High Performance Research Articles

EB-Net: an efficient balanced network for accuracy and speed of remote sensing detection

ABSTRACT Remote sensing detection is a difficult task that requires not only identifying objects with complex background, quality, and angle issues, but also being lightweight enough to be carried by edge devices. In real remote sensing scenarios, achieving accurate, fast, and low-resource-consumption automated detection remains a significant challenge. Therefore, this paper proposes an efficient balanced network (EB-Net) for real remote sensing devices. First, a dynamic sparse attention (DSA) mechanism is proposed and has been proven with high performance via the complexity analysis. In addition, a new dynamic sparse transformer (DSFormer) is constructed using DSA, which enhances feature information and adapts to image resolution by self-attention and multi-headed attention, and achieves more flexible computation by random sampling. Then, three versions of discrete distribution IoU (DDIoU) are defined for adapting various scenarios and tasks in remote sensing, and this loss function makes the model achieve high accuracy and more lightweight. Finally, to make the model more lightweight, a cropped SPPF (CroSPPF) is presented, which significantly improves the computational efficiency by the lightweight of the sequence structure and activation function. Ablation experiments are conducted on the NWPU VHR-10 dataset, and demonstrate the effectiveness of the proposed methods. Numerous comparisons with state-of-the-art detectors are conducted on the NWPU VHR-10, RSOD and DOTA datasets. The experiments show that EB-Net outperforms state-of-the-art remote sensing detection models in comprehensive performance and achieves end-to-end accurate and lightweight detection.

Polymeric piezoelectric accelerometers with high sensitivity, broad bandwidth, and low noise density for organic electronics and wearable microsystems

Piezoelectric accelerometers excel in vibration sensing. In the emerging trend of fully organic electronic microsystems, polymeric piezoelectric accelerometers can be used as vital front-end components to capture dynamic signals, such as vocal vibrations in wearable speaking assistants for those with speaking difficulties. However, high-performance polymeric piezoelectric accelerometers suitable for such applications are rare. Piezoelectric organic compounds such as PVDF have inferior properties to their inorganic counterparts such as PZT. Consequently, most existing polymeric piezoelectric accelerometers have very unbalanced performance metrics. They often sacrifice resonance frequency and bandwidth for a flat-band sensitivity comparable to those of PZT-based accelerometers, leading to increased noise density and limited application potentials. In this study, a new polymeric piezoelectric accelerometer design to overcome the material limitations of PVDF is introduced. This new design aims to simultaneously achieve high sensitivity, broad bandwidth, and low noise. Five samples were manufactured and characterized, demonstrating an average sensitivity of 29.45 pC/g within a ± 10 g input range, a 5% flat band of 160 Hz, and an in-band noise density of 1.4 µg/Hz\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{{Hz}}$$\\end{document}. These results surpass those of many PZT-based piezoelectric accelerometers, showing the feasibility of achieving comprehensively high performance in polymeric piezoelectric accelerometers to increase their potential in novel applications such as organic microsystems.

Solution-Processed Metallic Nanowire Network for Wearable Transparent Thermal Radiation Shield.

Heating requirements for residential and commercial dwellings result in significant energy consumption and deleterious environmental effects. Personal radiative thermal management textiles regulate the wearer's body temperature by controlling the material's intrinsic optical properties. Passive heating textiles suppress radiative heat losses and therefore significantly reduce the energy consumption required for building heating systems. Guided by an optical theoretical approach, a transparent radiation shield (TRS) is designed based on silver nanowires (AgNWs) that can suppress human body heat with simultaneous visible light transmittance anticipated for practical fabrics. We experimentally demonstrated a TRS with large infrared light reflectance (low emissivity of 35%) and a visible (VIS) transparency value of 75% (400-800 nm). The results are well corroborated by the Mie scattering theory and the wire-mesh equivalent sheet impedance model, which provide fundamental mechanism understanding and guidance toward higher performance. The TRS is fabricated by a simple, solution-processing method with thermoplastic elastomer protective layers, granting notable stretching capabilities, mechanical robustness, and conformability to any body shape or object. The rigorous theoretical strategy enables the scalable synthesis of low-emissivity and visibly transparent textiles for personal thermal comfort.

