bluuebunny/crossref_metadata_2025_split huggingface dataset:
On the pressure dependence of viscosity, especially for fluids that have a tendency to form glasses
Authors: Hopper Nicholas , Espinosa-Marzal Rosa M. , Tysoe Wilfred 
Published: December 2024

Understanding fluid viscosity is crucial for applications including lubrication and chemical kinetics. A commonality of molecular models is that they describe fluid flow based on the availability of vacant space. The proposed analysis builds on Goldstein’s idea that viscous transport must involve the concerted motion of a molecular ensemble, referred to as cooperatively rearranging regions (CRRs) by Adam and Gibbs in their entropy-based viscosity model for liquids close to their glass transition. The viscosity data for propylene carbonate reveal a non-monotonic trend of the activation volume with pressure, suggesting the existence of two types of CRR with different compressibility behaviors. This is proposed to result from a change in CRR free volume (&lt;0.2 GPa) and a growth in its size (&gt;0.2 GPa). We use Evans–Polanyi perturbation theory to develop an analytical model for the structural changes of the CRR in function of pressure and temperature and their effect on Eyring viscosity. This analysis shows that the activation energies and volumes scale with the CRR size. Using the compressibility data of propylene carbonate, we show that the activation volume of the CRR at low pressures depends on the compressibility of an ensemble comprised of the first coordination shell around a molecule. At higher pressures, we apply an Adam–Gibbs-type analysis to model the increase in CRR size and its effect on viscosity, where the increase in size is estimated from propylene carbonate’s heat capacity. However, this analysis also reveals deviations from the Adam and Gibbs model that will guide future improvements.
Proximity effects, topological states, and correlated physics in graphene heterostructures
Authors: Zollner Klaus , Fabian Jaroslav 
Published: December 2024

Abstract
Graphene spintronics is an emerging field of research that explores the use of graphene’s extraordinary spin and charge transport properties to manipulate and control the electron spin degree of freedom for potential applications in information processing and data storage. Particularly interesting are graphene-based van-der-Waals heterostructures, which allow the creation of tailored spintronic properties, emerging from proximity effects, without destroying the unique Dirac states. The possibility to induce customized spin–orbit and exchange coupling in graphene, via band structure engineering, can lead to topologically protected edge states for dissipationless electronics and spintronics. In flat-band graphene materials, in particular, magic-angle bilayer graphene and rhombohedral (ABC stacked) trilayer graphene, the coupling between spin and valley (orbital) degrees of freedom can be coupled by strong Coulomb interactions, leading to a variety of fascinating correlated and superconducting phases. The emerging isospin electronics, combining both the electron spin and valley flavors, can transform the landscape of low-temperature electronics and lead to novel functionalities based on quantum matter. This Perspective explores the latest advancements in proximity effects, topological states, and correlated physics in graphene-based van der Waals heterostructures, discussing the fundamentals for potential applications.
Green synthesis of propolis mediated silver nanoparticles with antioxidant, antibacterial, anti-inflammatory properties and their burn wound healing efficacy in animal model
Authors: Islam Shabana , Hussain Erum Akbar , Shujaat Shahida , Rasheed Muhammad Adil 
Published: December 2024

Abstract
Developing an efficient and cost-effective wound-healing substance to treat wounds and regenerate skin is desperately needed in the current world. The present study evaluated in vivo wound healing and in vitro antioxidant, antibacterial, anti-inflammatory activities of propolis mediated silver nanoparticles. Extract of Bee propolis from northeast Punjab, Pakistan, has been prepared via maceration and subjected to chemical identification. The results revealed that it is rich in phenolic contents (88 ± 0.004 mg GAE ml−1, 34 ± 0.1875 mg QE ml−1) hence, employed as a reducer and capping agent to afford silver nanoparticles (AgNPs) by green approach. The prepared nanoparticles have been characterized by UV-visible (UV–vis), Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), x-ray diffraction (XRD). The propolis mediated AgNPs possess cubic face center with spherical shape and measured 50–60 nm in size. Moreover, propolis mediated silver nanoparticles have been studied for various biological activities. The results showed excellent antioxidant (0.4696 μg ml−1), anti-inflammatory (0.3996 μg ml−1) and antibacterial activities against Staphylococcus aureus (MIC 0.462 μg ml−1) and Proteus mirabilis (MIC 0.659 μg ml−1) bacterium. An ointment was prepared by mixing AgNPs with polymeric gels for burn wound treatment in rabbits. We found rapid wound healing and higher collagen deposition in AgNPs treated wounds than in control group. Our data suggest that AgNPs from propolis ameliorate excision wounds, and hence, these AgNPs could be potential therapeutic agents for the treatment of burns.
Characterization and thermometry of dissipatively stabilized steady states
Authors: Grattan G S , Liguori-Schremp A M , Perez D Rodriguez , Kapit E , Jones W , Graf P 
Published: December 2024

