Application Of Structure Research Articles

Investigation on the Anisotropic Electrothermal Performance of Wood‐Based Graphene Electric Heating Composites

The unique material structure and pronounced anisotropy of wood bestow it with an array of remarkable properties, creating opportunities for designing functional materials. This study investigates the application of wood's anisotropic structure in temperature‐controlled intelligent electric heating materials by preparing wood‐based graphene electric heating composites (GNPs@EW). Graphene nanosheets (GNPs) are incorporated into a birch matrix (EW) to enhance the electrical and thermal conductivity of insulating wood. The integration of graphene with wood is confirmed through Fourier transform infrared spectrometer (FTIR) and X‐ray diffractometer (XRD) analysis. The optimal composite (GNPs@EW1) is prepared with graphene‐to‐anhydrous ethanol mass ratio of 1:1. The results demonstrate the influence of wood's anisotropy on the electrical and thermal conductivity of the composites, revealing superior performance in the axial direction compared to the tangential direction. The resistivity of GNPs@EW1 along the axial direction decreases by 2.1–2.9 times relative to the tangential direction, and the temperature rise after energization at 6 V is 22–29 °C higher in the axial direction. Furthermore, GNPs@EW1 exhibits thermal stability, enhanced axial mechanical properties, and temperature uniformity, establishing these superior axial properties as a foundation for the development of wood‐based heating materials and innovative smart heating applications.

Inulin as a Biopolymer; Chemical Structure, Anticancer Effects, Nutraceutical Potential and Industrial Applications: A Comprehensive Review

Inulin is a versatile biopolymer that is non-digestible in the upper alimentary tract and acts as a bifidogenic prebiotic which selectively promotes gut health and modulates gut–organ axes through short-chain fatty acids and possibly yet-to-be-known interactions. Inulin usage as a fiber ingredient in food has been approved by the FDA since June 2018 and it is predicted that the universal inulin market demand will skyrocket in the near future because of its novel applications in health and diseases. This comprehensive review outlines the known applications of inulin in various disciplines ranging from medicine to industry, covering its benefits in gut health and diseases, metabolism, drug delivery, therapeutic pharmacology, nutrition, and the prebiotics industry. Furthermore, this review acknowledges the attention of researchers to knowledge gaps regarding the usages of inulin as a key modulator in the gut–organ axes.

Deep eutectic solvents (DES): Structure, properties, and cutting-edge applications in green catalysis

Deep eutectic solvents (DES): Structure, properties, and cutting-edge applications in green catalysis

A (Comprehensive) Review of the Application of Quantitative Structure–Activity Relationships (QSARs) in the Prediction of New Compounds with Anti-Breast Cancer Activity

Computational approaches applied in drug discovery have advanced significantly over the past few decades. These techniques are commonly grouped under the term “computer-aided drug design” (CADD) and are now considered one of the key pillars of pharmaceutical discovery pipelines in both academic and industrial settings. In this work, we review Quantitative Structure–Activity Relationships (QSARs), one of the most used ligand-based drug design (LBDD) methods, with a focus on its application in the discovery and development of anti-breast cancer drugs. Critical steps in the QSAR methodology, essential for its correct application—but often overlooked, leading to insignificant or misleading models—are examined. Additionally, current anti-breast cancer treatment strategies were briefly overviewed, along with some targets for future treatments. The review covers QSAR studies from the past five years and includes a discussion of notable works that could serve as models for future applications of this interdisciplinary and complex method and that may help in feature drug design and development.