Comparative Pharmacokinetic Study of Standard Astaxanthin and its Micellar Formulation in HealthyMale Volunteers.

Astaxanthin is a naturally occurring carotenoid with high anti-oxidant properties, but it is a very lipophilic compound with low oral bioavailability. This study was conducted to compare the pharmacokinetic parameters of a novel astaxanthin preparation based on micellar solubilization technology, NovaSOL® 400-mg capsules (Test product), and those of astaxanthin 400-mg capsules (reference product), after single oral dose administration to healthy male adults. A single oral dose (400 mg equivalent to 8 mg astaxanthin) of test and reference astaxanthin were administered with 240 mL of water to 12 volunteers according to crossover design, in two phases, with a washout period of 1week in between. Blood samples were collected at hourly intervals for the first 12 h, then at 24.0, 48.0, and 72.0 h after administration. Aliquots of plasma were centrifuged and the clear supernatant was injected into the high performance liquid chromatography-diode array detection (HPLC-DAD) system. Plasma concentration of astaxanthin versus time profiles were constructed, and the primary pharmacokinetic parameters, maximum concentration (Cmax), area under concentration time curve from time of administration (0) to time (t) [AUC0-t] or to infinity ∞, [AUC0-∞], half-life (T½) and time to reach Cmax (Tmax)were calculated. The test micellar astaxanthin reached a Cmax of 7.21 µg/ml after 3.67 h compared to only 3.86 µg/ml after 8.5 h for the reference native astaxanthin. Micellar formulation of astaxanthin is capable of producing a high concentration of astaxanthin in plasma in a shorter time, thereby expected to provide faster potential therapeutic efficacy.

Highly-integrable analogue reservoir circuits based on a simple cycle architecture

Physical reservoir computing is a promising solution for accelerating artificial intelligence (AI) computations. Various physical systems that exhibit nonlinear and fading-memory properties have been proposed as physical reservoirs. Highly-integrable physical reservoirs, particularly for edge AI computing, has a strong demand. However, realizing a practical physical reservoir with high performance and integrability remains challenging. Herein, we present an analogue circuit reservoir with a simple cycle architecture suitable for complementary metal-oxide-semiconductor (CMOS) chip integration. In several benchmarks and demonstrations using synthetic and real-world data, our developed hardware prototype and its simulator exhibit a high prediction performance and sufficient memory capacity for practical applications, showing promise for future applications in highly integrated AI accelerators.

Experimental design and analysis of advanced three phase converter for PV application with WCO-P&O MPPT controller

Photovoltaic (PV)-based power generation systems are becoming increasingly popular as a due to its high performance and cleanliness. Several factors influence the performance of a PV system, including shadowing effects. PV systems employ MPPT methodologies to obtain the power from PV array. Conventional MPPTs works well under normal conditions when there is no shadow effects or partial shading. The presence of partial shading affects the system performance and generates several power peaks. This complicates the process of finding out of the global peak (GMPP) with improved tracking efficiency and reduced settling time including conversion efficiency. This work proposes three hybrid MPPT techniques: Water Cycle Optimisation-Perturb and Observe (WCO-PO), Artificial Neural Network Supported Adaptable Stepped-Scaled Perturb and Observe (ANN-ASSPO), Grey Wolf Optimisation-Modified Fast Terminal Sliding Mode Controller (GWO-MFTSMC), and two conventional MPPT techniques WCO and P&O have been implemented. The proposed system utilizes interleaved boost converter with three phase. The performances of proposed hybrid MPPTs strategies were compared in terms of output voltage, output current and extracted power. The comparison also includes conversion efficiency and average settling time. To analyse the performances, four different cases have been used to test the efficacy of hybrid MPPTs under changing climatic conditions. The MATLAB/Simulink tool has been used to analyze the PV system performances. In the three hybrid MPPT techniques, WCO-PO has performed better when compared to other two hybrid MPPTs in terms of conversion efficiency (99.56%) and settling time (1.4 m).