Abstract
In this work we study the properties of dissipatively stabilized steady states of noisy quantum algorithms, exploring the extent to which they can be well approximated as thermal distributions, and proposing methods to extract the effective temperature T. We study an algorithm called the relaxational quantum eigensolver (RQE), which is one of a family of algorithms that attempt to find ground states and balance error in noisy quantum devices. In RQE, we weakly couple a second register of auxiliary ‘shadow’ qubits to the primary system in Trotterized evolution, thus engineering an approximate zero-temperature bath by periodically resetting the auxiliary qubits during the algorithm’s runtime. Balancing the infinite temperature bath of random gate error, RQE returns states with an average energy equal to a constant fraction of the ground state. We probe the steady states of this algorithm for a range of base error rates, using several methods for estimating both T and deviations from thermal behavior. In particular, we both confirm that the steady states of these systems are often well-approximated by thermal distributions, and show that the same resources used for cooling can be adopted for thermometry, yielding a fairly reliable measure of the temperature. These methods could be readily implemented in near-term quantum hardware, and for stabilizing and probing Hamiltonians where simulating approximate thermal states is hard for classical computers.
Assessment of bioaerosol composition and public health implications in high-traffic urban areas of Southwest, Nigeria
Authors: Odeyemi Daniel Abayomi , Alao Jude Oluwapelumi , Kayode Tolulope Adeyemi , Durugbo Ernest Uzodimma 
Published: December 2024

Abstract
Bioaerosols, a significant yet underexplored component of atmospheric particulate matter, pose substantial public health risks, particularly in regions with poor air quality. This study investigates the composition of bioaerosols in public spaces, specifically two interstate motor parks and two marketplaces in Osun State, Nigeria, over six months. Air samples were collected, and bacterial and fungal species were identified, focusing on pathogenic organisms. The results revealed the presence of well-known pathogens, including Staphylococcus aureus, Escherichia coli, Klebsiella sp., Pseudomonas aeruginosa, Aspergillus sp., and Fusarium sp., which are associated with respiratory and gastrointestinal infections, as well as antimicrobial resistance. Site-specific differences in microbial diversity were observed, with higher bacterial diversity in motor parks and greater fungal occurrence in marketplaces influenced by environmental factors such as waste management. The findings highlight the urgent need for microbial air quality monitoring in public spaces, alongside improved sanitation practices. This study provides critical insights into the public health risks posed by bioaerosols and calls for local and global interventions to mitigate the impact of airborne pathogens in urban environments.
Evaluating the environmental effects of Japan's tourism: insights into pandemic-induced changes and driving forces
Authors: Huang Yuzhuo , Shigetomi Yosuke , Matsumoto Ken’ichi 
Published: December 2024

Abstract
The intersection of the COVID-19 pandemic and tourism has significantly influenced emissions from supply chains, highlighting a complex dynamic between tourism and climate change. Although the pandemic represents a unique event within a specific timeframe, analyzing its impact on tourism provides valuable insights into the characteristics of tourism carbon footprints (TCF) in the context of sudden public health crises. Accordingly, we used the latest data available from the global multi-regional input–output table of EXIOBASE3 and Japan’s tourist consumption statistics to estimate the TCF from all tourist sources in Japan based on the global economic system, and we visualized the carbon flow caused by population mobility. The decomposition of the environmental impact of the pandemic response further identified the driving forces of TCF changes. The main findings suggested that substantial differences exist in the emission levels and sectoral distributions of TCF between inbound and domestic tourism in Japan. Domestic TCF accounted for the majority of the overall TCF, and it also showed a stronger ability to withstand the pandemic impact, accounting for over 90% of Japan’s TCF during the pandemic. The pandemic greatly reduced the TCF and further exacerbated the driving differences. Prior to the pandemic, Japan’s TCF had diverse driving forces; however, pandemic-induced restrictions magnified the volume effect, especially in the inbound TCF, whereas other effects remained substantial for the domestic TCF.
Occurrence and dissipation of antibiotics in manure-amended vegetable greenhouse soils under sprinkling irrigation
Authors: Yang Kaifeng , Zhao Fangkai , Yang Lei , Huang Yong , Shen Linjun , Liu Honglin , Feng Qingyu , Chen Liding 
Published: December 2024