A rotable tunable ultra-wideband microwave absorber and simple coding application of gradient structure

A rotable tunable ultra-wideband microwave absorber and simple coding application of gradient structure

A graph neural network model application in point cloud structure for prolonged sitting detection system based on smartphone sensor data

AbstractThe prolonged sitting inherent in modern work and study environments poses significant health risks, necessitating effective monitoring solutions. Traditional human activity recognition systems often fall short in these contexts owing to their reliance on structured data, which may fail to capture the complexity of human movements or accommodate the often incomplete or unstructured nature of healthcare data. To address this gap, our study introduces a novel application of graph neural networks (GNNs) for detecting prolonged sitting periods using point cloud data from smartphone sensors. Unlike conventional methods, our GNN model excels at processing the unordered, three‐dimensional structure of sensor data, enabling more accurate classification of sedentary activities. The effectiveness of our approach is demonstrated by its superior ability to identify sitting, standing, and walking activities—critical for assessing health risks associated with prolonged sitting. By providing real‐time activity recognition, our model offers a promising tool for healthcare professionals to mitigate the adverse effects of sedentary behavior.

Designing Bouligand‐inspired reinforcement for creating wood fiber‐reinforced composites with excellent bending properties

AbstractFiber‐reinforced composites with a bionic Bouligand structure are known for their excellent mechanical properties. Currently, most of the research in this field focuses on man‐made fibers, and research on natural unidirectional fiber materials is still limited. This paper designing Bouligand‐inspired reinforcement for creating wood fiber‐reinforced composites. We investigate the effects of the helix angle on the bending properties, impact resistance and extreme environments of the material through a combination of experimental methods, finite element analysis, and theoretical modeling. Morphological analysis using scanning electron microscopy (SEM) to further explore fracture mechanisms. The findings reveal that the load–displacement curves predicted by the finite element method closely align with the experimental results. The bionic composites demonstrate not only strong bending properties but also significant impact resistance. Notably, the highest bending strength, bending modulus, and impact strength were achieved at a small helix angle of 12.8°, reaching values of 184.66 MPa, 10.21 GPa and 28.03 kJ/m2. The bouligand structure is designed to withstand extreme environments. This research significantly expands the potential applications of the bionic Bouligand structure within the realm of wood fiber‐reinforced composites.Highlights Preparation of Bouloigand‐like composites utilizing wood. Helix stacking angle affects the performance of WFRCs. Finite element analysis was employed to predict the mechanical properties. “Green” composites exhibit exceptional mechanical properties. Expanding the application areas of wood fiber‐reinforced composites.

Forest restoration improves habitat and water quality in tropical streams: A multiscale landscape assessment.

Forest restoration has been a common practice to safeguard water quality and stream health but it is unclear to which extent and pace forest restoration recovers stream ecosystem structure and functions. Also, stream health might be affected by the forest restoration type and the quality of the interventions. Here, we sought to evaluate the recovery of stream habitat and water quality through forest restoration in catchments dominated by pasturelands, and explored the relationship between landscape structure and stream ecosystem recovery. We sampled a total of 30 catchments during the dry season of 2023, covering six different classes (five catchment per class), based on the type and extent of forest cover: (i) all catchment area covered by native forest remnants, (ii) catchments mostly covered by old (26-37years) restored forests, (iii) catchments mostly covered by young (5-25years) restored forests, (iv) catchments in a pasture matrix with forest remnants around springs, (v) catchments in a pasture matrix with riparian buffers covered by pioneer vegetation (mostly herbs and shrubs), and (vi) catchments mostly covered by pastures. Data on stream water (e.g. temperature, nutrients and sediments) and habitat (e.g. substrate heterogeneity and volume of wood debris) quality were sampled and landscape metrics calculated by GIS at reach, riparian and catchment scales. In catchments covered by remnant and old restored forests, the water temperature and nutrient concentration were lower, and instream leaf banks were higher, whereas the number of large wood debris was higher in forest remnant catchments. Water temperature and ammoniacal-N correlated with Normalized Difference Vegetation Index (NDVI) at reach scale. Substrate heterogeneity and volume of woody debris correlated strongly with NDVI and proportion of forest over 38years old at the catchment scale. This outcome shows the potential application of forest structure (NDVI) and age for monitoring the stream ecosystem benefits of forest restoration. Overall, we found a gradient of recovery of both water and habitat quality progressing from more degraded (pasture-dominated catchments, pasture-dominated with forest remnants around springs, pasture-dominated with pioneer vegetation in riparian buffers) to more conserved catchments (young forest restoration, old forest restoration and old-growth conserved forests). In conclusion, the Atlantic Forest restoration contributed to improve water and habitat quality in streams, however these benefits were dependent on forest restoration age, extension and location.

