beneficial for use as flexible, high‐performing thermoelectric materials. As a result, the application of CNTs in the thermoelectric field has become increasingly widespread. Considering the rapid advancements in this field, this review offers a timely overview of the most recent progress on CNT‐based thermoelectric materials and devices over the past five years. This review begins by introducing the fundamental concepts and thermoelectric mechanisms of CNT‐based thermoelectric materials. Then new strategies are explored to enhance their thermoelectric performance, focusing on doping and composites, while emphasizing the importance of CNT stability as a key research area. Additionally, the latest design concepts and expanded application scenarios for flexible and wearable CNTs‐based thermoelectric devices are summarized. Finally, the current challenges are addressed and future directions for the development of CNT‐based thermoelectric materials and devices are discussed.
Fabrication of Ultra‐Thick Masks for X‐Ray Phase Contrast Imaging at Higher Energy
Authors: Rossi Alessandro , Buchanan Ian , Astolfo Alberto , Michalska Martyna , Briglin Daniel , Charman Anton , Josell Daniel , Olivo Alessandro , Papakonstantinou Ioannis 
Published: February 2025

AbstractX‐ray phase contrast imaging (XPCI) provides higher sensitivity to contrast between low absorbing objects that can be invisible to conventional attenuation‐based X‐ray imaging. XPCI's main application is so far focused on medical areas at relatively low energies (< 100 keV). The translation to higher energy for industrial applications, where energies above 150 keV are often needed, is hindered by the lack of masks/gratings with sufficiently thick gold septa. Fabricating such structures with apertures of tens of micrometers becomes difficult at depths greater than a few hundreds of micrometers due to aspect ratio‐dependent effects such as anisotropic etching, and preferential gold (Au) deposition at the top of the apertures. In this work, these difficulties are overcome by Deep Reactive Ion Etching optimized by a stepped parameters approach and bismuth‐mediated superconformal filling of Au, ultimately resulting in 500 µm deep silicon masks filled with Au at bulk density. The obtained masks, tested in an Edge Illumination XPCI system with a conventional source and a photon‐counting detector, show good agreement with simulations at different energy thresholds. They also demonstrate a higher phase sensitivity for highly absorbing objects when compared to lower aspect ratio masks, proving their potential for industrial non‐destructive testing.
Colored Designs for Radiative Cooling: Progress and Perspectives
Authors: Yin Yingying , Zhang Mei , Wang Zuankai 
Published: February 2025

AbstractRadiative cooling, a passive and sustainable technology that dissipates heat without energy consumption, holds great promise for efficient thermal management across various applications, including buildings, electronics, cold‐chain transportation, and personal thermal comfort. Despite extensive efforts, most current radiative coolers predominantly feature white or silver surfaces due to their high solar reflectivity requirement for enhanced daytime cooling performance, which restricts their aesthetic appeal and versatility in diverse settings. To address this limitation, a new class of colored radiative coolers (CRCs) emerges as a significant breakthrough to balance color display with cooling efficiency. This advancement is enabled by a fascinating design philosophy that emphasizes selective narrow‐band absorption in the visible light spectrum to show the desired color while maintaining a high reflectivity in other solar wavelengths to reduce thermal absorption. In this Perspective paper, a brief introduction to the fundamental principles of colored radiative cooling is first provided. Then, recent progress in CRCs is reviewed, focusing on colored designs utilizing pigments, photonic structures, and field stimulations. Finally, the challenges and future prospects in CRCs are outlined, advocating for their further advancement and widespread integration into practical applications.
A Practical High‐Performance Lithium‐Ion Capacitor Fabricated with Dual Graphene‐Based Electrode Materials
Authors: Hu Tao , Zhang Xiong , Li Chen , Zhao Shasha , An Yabin , Zhang Xiaohu , Sun Xianzhong , Wang Kai , Ma Yanwei 
Published: February 2025

