Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiscale Modification of Populus cathayana by Alkali Lignin Combined with Heat Treatment

Chemical modification of wood with green modifiers is highly desirable for sustainable development. With the aim of enhancing the water resistance and dimensional stability of fast growing wood, modifications were conducted using renewable and toxicity-free industrial lignin combined with heat treatment. Poplar (Populus cathayana) samples first underwent impregnation with alkali lignin solution and were then subjected to heat treatment at 140–180 °C for two hours. The results indicated that the modified wood showed excellent leaching resistance. The alkali lignin treatment improved surface hydrophobicity and compression strength, and decreased moisture and water uptake, thereby reducing the dimensional instability of modified wood. These changes became more pronounced as the heat-treating temperature increased. Scanning electron microscopy, confocal laser scanning microscopy, and Fourier transform infrared spectroscopy evidenced that a multiscale improvement of the alkali lignin occurred in the cell lumen and cell wall of wood fibers and vessels, with small alkali lignin molecules reacting with the wood matrix. This study paves the way for developing an effective modification approach for fast growing wood, as well as promoting the reuse of industrial lignin for high-value applications, and improves the sustainable development of the forestry industry.
In Situ Stochastic Training of MTJ Crossbars With Machine Learning Algorithms

Owing to high device density, scalability, and non-volatility, magnetic tunnel junction (MTJ)-based crossbars have garnered significant interest for implementing the weights of neural networks (NNs). The existence of only two stable states in MTJs implies a high overhead of obtaining optimal binary weights in software. This article illustrates that the inherent parallelism in the crossbar structure makes it highly appropriate for in situ training, wherein the network is taught directly on the hardware. It leads to significantly smaller training overhead as the training time is independent of the size of the network, while also circumventing the effects of alternate current paths in the crossbar and accounting for manufacturing variations in the device. We show how the stochastic switching characteristics of MTJs can be leveraged to perform probabilistic weight updates using the gradient descent algorithm. We describe how the update operations can be performed on crossbars implementing NNs and restricted Boltzmann machines, and perform simulations on them to demonstrate the effectiveness of our techniques. The results reveal that stochastically trained MTJ-crossbar feed-forward and deep belief nets achieve a classification accuracy nearly the same as that of real-valued weight networks trained in software and exhibit immunity to device variations.
The Structure and Mechanical Properties of the UHMWPE Films Modified by the Mixture of Graphene Nanoplates with Polyaniline

Highly oriented UHMWPE films were reinforced with functionalized graphene nanoplates (GNP). GNP was functionalized by deposition of polyaniline (PANI) on the GNP surface. The structure of GNP/PANI was studied by Raman spectroscopy, and the structure of xerogels and films based on UHMWPE was studied by DSC and SEM. PANI promotes the reduction of the GNP aggregation in the UHMWPE matrix and increases the degree of crystallinity due to heterogeneous crystallization. The new lamellar crystal structure has a high drawability. The highest value of the tensile strength 1330 MPa (an increase of 45%) was obtained with a filler content of 2 wt % GNP/PANI, and the highest value of Young’s modulus 41 GPa (an increase of 32%) was obtained with a filler content of 1 wt % GNP/PANI. The effect of GNP with PANI fillers on the dynamic mechanical properties of the UHMWPE films was discussed.
Biodegradation of thiocyanate by a native groundwater microbial consortium