Efficient Capture and Electroreduction of Dilute CO2 into Highly Pure and Concentrated Formic Acid Aqueous Solution.

High-purity CO2 rather than dilute CO2 (15 vol %, CO2/N2/O2 = 15:80:5, v/v/v) similar to the flue gas is currently used as the feedstock for the electroreduction of CO2, and the liquid products are usually mixed up with the cathode electrolyte, resulting in high product separation costs. In this work, we showed that a microporous conductive Bi-based metal-organic framework (Bi-HHTP, HHTP = 2,3,6,7,10,11-hexahydroxytriphenylene) can not only efficiently capture CO2 from the dilute CO2 under high humidity but also catalyze the electroreduction of the adsorbed CO2 into formic acid with a high current density of 80 mA cm-2 and a Faradaic efficiency of 90% at a very low cell voltage of 2.6 V. Importantly, the performance in a dilute CO2 atmosphere was close to that under a high-purity CO2 atmosphere. This is the first catalyst that can maintain exceptional eCO2RR performance in the presence of both O2 and N2. Moreover, by using dilute CO2 as the feedstock, a 1 cm-2 working electrode coating with Bi-HHTP can continuously produce a 200 mM formic acid aqueous solution with a relative purity of 100% for at least 30 h in a membrane electrode assembly (MEA) electrolyzer. The product does not contain electrolytes, and such a highly concentrated and pure formic acid aqueous solution can be directly used as an electrolyte for formic acid fuel cells. Comprehensive studies revealed that such a high performance might be ascribed to the CO2 capture ability of the micropores on Bi-HHTP and the lower Gibbs free energy of formation of the key intermediate *OCHO on the open Bi sites.

3D printing of reversible solid oxide cell stacks for efficient hydrogen production and power generation

Renewable hydrogen offers a promising pathway to address the challenge of large-scale chemical energy storage in the transition to a decarbonized future. The most efficient devices to convert renewable electricity into hydrogen, and vice versa, are reversible solid oxide cells (SOC-s). Despite recent advances in materials performance and durability, conventional ceramic manufacturing technologies strongly limit the huge potential of SOC-s, imposing severe geometrical restrictions that penalize the overall system efficiency. Here we capture the benefits of using previously unexplored complex-shapes by scaling 3D-printed reversible solid oxide cells with improved mechanical properties, higher performance, and embedded functionality. Stacks made of 45 cm2-3D-printed electrolyte-supported solid oxide cells combined with ultrathin flat metallic interconnects were successfully fabricated and operated in fuel cell (SOFC) and electrolysis (SOEC) modes delivering up to 85 L/h of hydrogen production while presenting less than 5 % degradation after more than 500 h of operation. The advances presented here demonstrate the viability of upscaling the process of solid-oxide-cell 3D printing and brings advantageous stack designs aimed for a cost-competitive and customizable hydrogen technologies deployment. In particular, the enhancement by design achieved here provides innovative SOC stacks with volumetric and gravimetric power densities that are three and four times higher, respectively, than their planar counterparts using the same set of materials.

N-Doping CQDs as an Efficient Fluorescence Probe Based on Dynamic Quenching for Determination of Copper Ions and Alcohol Sensing in Baijiu.