A Study on the Differences in Optimized Inputs of Various Data-Driven Methods for Battery Capacity Prediction

As lithium-ion batteries become increasingly popular worldwide, accurately determining their capacity is crucial for various devices that rely on them. Numerous data-driven methods have been applied to evaluate battery-related parameters. In the application of these methods, input features play a critical role. Most researchers often use the same input features to compare the performance of various neural network models. However, because most models are regarded as black-box models, different methods may show different dependencies on specific features given the inherent differences in their internal structures. And the corresponding optimal inputs of different neural network models should be different. Therefore, comparing the differences in optimized input features for different neural networks is essential. This paper extracts 11 types of lithium battery-related health features, and experiments are conducted on two traditional machine learning networks and three advanced deep learning networks in three aspects of input differences. The experiment aims to systematically evaluate how changes in health feature types, dimensions, and data volume affect the performance of different methods and find the optimal input for each method. The results demonstrate that each network has its own optimal input in the aspects of health feature types, dimensions, and data volume. Moreover, under the premise of obtaining more accurate prediction accuracy, different networks have different requirements for input data. Therefore, in the process of using different types of neural networks for battery capacity prediction, it is very important to determine the type, dimension, and number of input health features according to the structure, category, and actual application requirements of the network. Different inputs will lead to larger differences in results. The optimization degree of mean absolute error (MAE) can be improved by 10–50%, and other indicators can also be optimized to varying degrees. Therefore, it is very important to optimize the network in a targeted manner.

FOF1-ATPase Biomolecular Motor: Structure, Motility Manipulations, and Biomedical Applications.

Biomolecular motors are dynamic systems found in organisms with high energy conversion efficiency. FOF1-ATPase is a rotary biomolecular motor known for its near 100% energy conversion efficiency. It utilizes the synthesis and hydrolysis of ATP to induce conformational changes in motor proteins, thereby converting chemical energy into mechanical motion. Given their high efficiency, autonomous propulsion capability, and modifiable structures, FOF1-ATPase motors have attracted significant attention for potential biomedical applications. This Review aims to introduce the detailed structure of FOF1-ATPase, explore various motility manipulation strategies, and summarize its applications in biological detection and cargo delivery. Additionally, innovative research methods are proposed to analyze the motion mechanism of FOF1-ATPase more comprehensively, with the goal of advancing its biomedical applications. Finally, this Review concludes with key insights and future perspectives.

The Beneficial Role of Polysaccharide Hydrocolloids in Meat Products: A Review.

Polysaccharide hydrocolloids have garnered increasing attention from consumers, experts, and food processing industries due to their advantages of abundant resources, favorable thickening properties, emulsification stability, biocompatibility, biodegradability, and high acceptance as food additives. This review focuses on the application of polysaccharide hydrocolloids and their beneficial roles in meat products by focusing on several commonly used polysaccharides (i.e., cellulose, chitosan, starch, sodium alginate, pectin, and carrageenan). Firstly, the recent advancements of polysaccharide hydrocolloids used in meat products are briefly introduced, along with their structure and potential application prospects. Then, the beneficial roles of polysaccharide hydrocolloids in meat products are comprehensively summarized and highlighted, including retarding lipid and protein oxidation, enhancing nutritional properties, improving texture and color quality, providing antibacterial activity, monitoring freshness, acting as a cryoprotectant, improving printability, and ensuring security. Finally, the challenges and opportunities of polysaccharide hydrocolloids in meat products are also introduced.