AbstractLithium‐ion capacitors (LICs) hold great promise by merging the benefits of lithium‐ion batteries and supercapacitors. However, their performance is frequently constrained by a disparity in the kinetic properties of the cathode and anode. This study introduces a dual graphene‐based approach aimed at improving the efficiency and functionality of LICs and demonstrates the successful large‐scale production of graphene (SHSG) using a self‐propagating high‐temperature synthesis method. In the cathode, SHSG forms a continuous graphene network, reducing interfacial resistance, enhancing conductivity and achieving a capacity of 85.9 mAh g−1. In the anode, SHSG improves ion diffusion and reaction interfaces, increasing capacity from 247.9 to 286.6 mAh g−1. A full LIC cell assembled with 10% SHSG in both electrodes demonstrates a peak energy density of 106.3 Wh kg−1 and retains 33 Wh kg−1 at 4.4 kW kg−1, which is calculated based on the total mass of the electrodes. Additionally, a 1100 F LIC pouch cell is developed, showcasing its potential for practical energy storage. This work underscores the transformative role of graphene in optimizing LICs and advancing energy storage technologies.
Rolling Bearing Fault Diagnosis Based on 2D CNN and Hybrid Kernel Fuzzy SVM
Authors: Zhang Qingbao , Ju Zhe 
Published: February 2025

AbstractAddressing the issues of poor anti‐noise performance and insufficient mining of fault information in vibration signals in traditional fault diagnosis methods, a network structure algorithm (2DCNN‐HKFSVM) based on the 2D convolutional neural network (CNN) and the hybrid kernel fuzzy support vector machine (HKFSVM) is proposed. First, the original bearing vibration signals are converted into 2D grayscale images; then, these grayscale images are used as inputs to the 2D convolutional neural network for feature extraction and dimensionality reduction; finally, the obtained feature vectors are passed to the hybrid kernel fuzzy support vector machine for fault detection. Compared with the support vector machine (SVM), the fuzzy support vector machine (FSVM) assigns different weights to different bearing fault samples through the fuzzy membership function, thereby reducing the impact of noise on the classification results. Furthermore, the hybrid kernel function combined according to Mercer's theorem enables the FSVM to take both global and local fitting into account, further improving the classification performance of the FSVM. Compared with some existing fault diagnosis models that combine CNN with machine learning algorithms such as SVM and random forests (RF), 2DCNN‐HKFSVM exhibits better generalization ability and anti‐noise performance.
Numerical Analysis and Artificial Neural Networks for Solving Nonlinear Tuberculosis Model in SEITR Framework
Authors: Jeeva N. , Dharmalingam K. M. 
Published: February 2025

AbstractThis study investigates an epidemiological model of tuberculosis dynamics by classifying the total population into five distinct compartments: susceptible, exposed, infected, treated, and recovered. To solve the system of nonlinear differential equations and obtain approximate solutions for the tuberculosis model, three analytical methods are utilized: the transcendental‐exponential type proposed method (PNM), the Homotopy perturbation method (HPM), and the higher‐order inverse polynomial method (HOIPM). Additionally, the study examines the stochastic performance of artificial neural networks trained using the Levenberg–Marquardt algorithm (ANNs‐LMB) to offer a comprehensive evaluation of the tuberculosis model. The predictions generated by ANNs‐LMB provide valuable benefits for researchers, significantly improving their understanding of infectious tuberculosis dynamics. Furthermore, error estimations demonstrate that the PNM, HOIPM, and ANNs‐LMB methods are highly effective in generating accurate solutions, closely matching those obtained from the Runge–Kutta solver, and surpassing the performance of HPM. These methods exhibit strong reliability and efficiency, making them innovative tools for addressing tuberculosis models and simulating epidemiological challenges. Moreover, the analysis of key parameters, including contact rate, infection rate, tuberculosis‐related mortality rate, reinfection rate, and treatment rate, provides crucial insights into the model's behavior and dynamics, paving the way for future research and effective intervention strategies.
Numerical Approximation of the Fractional Model of Atmospheric Dynamics of CO2 Using the Gegenbauer Wavelet Collocation Method
Authors: G Manohara , S Kumbinarasaiah 
Published: February 2025