Gold ore processing typically generates large amounts of thiocyanate (SCN−)-contaminated effluent. When this effluent is stored in unlined tailings dams, contamination of the underlying aquifer can occur. The potential for bioremediation of SCN−-contaminated groundwater, either in situ or ex situ, remains largely unexplored. This study aimed to enrich and characterise SCN−-degrading microorganisms from mining-contaminated groundwater under a range of culturing conditions. Mildly acidic and suboxic groundwater, containing ∼135 mg L−1 SCN−, was collected from an aquifer below an unlined tailings dam. An SCN−-degrading consortium was enriched from contaminated groundwater using combinatory amendments of air, glucose and phosphate. Biodegradation occurred in all oxic cultures, except with the sole addition of glucose, but was inhibited by NH4+ and did not occur under anoxic conditions. The SCN−-degrading consortium was characterised using 16S and 18S rRNA gene sequencing, identifying a variety of heterotrophic taxa in addition to sulphur-oxidising bacteria. Interestingly, few recognised SCN−-degrading taxa were identified in significant abundance. These results provide both proof-of-concept and the required conditions for biostimulation of SCN− degradation in groundwater by native aquifer microorganisms.
Electrospun Graphene Nanosheet-Filled Poly(trimethylene terephthalate) Composite Fibers: Effects of the Graphene Nanosheet Content on Morphologies, Electrical Conductivity, Crystallization Behavior, and Mechanical Properties

In this study the effects of increased graphene nanosheet (GNS) concentration on variations in the structure and properties of electrospun GNS-filled poly(trimethylene terephthalate) (PTT/GNS) composite fiber, such as its morphologies, crystallization behavior, mechanical properties, and electrical conductivity, were investigated. The effects of GNS addition on solution rheology and conductivity were also investigated. GNSs were embedded in the fibers and formed protrusions. The PTT cold crystallization rate of PTT/GNS composite fibers increased with the gradual addition of GNSs. A PTT mesomorphic phase was formed during electrospinning, and GNSs could induce the PTT mesomorphic phase significantly during PTT/GNS composite fiber electrospinning. The PTT/GNS composite fiber mats (CFMs) became ductile with the addition of GNSs. The elastic recoveries of the PTT/GNS CFMs with 170 °C annealing were better than those of the as-spun PTT/GNS CFMs. Percolation scaling laws were applied to the magnitude of conductivity to reveal the percolation network of electrospun PTT/GNS CFMs. The electrical conductivity mechanism of the PTT/GNS CFMs differed from that of the PTT/GNS composite films. Results showed that the porous structure of the PTT CFMs influenced the performance of the mats in terms of electrical conductivity.
Synthesis and Properties of Gelatin Methacryloyl (GelMA) Hydrogels and Their Recent Applications in Load-Bearing Tissue

Photocrosslinked gelatin methacryloyl (GelMA) hydrogels have attracted great concern in the biomedical field because of their good biocompatibility and tunable physicochemical properties. Herein, different approaches to synthesize GelMA were introduced, especially, the typical method using UV light to crosslink the gelatin-methacrylic anhydride (MA) precursor was introduced in detail. In addition, the traditional and cutting-edge technologies to characterize the properties of GelMA hydrogels and GelMA prepolymer were also overviewed and compared. Furthermore, the applications of GelMA hydrogels in cell culture and tissue engineering especially in the load-bearing tissue (bone and cartilage) were summarized, followed by concluding remarks.
Study on Foaming Quality and Impact Property of Foamed Polypropylene Composites

In the present work, foamed polypropylene (PP) composites were prepared by chemical foaming technology, and the foaming quality and impact property of the foamed PP composites were studied. The results showed that the foaming quality was significantly improved after the introduction of thermoplastic rubber (TPR) and polyolefin elastomer (POE). Meanwhile, it was found that the impact property depended on the intrinsic toughness and contribution of foams (cells) to the PP composites. Furthermore, the data regarding impact property in low temperature showed that when the temperature was between −80 and −20 °C, the impact properties of the foamed PP composites were higher than that of the unfoamed sample, which was due to the impact property being completely contributed by cells under this condition. Meanwhile, when the temperature ranged from −20 to 20 °C, the impact property of the unfoamed sample was higher, which was due to the PP matrix contributing more to the impact property under this temperature. This work significantly improved the foaming quality of foamed PP composites and provided reliable evidence for the improvement of impact property.
An overview of deep learning in the field of dentistry