A Wavy Multiple Tm(III)-Containing Open Wells-Dawson Silicotungstate: Synthesis, Structure, and Catalytic Application.

A Tm(III)-rich silicotungstate Na{Tm3(H2O)17[Tm2(H2O)7(Si2W18O66)]}·35H2O (Tm5Si2W18) based on the open Wells-Dawson-type [Si2W18O66]16- building unit was synthesized by the reaction of Na10[α-SiW9O34]·18H2O, itaconic acid, and TmCl3 in a HAc/NaAc buffer solution. Five kinds of Tm(III) ions were found in this compound and further linked the {Tm2Si2W18} subunit to form an interesting wavy 1D chain structure, which achieved the introduction of more lanthanide (Ln) ions into the [Si2W18O66]16- unit for the first time. Tm5Si2W18 contains multiple exposed Tm-metal active sites, making it an efficient catalyst for the acetalization of 2-aminobenzamides/2-aminobenzenesulphonamides with aldehydes. A series of 2,3-dihydroquinazolinones and 3,4-dihydro-2H-1,2,4-benzothiadiazine-1,1-dioxides could be obtained in excellent yields under green and mild conditions.

Development and Performance Evaluation of an Auxetic Yield Metal Damper for Enhanced Seismic Energy Dissipation

In this study, a new auxetic yield metal (AYM) damper is introduced for the first time based on the application of the auxetic structure. The performance of the proposed system is investigated via nonlinear finite element analysis by using ABAQUS commercial code. These types of steel dampers are fabricated from mild steel plate with elliptical holes and dissipate energy through the inelastic deformation of the constitutive material. To assess the capability of the proposed AYM damper, a set of nonlinear quasi-static finite element analyzes has been conducted on the damper with various geometric parameters. To ensure that the numerical models accurately predict the responses of AYM dampers, experimental validation techniques were employed. This validation confirms the models' sufficient accuracy in representing the dampers' behavior. According to the results, the proposed AYM damper exhibits a low yield displacement, stable hysteretic loops, a good range of ductility, and a high energy dissipating capacity. The specific energy absorption and ductility of the proposed auxetic damper are 32.5J/kg and 59, respectively. With its ductile behavior, this damper can dissipate a large amount of earthquake input energy. Furthermore, it is found that the use of proposed auxetic dampers in the steel frame, increases the hardness, strength and ductility of the frame and the energy absorption increased by 210%.

Optimization design and application of high-rise building structure based on hybrid genetic algorithm

Optimization design and application of high-rise building structure based on hybrid genetic algorithm

Concrete members strengthened by adhesive-bolted steel plates: shear behaviour of the composite connection interface

The method of strengthening adhesive-bolted steel plates is widely used in various strengthening projects due to its advantages of slight damage to the original structure, convenient construction, and wide application. However, the strengthening effect is greatly limited by the stripping failure of the steel-adhesive-concrete interface. Some researchers have studied the shear behaviour of the anchor bolts and adhesive layer on the interface. While developing an accurate model to predict the behaviour of the hybrid connection interface is still a large challenge. In this paper, shear tests were carried out on push-out test specimens with three different interface connection forms to investigate the failure mechanism of the adhesive-bolted steel plate composite connection interface. The failure process and failure mode of various interfaces were summarized. Moreover, the ultimate strength, load vs. slips curve, interface strain, and stiffness degradation were discussed. The obtained results indicate that anchor bolts could limit the transfer of interfacial shear stress, and the interface with a larger anchor bolt diameter possesses better plasticity, greater average stiffness, and could bear a high shear force after multiple stripping. The interfacial ultimate shear capacity depends on the relative strength of the anchor bolts and adhesive layer. For the interface with lower strength anchor bolts, the interface strength depends on the strength of the adhesive layer. For the interface with higher strength anchor bolts, the interface strength depends on the anchor bolts and the area of the unpeeled adhesive layer before the failure of the interface. Subsequently, the design method for estimating the shear strength of the interface with sufficient efficiency was proposed based on the data analysis.