AbstractThis work provides an understanding of the fractional order nonlinear mathematical model that describes the dynamic variation of carbon dioxide ( gas atmospheric concentration. The impact of changes in the human population and forest biomass on the dynamics of gas concentration in the atmosphere is also highlighted in this work. It investigates the model's solution by applying an effective wavelet method known as the Gegenbauer wavelet collocation method (GWCM). The considered model is a nonlinear coupled system of fractional ordinary differential equations (SFODEs). The operational matrices of integration are constructed using the Gegenbauer wavelets. Processing is accelerated by utilizing the properties of the Gegenbauer wavelet expansions and the operational matrix of integration to convert nonlinear SFODEs into a system of algebraic equations. The Newton‐iterative strategy is used to solve this system of algebraic equations to determine the unknown coefficients and to arrive at an approximate solution. Numerical illustration demonstrates the method's effectiveness and accuracy. In addition to demonstrating the effectiveness of the applied strategy, the results show that it is appropriate for high approximations of the atmospheric gas concentration fractional model solution. All the numerical computations are carried out with the help of Mathematica software.
Stable Cycling of Sodium All‐Solid‐State Batteries with High‐Capacity Cathode Presodiation
Authors: Tang Wei , Xu Dapeng , Wu Junlin , Lee Dong Ju , Fuqua Alexander , Li Feng , Jeon Yuju , Bian Wenjuan , Chen Zheng 
Published: February 2025

AbstractSodium all‐solid‐state batteries (NaSSBs) with an alloy‐type anode (e.g., Sn and Sb) offer superior capacity and energy density compared to hard carbon anode. However, the irreversible loss of Na+ at the alloy anode during the initial cycle results in diminished capacity and stability, impairing full‐cell performance. This study presents an easy‐to‐implement cathode presodiation strategy by employing a Na‐rich material to address these challenges. Leveraging the high theoretical capacity and suitable voltage window, Na2S is chosen as the Na donor, which is activated by creating a mixed electron‐ion conducting network, delivering a high capacity of 511.7 mAh g−1. By adding a small amount (i.e., 3 wt.%) of Na2S to the cathode composite, a NaCrO2 || Sn full cell demonstrated capacity improvement from 90.8 to 118.2 mAh g−1 (based on cathode mass). The capacity‐balanced full cell can thus cycle to more than 300 times with >90% capacity retention. This work provides a practical solution to enhance the full‐cell performance and advance the transformation from half‐cell to full‐cell applications of NaSSBs.
Multispectral red‐edge indices accurately estimate nitrogen content in winter cereal cover crops
Authors: Thieme A. , Jennewein J. , Hively W. D. , Lamb B. T. , Whitcraft A. K. , Mirsky S. B. , Reberg‐Horton S. C. , Justice C. 
Published: February 2025

AbstractWinter cover crops reduce erosion and nutrient runoff from agricultural systems. Although cereal cover crops can decrease field nitrate leaching by 50%–95%, the magnitude of this reduction varies within and between fields, making it challenging to monitor the impact of cover crops on nitrate leaching at large spatial extents. Satellite remote sensing using red‐edge bands has been shown to effectively estimate crop nitrogen (N) content (kg ha−1) in later growth‐stage crops with a closed canopy. In this study, we evaluated 15 spectral indices derived from Sentinel‐2 imagery to estimate N concentration (%) and content (kg ha−1) of cereal cover crops, using 1627 destructive samples collected from 2018 to 2023 in Maryland. Observed N content ranged from 0.1 to 214.7 kg ha−1, while N concentration ranged from 0.6% to 5.5%. The 15 indices considered were poor predictors of N concentration (adj. R2 = 0.089, root mean squared error [RMSE] = 0.802%), but were more successful at measuring N content (biomass × N concentration). Delta red‐edge (ΔRE) was the best predictor of N content (adj. R2 = 0.748, RMSE = 13.10 kg ha−1 from cross‐validation with 80% train and 20% test splits iterated 100 times) using samples with imagery collected within ±4 days of destructive sampling (n = 1110). Our findings indicate that longer red‐edge wavelengths (783 and 740 nm) are more suited for estimating N content in cereal cover crops compared to shorter red‐edge wavelengths, which have been shown to be more sensitive to biomass. Leave‐one‐year‐out cross‐validation demonstrated that the relationship between ΔRE and N content was robust across all four cover crop sampling years included in the study (adj. R2 = 0.700–0.769, RMSE = 10.70–15.40 kg ha−1). Regression model performance improved with the addition of multiple predictors, including biomass (estimated from Normalized Difference Vegetation Index), weather variables (adj. R2 = 0.765, RMSE = 12.37 kg ha−1), management variables (species, season, adj. R2 = 0.772, and RMSE = 12.13 kg ha−1), and biophysical variables (height, fractional ground cover, adj. R2 = 0.818, and RMSE = 10.29 kg ha−1). These findings demonstrate the feasibility of quantifying N content in cereal cover crops using a red‐edge‐based spectral index across large geographic extents and indicate the inclusion of additional predictors, such as weather and management data, improves model accuracy. This work has implications for quantifying reductions in N leaching associated with cover crops, aiding in policymaking and evaluation of conservation programs that impact water bodies such as Chesapeake Bay.
Does splitting the nitrogen rate for corn (Zea mays) reduce the carbon dioxide equivalence?
Authors: Brugler Skye , Clay David E. , Joshi Deepak , Rizzo Donna M. , Clay Sharon A. , Nleya Thandi , Hatfield Gary 
Published: February 2025