To address an accurate detection of heavy metal ions in Baijiu production, a nitrogen-doping carbon quantum dots (N-CQDs) was prepared by hydrothermal method from citric acid and urea. The as-prepared N-CQDs had an average particle size of 2.74 nm, and a large number of functional groups (amino, carbonyl group, etc.) attached on its surface, which obtained a 9.6% of quantum yield (QY) with relatively high and stable fluorescence performance. As a fluorescent sensor, the fluorescence of N-CQDs at 380 nm excitation wavelength could be quenched quantitatively by adding Cu2+, due to the dynamic quenching of electron transfer caused by the binding of amine groups and Cu2+, which showed excellent sensitivity and selectivity to Cu2+ in the range of 0.5-5 μM with a detection limit (LOD) of 0.032 μM. In addition, the N-CQDs as well as could be applied to quantitative determine alcohol content in the range of 10-80 V/V% depending on the fluorescence enhancement. Upon the experiment, the fluorescent mechanism was studied by Molecular dynamics (MD) simulations, which demonstrated that solvent effect played an influential role on sensing alcohol content in Baijiu. Overall, the work provided a theoretically guide for the design of fluorescence sensors to monitor heavy metal ion in liquid drinks and sense alcohol content.

ESG-driven ecopreneur selection in European financial institutions: entropy and TOPSIS analysis

PurposeThis is the application of the Entropy and TOPSIS model to assess the eco-efficiency of European financial institutions using environmental, social, and governance (ESG) strategies. The aim is to categorize financial institutions based on key factors such as environmental training and management and to examine the alignment between ideal ESG performance and eco-efficiency.Design/methodology/approachThe study uses environmental, social, and governance (ESG) strategies to identify and categorize eco-entrepreneurs in European financial institutions. The study utilizes data to examine the structure between environmental training, effective management practices, and the green performance of financial institutions.FindingsThe study shows that European financial institutions exhibit varying degrees of eco-efficiency as assessed using the Entropy and TOPSIS model applied to ESG strategies. Surprisingly, the study found that institutions with a high ESG performance do not always match those with the highest eco-efficiency.Research limitations/implicationsThey emphasize the need for financial institutions to align their operations with sustainable practices. This research provides insights to increase eco-efficiency and improve the ESG performance of financial institutions. It also informs policy and decision-making in these institutions in relation to environmental training and management practices, contributing to the wider dialogue on sustainable finance.Originality/valueThis indicates a discrepancy between ESG ratings and actual eco-efficiency, emphasizing the need to reassess the ESG framework. The study findings are crucial for aligning financial institutions with sustainable practices and improving the effectiveness of the ESG framework, especially for institutions at the lower end of the eco-efficiency spectrum.

Machine-learning developed an iron, copper, and sulfur-metabolism associated signature predicts lung adenocarcinoma prognosis and therapy response

BackgroundPrevious studies have largely neglected the role of sulfur metabolism in LUAD, and no study has combine iron, copper, and sulfur-metabolism associated genes together to create prognostic signatures.MethodsThis study encompasses 1564 LUAD patients, 1249 NSCLC patients, and over 10,000 patients with various cancer types from diverse cohorts. We employed the R package ConsensusClusterPlus to separate patients into different ICSM (Iron, Copper, and Sulfur-Metabolism) subtypes. Various machine-learning methods were utilized to develop the ICSMI. Enrichment analyses were conducted using ClusterProfiler and GSVA, while IOBR quantified immune cell infiltration. GISTIC2.0 and maftools were utilized for CNV and SNV data analysis. The Oncopredict package predicted drug information based on GDSC1. TIDE algorithm and cohorts GSE91061 and IMvigor210 evaluated patient response to immunotherapy. Single-cell data was processed using the Seurat package, AUCell package calculated cells geneset activity scores, and the Scissor algorithm identified ICSMI-associated cells. In vitro experiments was conducted to explore the role of ICSMRGs in LUAD.ResultsUnsupervised clustering identified two distinct ICSM subtypes of LUAD, each with unique clinical characteristics. The ICSMI, comprising 10 genes, was constructed using integrated machine-learning methods. Its prognostic power was validated in 10 independent datasets, revealing that LUAD patients with higher ICSMI levels had poorer prognoses. Furthermore, ICSMI demonstrated superior predictive abilities compared to 102 previously published signatures. A nomogram incorporating ICSMI and clinical features exhibited high predictive performance. ICSMI positively correlated with patients gene mutations, and integrated analysis of bulk and single-cell transcriptome data revealed its association with TME modulators. Cells representing the high-ICSMI phenotype exhibited more malignant features. LUAD patients with high ICSMI levels exhibited sensitivity to chemotherapy and targeted therapy but displayed resistance to immunotherapy. In a comprehensive analysis across various cancers, ICSMI retained significant prognostic value and emerged as a risk factor for the majority of cancer patients.ConclusionsICSMI provides critical prognostic insights for LUAD patients, offering valuable insights into the tumor microenvironment and predicting treatment responsiveness.