Abstract
Agricultural practices critically influence the dissipation of antibiotics in soil, particularly in intensive vegetable greenhouses. This study examines how manure fertilization and irrigation techniques affect antibiotic behavior to improve management practices and reduce contamination. Soil samples were collected from plastic greenhouses with different fertilization periods (0, 5, and 10 years). Regression models were employed to quantify the relationships and interactions among agricultural management intensities, soil characteristics, and antibiotic dissipation. In soils with a long history of organic fertilization, significantly higher antibiotic residues were observed compared to those with less or no fertilization. The concentration of antibiotics decreased further away from irrigation sources, suggesting that irrigation patterns significantly affect their spatial distribution (R2
≥ 0.90, p ≤ 0.05). Prolonged fertilization increased soil pH and organic carbon, which impeded antibiotic dissipation, whereas enhanced soil moisture actively facilitated it (λ = 0.48, p < 0.001). These findings underscore the profound impact of agricultural practices on antibiotic dissipation, advocating for adjustments in these practices to mitigate antibiotic contamination and bolster agroecosystem sustainability. The insights provided are crucial for developing targeted strategies to minimize antibiotic residues in agricultural soils, ultimately supporting healthier and more sustainable farming environments.
Atomic surface of diamond induced by novel green photocatalytic chemical mechanical polishing with high material removal rate
Authors: Yu Zhibin , Zhang Zhenyu , Zeng Zinuo , Fan Cheng , Gu Yang , Shi Chunjing , Zhou Hongxiu , Meng Fanning , Feng Junyuan 
Published: December 2024

Abstract
Atomic surfaces are strictly required by high-performance devices of diamond. Nevertheless, diamond is the hardest material in nature, leading to the low material removal rate (MRR) and high surface roughness during machining. Noxious slurries are widely used in conventional chemical mechanical polishing (CMP), resulting in the possible pollution to the environment. Moreover, the traditional slurries normally contain more than four ingredients, causing difficulties to control the process and quality of CMP. To solve these challenges, a novel green CMP for single crystal diamond was developed, consisting of only hydrogen peroxide, diamond abrasive and Prussian blue (PB) /titania catalyst. After CMP, atomic surface is achieved with surface roughness Sa of 0.079 nm, and the MRR is 1168 nm·h−1. Thickness of damaged layer is merely 0.66 nm confirmed by transmission electron microscopy (TEM). X-ray photoelectron spectroscopy, electron paramagnetic resonance and TEM reveal that •OH radicals form under ultraviolet irradiation on PB/titania catalyst. The •OH radicals oxidize diamond, transforming it from monocrystalline to amorphous atomic structure, generating a soft amorphous layer. This contributes the high MRR and formation of atomic surface on diamond. The developed novel green CMP offers new insights to achieve atomic surface of diamond for potential use in their high-performance devices.
Numerical prediction with finite element analysis (FEA) of burst pressure in pipelines with interacting internal & external corrosion defects
Authors: Al Naamani Hussain , Al Jahwari Farooq , Pervez Tasneem , Al Owaisi Sultan , Al Shabibi Abdullah , Joshi Sanket 
Published: December 2024

Abstract
Pipelines are the main method of oil and gas transportation due to economics and safety. However, the use of carbon steel as a material type has led to the introduction of pipeline defect assessment due to the increased probability of corrosion threat. Several assessment methods have been developed over the years to assess corrosion defects in pipelines. Corrosion defects can occur in pipelines internally or externally in the form of single or clustered defects. As the distance between two corrosion defects decreases, they will begin to interact reducing the failure pressure compared to when they are treated as single defects. Although there have been studies on this subject, oil and gas pipeline operators are still faced with inconsistencies with the decision upon which closely spaced defects can be clustered as single defects. In this work, the influence of surface location (internal or external) and depth on defect interaction were analyzed using finite element analysis and further compared with analytical methods. The pipes were modeled with curved boxed defects at different spacing and orientations to simulate actual metal loss defects. Comparison of the results showed conservatism in the existing analytical methods which may result in unnecessary shutdowns, repairs, and operating below the required parameters. The numerical results showed that defect surface location (internal or external) and depth has a great influence on the defect interaction and predicted failure pressure.
Apple detection in complex environment based on improved YOLOv8n
Authors: Yue Y R , Cui S H , Shan W 
Published: December 2024

Abstract
Due to the complexity of apple growth environment and the large number of fruits, the existing object detection algorithms are difficult to achieve balance in fruit detection accuracy and model lightweight.An apple detection algorithm based on Faster-EMA Block and Lightweight neck in complex environment is proposed.Firstly, the Bottleneck of Faster-EMA Block replacing C2f of Backbone is constructed by using PConv (Partial Convolution) and EMA (Efficient Multi-Scale Attention) attention mechanism. Secondly, an efficient feature fusion technology is adopted, especially the lightweight processing of neck features, so that the model can maintain a high level of accuracy while reducing the burden of parameters.Then, in order to deepen the model’s ability in vision detection, we integrated the detection layer with unobvious apple features to capture the apple shape more accurately and improve the detection accuracy. Finally, to evaluate the matching degree between the prediction results and the real shape more accurately, the Shape IoU is introduced. Experiments show that, compared with the YOLOv8 baseline network, mAP50 was increased by 1.5%, mAP50–95 was increased by 1.7%, and params was reduced by 20% on the hybrid apple dataset. The improved network has a better detection effect on apples,which lays the foundation for the intelligent picking of apples.
Facile synthesis of ternary NiFe2O4-Co3O4@G nanocomposite for supercapacitor
Authors: Kumar Rajiv , Lakra Rajan , Singh Mamraj , Soam Ankur 
Published: December 2024