Purpose Artificial intelligence (AI), represented by deep learning, can be used for real-life problems and is applied across all sectors of society including medical and dental field. The purpose of this study is to review articles about deep learning that were applied to the field of oral and maxillofacial radiology. Materials and Methods A systematic review was performed using Pubmed, Scopus, and IEEE explore databases to identify articles using deep learning in English literature. The variables from 25 articles included network architecture, number of training data, evaluation result, pros and cons, study object and imaging modality. Results Convolutional Neural network (CNN) was used as a main network component. The number of published paper and training datasets tended to increase, dealing with various field of dentistry. Conclusion Dental public datasets need to be constructed and data standardization is necessary for clinical application of deep learning in dental field.
Novel Multifunctional Luminescent Electrospun Fluorescent Nanofiber Chemosensor-Filters and Their Versatile Sensing of pH, Temperature, and Metal Ions

Novel multifunctional fluorescent chemosensors composed of electrospun (ES) nanofibers with high sensitivity toward pH, mercury ions (Hg2+), and temperature were prepared from poly(N-Isopropylacrylamide-co-N-methylolacrylamide-co-rhodamine derivative) (poly(NIPAAm-co-NMA-co-RhBN2AM)) by employing an electrospinning process. NIPAAm and NMA moieties provide hydrophilic and thermo-responsive properties (absorption of Hg2+ in aqueous solutions), and chemical cross-linking sites (stabilization of the fibrous structure in aqueous solutions), respectively. The fluorescent probe, RhBN2AM is highly sensitive toward pH and Hg2+. The synthesis of poly(NIPAAm-co-NMA-co-RhBN2AM) with different compositions was carried on via free-radical polymerization. ES nanofibers prepared from sensory copolymers with a 71.1:28.4:0.5 NIPAAm:NMA:RhBN2AM ratio (P3 ES nanofibers) exhibited significant color change from non-fluorescent to red fluorescence while sensing pH (the λPL, max exhibited a 4.8-fold enhancement) or Hg2+ (at a constant Hg2+ concentration (10−3 M), the λPL, max of P3-fibers exhibited 4.7-fold enhancement), and high reversibility of on/off switchable fluorescence emission at least five times when Hg2+ and ethylenediaminetetraacetic acid (EDTA) were sequentially added. The P3 ES nanofibrous membranes had a higher surface-to-volume ratio to enhance their performance than did the corresponding thin films. In addition, the fluorescence emission of P3 ES nanofibrous membranes exhibited second enhancement above the lower critical solution temperature. Thus, the ES nanofibrous membranes prepared from P3 with on/off switchable capacity and thermo-responsive characteristics can be used as a multifunctional sensory device for specific heavy transition metal (HTM) in aqueous solutions.
Recent Advances in Organic Thermoelectric Materials: Principle Mechanisms and Emerging Carbon-Based Green Energy Materials

Thermoelectric devices have recently attracted considerable interest owing to their unique ability of converting heat to electrical energy in an environmentally efficient manner. These devices are promising as alternative power generators for harvesting electrical energy compared to conventional batteries. Inorganic crystalline semiconductors have dominated the thermoelectric material fields; however, their application has been restricted by their intrinsic high toxicity, fragility, and high cost. In contrast, organic thermoelectric materials with low cost, low thermal conductivity, easy processing, and good flexibility are more suitable for fabricating thermoelectric devices. In this review, we briefly introduce the parameters affecting the thermoelectric performance and summarize the most recently developed carbon-material-based organic thermoelectric composites along with their preparation technologies, thermoelectric performance, and future applications. In addition, the p- and n-type carbon nanotube conversion and existing challenges are discussed. This review can help researchers in elucidating the recent studies on carbon-based organic thermoelectric materials, thus inspiring them to develop more efficient thermoelectric devices.
A Review of Multiscale Computational Methods in Polymeric Materials