Sono-assisted photocatalytic degradation of ciprofloxacin in aquatic media using g-C3N4/MOF-based nanocomposite under visible light irradiation.

This research study is centered on the sono-assisted photocatalytic degradation of a well-known antibiotic (ciprofloxacin; CIP) in aquatic media using a g-C3N4/NH2-UiO-66 (Zr) catalyst under visible light irradiation. Initially, the catalyst was prepared by a simple method, and its physiochemical features were thoroughly analyzed by XRD, FT-IR, FE-SEM, EDX, EDS-Dot-Mapping, and UV-Vis analytical techniques. After that, the impact of several influential factors affecting the performance of the applied sono-assisted photocatalytic process such as the initial concentration of CIP, solution pH, catalyst dosage, light intensity, and ultrasound power was fully assessed, and the optimal conditions were established. After 75min of the sono-assisted photocatalytic treatment, the complete degradation of CIP (10mg/L) was accomplished under the condition as follows: g-C3N4/NH2-UiO-66 (Zr), 0.6g/L; pH, 5.0, and ultrasound power, light intensity 75 mw/cm2, 200 W/m2. Meanwhile, the photocatalytic degradation of CIP followed the pseudo-first-order kinetic model. In addition, the scavenger experiments demonstrated that OH˚ and O2°- radicals played a key role in the sono-assisted photocatalytic degradation process. It is also acknowledged that the applied catalyst was reused for five consecutive runs with a minor loss observed in its degradation efficiency. In a further experiment, a significant synergistic effect with regard to the degradation of CIP was observed once all three major parameters (visible light, ultrasound waves, and catalyst) were used in combination compared to each used alone. To sum up, it is thought that the integration of g-C3N4/MOF-based catalyst, ultrasound waves, and visible light irradiation could be potentially applied as a promising strategy for the degradation of various pharmaceuticals on account of high degradation performance, simple operation, excellent reusability, and eco-friendly approach.

Deep learning with diffusion MRI as in vivo microscope reveals sex-related differences in human white matter microstructure

Biological sex is a crucial variable in neuroscience studies where sex differences have been documented across cognitive functions and neuropsychiatric disorders. While gross statistical differences have been previously documented in macroscopic brain structure such as cortical thickness or region size, less is understood about sex-related cellular-level microstructural differences which could provide insight into brain health and disease. Studying these microstructural differences between men and women paves the way for understanding brain disorders and diseases that manifest differently in different sexes. Diffusion MRI is an important in vivo, non-invasive methodology that provides a window into brain tissue microstructure. Our study develops multiple end-to-end classification models that accurately estimates the sex of a subject using volumetric diffusion MRI data and uses these models to identify white matter regions that differ the most between men and women. 471 male and 560 female healthy subjects (age range, 22–37 years) from the Human Connectome Project are included. Fractional anisotropy, mean diffusivity and mean kurtosis are used to capture brain tissue microstructure characteristics. Diffusion parametric maps are registered to a standard template to reduce bias that can arise from macroscopic anatomical differences like brain size and contour. This study employ three major model architectures: 2D convolutional neural networks, 3D convolutional neural networks and Vision Transformer (with self-supervised pretraining). Our results show that all 3 models achieve high sex classification performance (test AUC 0.92–0.98) across all diffusion metrics indicating definitive differences in white matter tissue microstructure between males and females. We further use complementary model architectures to inform about the pattern of detected microstructural differences and the influence of short-range versus long-range interactions. Occlusion analysis together with Wilcoxon signed-rank test is used to determine which white matter regions contribute most to sex classification. The results indicate that sex-related differences manifest in both local features as well as global features / longer-distance interactions of tissue microstructure. Our highly consistent findings across models provides new insight supporting differences between male and female brain cellular-level tissue organization particularly in the central white matter.