Abstract
This work presents a novel NiFe2O4-Co3O4@G nanocomposite electrode for supercapacitors. NiFe2O4 nanoparticles along with Co3O4 were observed to be attached with graphene layer sheets. The prepared electrode with conductive network and porous structure offered a large active site to the electrolyte ions. NiFe2O4-Co3O4@G nanocomposite has shown a specific capacitance of 407 Fg–1 at a scan rate of 20 mVs−1 in 2 M KOH aqueous electrolyte. Moreover, NiFe2O4-Co3O4@G nanocomposite electrode exhibited a retention of 91% capacitance after 2000 cycles. The improved electrochemical performance of the composite is due to the synergistic effects among all three constituents and unique surface morphology with large effective surface area to the electrolyte ions.
Comparative analysis of grain size for bearing coating using variational mode decomposition of vibration signal
Authors: Cambow Rameshwar , Singh Manpreet 
Published: December 2024

Abstract
One of the prime factors behind variation in properties of the given material is the change in its grain size. In this work, any change in grain size of bearing coating selected as an indicator to alert deterioration in coated surface of the bearing. Here, an online method to differentiate the level of grain size for the bearing surface is presented using vibration signature analysis. Five ball bearings (Make: NBC, Model: 6205) were electroplated with industry specified coatings, Black oxide, Silver, Copper, Zinc phosphate (ZnP), Nickel. These coated bearings were installed in customised bearing test rig, one after another. Each coated bearing was made to run at five different speeds varying from 300 rpm to 1500 rpm and its vibration signatures were captured. The obtained vibration signals must be carrying unique characteristics corresponding to interaction of bearing balls with surfaces of different grain size thus distributed in general. The signals were decomposed into six modes using Variational mode decomposition (VMD) for band wise evaluation Further, to study distributed characteristics present in the signal in relevance to grain size, prominently used statistical parameters: Root Mean Square (RMS), crest factor, Variance, Skewness, Kurtosis, Shannon entropy and Log energy were selected. Then, all previously mentioned statistical parameters were calculated individually for newly generated frequency bands. Further, it has been noted that at 300 & 1500 rpm, RMS has shown considerable correlation to grain size variation. Additionally, it has been noted that these responded Intrinsic Mode Functions (IMFs) were not having high energy contents of the signal which indicates the miniscule presence of grain size signature in vibration signal in comparison to other rotational characteristics. On further analysis based on the chain indexing, RMS was found to be most relevant statistical parameter at low rpm as well as high rpm in reference to grain size.
Machine-learned closure of URANS for stably stratified turbulence: connecting physical timescales & data hyperparameters of deep time-series models
Authors: Gopalakrishnan Meena Muralikrishnan , Liousas Demetri , Simin Andrew D , Kashi Aditya , Brewer Wesley H , Riley James J , de Bruyn Kops Stephen M 
Published: December 2024

Abstract
Stably stratified turbulence (SST), a model that is representative of the turbulence found in the oceans and atmosphere, is strongly affected by fine balances between forces and becomes more anisotropic in time for decaying scenarios. Moreover, there is a limited understanding of the physical phenomena described by some of the terms in the Unsteady Reynolds-Averaged Navier–Stokes (URANS) equations—used to numerically simulate approximate solutions for such turbulent flows. Rather than attempting to model each term in URANS separately, it is attractive to explore the capability of machine learning (ML) to model groups of terms, i.e. to directly model the force balances. We develop deep time-series ML for closure modeling of the URANS equations applied to SST. We consider decaying SST which are homogeneous and stably stratified by a uniform density gradient, enabling dimensionality reduction. We consider two time-series ML models: long short-term memory and neural ordinary differential equation. Both models perform accurately and are numerically stable in a posteriori (online) tests. Furthermore, we explore the data requirements of the time-series ML models by extracting physically relevant timescales of the complex system. We find that the ratio of the timescales of the minimum information required by the ML models to accurately capture the dynamics of the SST corresponds to the Reynolds number of the flow. The current framework provides the backbone to explore the capability of such models to capture the dynamics of high-dimensional complex dynamical system like SST flows6

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Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
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