Polymeric materials display distinguished characteristics which stem from the interplay of phenomena at various length and time scales. Further development of polymer systems critically relies on a comprehensive understanding of the fundamentals of their hierarchical structure and behaviors. As such, the inherent multiscale nature of polymer systems is only reflected by a multiscale analysis which accounts for all important mechanisms. Since multiscale modelling is a rapidly growing multidisciplinary field, the emerging possibilities and challenges can be of a truly diverse nature. The present review attempts to provide a rather comprehensive overview of the recent developments in the field of multiscale modelling and simulation of polymeric materials. In order to understand the characteristics of the building blocks of multiscale methods, first a brief review of some significant computational methods at individual length and time scales is provided. These methods cover quantum mechanical scale, atomistic domain (Monte Carlo and molecular dynamics), mesoscopic scale (Brownian dynamics, dissipative particle dynamics, and lattice Boltzmann method), and finally macroscopic realm (finite element and volume methods). Afterwards, different prescriptions to envelope these methods in a multiscale strategy are discussed in details. Sequential, concurrent, and adaptive resolution schemes are presented along with the latest updates and ongoing challenges in research. In sequential methods, various systematic coarse-graining and backmapping approaches are addressed. For the concurrent strategy, we aimed to introduce the fundamentals and significant methods including the handshaking concept, energy-based, and force-based coupling approaches. Although such methods are very popular in metals and carbon nanomaterials, their use in polymeric materials is still limited. We have illustrated their applications in polymer science by several examples hoping for raising attention towards the existing possibilities. The relatively new adaptive resolution schemes are then covered including their advantages and shortcomings. Finally, some novel ideas in order to extend the reaches of atomistic techniques are reviewed. We conclude the review by outlining the existing challenges and possibilities for future research.
Recent Progress in Thermoelectric Materials Based on Conjugated Polymers

Organic thermoelectric (TE) materials can directly convert heat to electricity, and they are emerging as new materials for energy harvesting and cooling technologies. The performance of TE materials mainly depends on the properties of materials, including the Seebeck coefficient, electrical conductivity, thermal conductivity, and thermal stability. Traditional TE materials are mostly based on low-bandgap inorganic compounds, such as bismuth chalcogenide, lead telluride, and tin selenide, while organic materials as promising TE materials are attracting more and more attention because of their intrinsic advantages, including cost-effectiveness, easy processing, low density, low thermal conductivity, and high flexibility. However, to meet the requirements of practical applications, the performance of organic TE materials needs much improvement. A variety of efforts have been made to enhance the performance of organic TE materials, including the modification of molecular structure, and chemical or electrochemical doping. In this review, we summarize recent progress in organic TE materials, and discuss the feasible strategies for enhancing the properties of organic TE materials for future energy-harvesting applications.
Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across

Background: Low-cost, high-throughput in vitro bioassays have potential as alternatives to animal models for toxicity testing. However, incorporating in vitro bioassays into chemical toxicity evaluations such as read-across requires significant data curation and analysis based on knowledge of relevant toxicity mechanisms, lowering the enthusiasm of using the massive amount of unstructured public data. Objective: We aimed to develop a computational method to automatically extract useful bioassay data from a public repository (i.e., PubChem) and assess its ability to predict animal toxicity using a novel bioprofile-based read-across approach. Methods: A training database containing 7,385 compounds with diverse rat acute oral toxicity data was searched against PubChem to establish in vitro bioprofiles. Using a novel subspace clustering algorithm, bioassay groups that may inform on relevant toxicity mechanisms underlying acute oral toxicity were identified. These bioassays groups were used to predict animal acute oral toxicity using read-across through a cross-validation process. Finally, an external test set of over 600 new compounds was used to validate the resulting model predictivity. Results: Several bioassay clusters showed high predictivity for acute oral toxicity (positive prediction rates range from 62–100%) through cross-validation. After incorporating individual clusters into an ensemble model, chemical toxicants in the external test set were evaluated for putative acute toxicity (positive prediction rate equal to 76%). Additionally, chemical fragment–in vitro–in vivo relationships were identified to illustrate new animal toxicity mechanisms. Conclusions: The in vitro bioassay data-driven profiling strategy developed in this study meets the urgent needs of computational toxicology in the current big data era and can be extended to develop predictive models for other complex toxicity end points. https://doi.org/10.1289/EHP3614
Larvae of Caribbean Echinoids Have Small Warming Tolerances for Chronic Stress in Panama