AbstractSplitting N fertilizer application between the corn (Zea mays) plants pre‐emergence and vegetative growth stage has the potential to reduce N losses while increasing N use efficiency. However, there are few published reports that show that splitting fertilizer reduces greenhouse gas emissions. Therefore, this study compared the CO2e (carbon dioxide equivalent) calculated from N2O‐N and CO2‐C emissions from a single pre‐emergence application of 157 kg urea‐ N ha−1 with a nonfertilized control and two applications of 78.5 kg urea‐N ha−1 that were applied at pre‐emergence and at the corn plants V6 growth stage. Over three growing seasons (2021, 2022, and 2023), soil temperature, moisture, N2O‐N and CO2‐C emissions were measured six times per day from when the urea was applied to harvest. N2O‐N and CO2‐C emissions were separated into pre‐split and post‐split periods, and carbon dioxide equivalence was calculated. Splitting the N rate: (1) increased N2O‐N emissions in 2021 and 2022; (2) reduced CO2‐C emissions during the post‐split period in 2022 and 2023; and (3) did not influence total CO2e in 2021 and reduced CO2e emissions in 2022 and 2023. Based on these results, splitting the N fertilizer rate between pre‐emergence and the corn plants V6 growth stage should be considered as a potential climate smart practice.
Scoring Anomalous Vertices Through Quantum Walks
Authors: Vlasic Andrew , Pham Anh 
Published: February 2025

AbstractWith the constant flow of data from vast sources over the past decades, a plethora of advanced analytical techniques have been developed to extract relevant information from different data types ranging from labeled data, quasi‐labeled data, and data with no labels known a priori. For data with at best quasi‐labels, graphs are a natural representation and have important applications in many industries and scientific disciplines. Specifically, for unlabeled data, anomaly detection on graphs is a method to determine which data points do not posses the latent characteristics that are present in most other data. There have been a variety of classical methods to compute an anomalous score for the individual vertices of a respective graph, such as checking the local topology of a node, random walks, and complex neural networks. Leveraging the structure of the graph, the first quantum algorithm is proposed to calculate the anomaly score of each node by continuously traversing the graph with a uniform starting position for all nodes. The proposed algorithm incorporates well‐known characteristics of quantum walks, and, taking into consideration the noisy intermediate‐scale quantum (NISQ) era and subsequent intermediate‐scale quantum (ISQ) era, an adjustment to the algorithm is provided to mitigate the increasing depth of the circuit. This algorithm is rigorously shown to converge to the expected probability with respect to the initial condition.
An Algebraic Roadmap of Particle Theories
Authors: Furey N. 
Published: February 2025

AbstractExpanding the results of previous research, a network of algebraic connections is demonstrated between six well‐known particle theories. These are the Spin(10) model, the Georgi–Glashow model, the Pati–Salam model, the Left–Right Symmetric model, the Standard Model both pre‐ and post‐Higgs mechanism. A new inclusion of a quaternionic reflection within the network further differentiates bosons from the boson in comparison to the Standard Model. It may introduce subtle new considerations for the phenomenology of electroweak symmetry breaking.