Natural fiber-based multifunctional aerogel with advanced oil absorption property and high thermal insulation performance

Designing an ultra-light and controllable structure for multifunctionality of low-cost aerogels is of great significance but remains a huge challenge because of the limitations of structural processability and mechanical vulnerability. Herein, we demonstrated a facile approach to synthesize a low-cost, ultralight, elastic, and highly recyclable superabsorbent from renewable fibers via environmentally friendly physical bonding and freeze-drying. Our approach introduces skin-core structure polyester (SPFs) into natural agriculture plant kapok, through which fibers entanglement and bonding to construct aerogel with adjustable hierarchical structure and desirable shapes. The resultant composite aerogel can be easily modified by organosilane, demonstrating durable superhydrophobicity (158.5°), advanced absorption capacity (128.4 g/g), good mechanical performance, self-cleaning, and thermal insulating (0.020 W/(m·K)). Therefore, the sustainable and multifunctional composite fibrous porous aerogels are potential materials for oil absorption applications and thermal management.

Glutamic Acid Induced Proton Substitution of Sodium Vanadate Cathode Promotes High Performance in Aqueous Zinc‐Ion Batteries

Glutamic Acid Induced Proton Substitution of Sodium Vanadate Cathode Promotes High Performance in Aqueous Zinc‐Ion Batteries

Rationalize the High Performance of Lithium Sorbents Derived from Gibbsite Guided by Phase Chemistry

Rationalize the High Performance of Lithium Sorbents Derived from Gibbsite Guided by Phase Chemistry

Recognizing online video genres using ensemble deep convolutional learning for digital media service management

It's evident that streaming services increasingly seek to automate the generation of film genres, a factor profoundly shaping a film's structure and target audience. Integrating a hybrid convolutional network into service management emerges as a valuable technique for discerning various video formats. This innovative approach not only categorizes video content but also facilitates personalized recommendations, content filtering, and targeted advertising. Given the tendency of films to blend elements from multiple genres, there is a growing demand for a real-time video classification system integrated with social media networks. Leveraging deep learning, we introduce a novel architecture for identifying and categorizing video film genres. Our approach utilizes an ensemble gated recurrent unit (ensGRU) neural network, effectively analyzing motion, spatial information, and temporal relationships. Additionally,w we present a sophisticated deep neural network incorporating the recommended GRU for video genre classification. The adoption of a dual-model strategy allows the network to capture robust video representations, leading to exceptional performance in multi-class movie classification. Evaluations conducted on well-known datasets, such as the LMTD dataset, consistently demonstrate the high performance of the proposed GRU model. This integrated model effectively extracts and learns features related to motion, spatial location, and temporal dynamics. Furthermore, the effectiveness of the proposed technique is validated using an engine block assembly dataset. Following the implementation of the enhanced architecture, the movie genre categorization system exhibits substantial improvements on the LMTD dataset, outperforming advanced models while requiring less computing power. With an impressive F1 score of 0.9102 and an accuracy rate of 94.4%, the recommended model consistently delivers outstanding results. Comparative evaluations underscore the accuracy and effectiveness of our proposed model in accurately identifying and classifying video genres, effectively extracting contextual information from video descriptors. Additionally, by integrating edge processing capabilities, our system achieves optimal real-time video processing and analysis, further enhancing its performance and relevance in dynamic media environments.