In species with complex life cycles, early developmental stages are often less thermally tolerant than adults, suggesting that they are key to predicting organismal response to environmental warming. Here we document the optimal and lethal temperatures of larval sea urchins, and we use those to calculate the warming tolerance and the thermal safety margin of early larval stages of seven tropical species. Larvae of Echinometra viridis, Echinometra lucunter, Lytechinus williamsi, Eucidaris tribuloides, Tripneustes ventricosus, Clypeaster rosaceus, and Clypeaster subdepressus were reared at 26, 28, 30, 32, and 34 °C for 6 days. The temperatures at which statistically significant reductions in larval performance are evident are generally the same temperatures at which statistically significant reductions in larval survival were detected, showing that the optimal temperature is very close to the lethal temperature. The two Echinometra species had significantly higher thermal tolerance than the other species, with some surviving culture temperatures of 34 °C and showing minimal impacts on growth and survival at 32 °C. In the other species, larval growth and survival were depressed at and above 30 or 32 °C. Overall, these larvae have lower warming tolerances (1 to 5 °C) and smaller thermal safety margins (−3 to 3 °C) than adults. Survival differences among treatments were evident by the first sampling on day 2, and survival at the highest temperatures increased when embryos were exposed to warming after spending the first 24 hours at ambient temperature. This suggests that the first days of development are more sensitive to thermal stress than are later larval stages.
A Pilot Study of Neurocognitive Function and Brain Structures in Adolescents With Alcohol Use Disorders: Does Maltreatment History Matter?

Neurocognitive and brain structural differences are associated with adolescent onset alcohol use disorders (AUDs). Maltreatment histories may contribute to current results. To examine these issues, healthy adolescents (n = 31), adolescents without maltreatment and AUD (AUD − MAL, n = 28), and adolescents with AUDs with maltreatment (AUD + MAL, n = 17) underwent comprehensive neurocognitive assessments and MRI structural scans. Controls performed significantly better than the two AUD groups in math and language. The AUD + MAL group performed significantly lower in sustained attention compared to the AUD − MAL and control groups and lower in reading compared to controls. The AUD + MAL group had larger left pars triangularis, a region of the inferior frontal gyrus, compared to the AUD-MAL and control groups, and smaller anterior corpus callosum volumes versus the AUD − MAL group. There were no group differences in other prefrontal cortex, amygdala, hippocampus, and parahippocampal volumes. The AUD + MAL group showed an inverse correlation between hippocampal volumes and age. AUD variables were associated with lower performance in fine-motor and executive function. Cannabis use variables were associated with lower performance in fine-motor, language, visual-spatial, memory, and executive function. Parahippocampal volumes positively correlated with abstinence. The preliminary results suggest adolescent AUD studies should consider examinations of maltreatment history, comorbid substance use disorders, and recovery during abstinence in their analyses.
Quantifying Mg2+ Binding to ssDNA Oligomers: A Self-Consistent Field Theory Study at Varying Ionic Strengths and Grafting Densities

The performance of aptamer-based biosensors is crucially impacted by their interactions with physiological metal ions, which can alter their structures and chemical properties. Therefore, elucidating the nature of these interactions carries the utmost importance in the robust design of highly efficient biosensors. We investigated Mg2+ binding to varying sequences of polymers to capture the effects of ionic strength and grafting density on ion binding and molecular reorganization of the polymer layer. The polymers are modeled as ssDNA aptamers using a self-consistent field theory, which accounts for non-covalent ion binding by integrating experimentally-derived binding constants. Our model captures the typical polyelectrolyte behavior of chain collapse with increased ionic strength for the ssDNA chains at low grafting density and exhibits the well-known re-entrant phenomena of stretched chains with increased ionic strength at high grafting density. The binding results suggest that electrostatic attraction between the monomers and Mg2+ plays the dominant role in defining the ion cloud around the ssDNA chains and generates a nearly-uniform ion distribution along the chains containing varying monomer sequences. These findings are in qualitative agreement with recent experimental results for Mg2+ binding to surface-bound ssDNA.