A Glimpse of Noncoding RNAs: Secondary Structure, Emerging Trends, and Potential Applications in Human Diseases.

An appealing strategy for the treatment of several diseases is the therapeutic targeting of noncoding RNAs (ncRNAs), such as microRNAs (miRNAs) and long noncoding RNAs (lncRNAs). Many antisense oligonucleotides and small interfering RNAs have been tested in clinical studies over the past 10years, and several of these have received FDA approval. However, trial results have thus far been mixed, with some studies reporting strong effects and others showing low effectiveness or side effects, including toxicity. Clinical trials for alternative entities like antimiRNAs are underway, and interest in lncRNA-based therapies is constantly growing. From this perspective, we discuss the basic overview of ncRNAs, their significant role as therapeutic biomarkers against different diseases, and the role of secondary structure in noncoding RNAs.

Construction of advanced Nb9VO25 electrode material by introducing graphene quantum dot for high energy supercapacitors with exceptionally high diffusive capacitance

Unreasonable tunnel structure and low intrinsic conductivity limit practical applications of niobium oxide in high-performance supercapacitors. The study reports the construction of Nb9VO25 electrode material via coordination of Nb(V) and V(V) with histidine and serine-functionalized and boron-doped graphene quantum dot (HSBGQD) and subsequent annealing. The introduction of HSBGQD and rambutan peel leads to formation of small Nb9VO25 nanocrystal and low valent Nb and V species. The combination of small size and more reasonable tunnel structure accelerates the ion diffusion. The Nb(IV) and V(IV) double doping optimizes the tunnel structure, narrows the bandgap and creates new pathways for high-speed electron transfer. The integration of defect engineering with graphene surface modification enhance the intrinsic conductivity. The Nb9VO25 electrode shows exceptionally high specific capacitance of 2925.3 F/g, which is more than 142 times that of Nb2O5. The symmetrical supercapacitor with Nb9VO25 electrodes and PVA/Li2SO4 gel electrolyte offers high specific capacitance (263 F/g at 1 A/g), high-rage capacity (138 F/g at 50 A/g), cycling stability (capacitance retention of 99.4 % after 10000-cycle), energy density (146 W h Kg−1 at 996 W Kg−1 and 77 W h Kg−1 at 50181 W kg−1) and broad application prospect in wearable electronic devices.

SPION clusters with porous silica shell: Synthesis, core-shell structure, magnetic properties, biocompatibility and MRI application

SPION clusters with porous silica shell: Synthesis, core-shell structure, magnetic properties, biocompatibility and MRI application

Structure, properties and multifaceted food applications of four typical iron-binding proteins

Structure, properties and multifaceted food applications of four typical iron-binding proteins

Immunoglobulins: Structure, Function, and Therapeutic Applications in Immune Response

The article titled "Immunoglobulins and Its Applications" provides a comprehensive overview of the structure, function, and therapeutic significance of immunoglobulins, also known as antibodies. These vital proteins, produced by plasma cells in response to immunogens, are essential components of the immune system, playing a critical role in recognizing and neutralizing pathogens such as bacteria and viruses. The article elaborates on the five primary classes of immunoglobulins (IgA, IgD, IgE, IgG, and IgM), each distinguished by its unique structural and functional properties. It discusses the molecular composition of immunoglobulins, highlighting the significance of their variable and constant regions in antigen binding. The paper also explores the diverse immune functions mediated by these proteins, including precipitation, agglutination, and neutralization of antigens. Additionally, the article emphasizes the therapeutic applications of immunoglobulins, particularly in treating immune deficiencies and other diseases. Advances in monoclonal antibody technology have significantly expanded the utility of immunoglobulins in both research and medicine, leading to the development of innovative therapeutic approaches such as humanized monoclonal antibodies and bifunctional antibodies. This comprehensive analysis underscores the critical role of immunoglobulins in both natural immunity and therapeutic interventions