Color matching of bicomponent (PET/PTT) filaments with high performances using genetic algorithm

In recent years, a great interest has focused on the use of bicomponent filaments in several high-performance textile articles such as swimwear, sportswear and even high-quality denim. To dye fabrics containing these filaments, it is necessary to establish appropriate dye recipes allowing to obtain desired shades. In this article, we developed a genetic algorithm to optimize the color matching step of these bicomponent filaments, especially (PET/PTT) filaments. Three disperse dyes with different molecular weights were used for dyeing. The objective is to reproduce the reference color by choosing the appropriate disperse dyes among the available dyestuffs and their corresponding quantities to use on the mixture. For modeling, two sets of parameters (lied to the color formulation problem and the genetic algorithm), the objective function as well as the different stages of the algorithm were defined and described. In addition, different techniques of selection and mutation were applied and evaluated. The optimization criterion is to reduce the CMC color difference between the desired reference colors and the colors proposed by the algorithm. The developed algorithm showed good performances with very small color differences. The results indicate that the roulette wheel selection technique outperforms both rank and uniform selection methods. Moreover, employing a simple mutation strategy yields favorable outcomes with CMC color differences all lower than 1.

Self-Assembled Hydrogel Membranes with Structurally Tunable Mechanical and Biological Properties.

Using supramolecular self-assembled nanocomposite materials made from protein and polysaccharide components is becoming more popular because of their unique properties, such as biodegradability, hierarchical structures, and tunable multifunctionality. However, the fabrication of these materials in a reproducible way remains a challenge. This study presents a new evaporation-induced self-assembly method producing layered hydrogel membranes (LHMs) using tropocollagen grafted by partially deacetylated chitin nanocrystals (CO-g-ChNCs). ChNCs help stabilize tropocollagen's helical conformation and fibrillar structure by forming a hierarchical microstructure through chemical and physical interactions. The LHMs show improved mechanical properties, cytocompatibility, and the ability to control drug release using octenidine dihydrochloride (OCT) as a drug model. Because of the high synergetic performance between CO and ChNCs, the modulus, strength, and toughness increased significantly compared to native CO. The biocompatibility of LHM was tested using the normal human dermal fibroblast (NHDF) and the human osteosarcoma cell line (Saos-2). Cytocompatibility and cell adhesion improved with the introduction of ChNCs. The extracted ChNCs are used as a reinforcing nanofiller to enhance the performance properties of tropocollagen hydrogel membranes and provide new insights into the design of novel LHMs that could be used for various medical applications, such as control of drug release in the skin and bone tissue regeneration.

Uncovering the Potential of Industrial Waste: Turning Discarded Resources into Sustainable Advanced Materials

Abstract To identify solutions for utilizing industrial wastes (slag, sludge, fly ash, and bottom ash) in the development of pro-environmental systems, this paper reviews existing literature and explores material characterization methods. A review analysis was conducted to uncover their potential, and it was found that many utilizations can be made to recycle them into sustainable materials. Structure characterization, optical-dielectric function, and morphology analysis were analyzed to confirm the recent review. Structural properties are represented by crystallite size and crystallinity, where nickel slag shows the smallest distribution (55±10) nm with the highest crystallinity (25.42%), indicating its association with uniformity of response to external disturbances and micro-structural stability. From the analysis of optical and dielectric functions via Kramers Kronig relation, the widening of ∆(LO-TO) was shown by fly ash and slag indicating stable bonding suitable as x-ray shielding and sensor, while high electron loss functions were shown by sludge and bottom ash indicating high potential electronic transition performance. Based on their chemical and physical properties, these industrial wastes are not limited as construction materials and can even be upgraded to more advanced development materials.