Communications in Science and Technology
Communications in Science and Technology [p-ISSN 2502-9258 | e-ISSN 2502-9266] is an international open access journal devoted to various subjects including natural science, medicine, technology, and engineering. CST publishes research articles, reviews, and letters in all areas of the aforementioned disciplines. The journal aims to provide a comprehensive source of information on recent developments in the field. The emphasis will be on publishing quality articles rapidly and making them freely available to researchers worldwide. All accepted manuscripts will be indexed in Scopus, DOAJ, EBSCO, Google Scholar, SINTA. To provide the maximum exposure to the articles, the indexation in WoS will be provided in the future. The journal will be important reading for scientists and researchers who wish to keep up with the latest developments in the field.
Komunitas Ilmuwan dan Profesional Muslim Indonesia
Copyright Open Access authors retain the copyrights of their papers, and all open access articles are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided that the original work is properly cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations. While the advice and information in this journal are believed to be true and accurate on the date of its going to press, neither the authors, the editors, nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. This work is licensed under a Creative Commons Attribution 4.0 International License.
Ytterbium-doped TeO2-ZnO-Na2O glasses: Insights into physical, mechanical, and optical properties
This present study constitutes a systematic investigation imto the influence of low-concentration Yb2O3 incorporation on the physical, mechanical, and optical properties of TeO2 ZnO Na2O (TZN) glasses. The glasses under consideration are composed of 65TeO2-(30-x)ZnO-5Na2O- xYb2O3 (x = 0-2.5 mol%). The fabrication process utilized the conventional melt-quenching technique. The present study specifically addresses the limited understanding of how minor Yb3+ incorporation affects the structure-property relationships in TZN glass systems. The results obtained demonstrate progressive densification of the glass network in the presence of increasing Yb2O3 content. This is evidenced by an increase in density from 5.15 to 5.62 g cm-3 and a corresponding decrease in molar volume. This phenomenon is correlated to enhanced network connectivity and it was attributed to the alteration of Non-Bridging Oxygen (NBO) to Bridging Oxygen (BO) linkages. Consequently, an enhancement in mechanical properties is observed with increasing both Young’s (50.67 to 52.61 GPa) and Bulk (29.77 to 31.89 GPa) modulus, respectively. It is suggested that these factors result in enhanced glass rigidity and durability. The optical investigation demonstrates a broadening of the optical band gap from 3.01 to 3.30 eV. This suggests the presence of a denser glass network and declined glass electronic polarizability. The findings of the study indicated that the integration of low-level Yb2O3 into TZN-based glasses can result in a modification of the glasses properties, thereby highlighting the potential for advanced application in the domain of photonics.
Copyright (c) 2026 Communications in Science and Technology http://creativecommons.org/licenses/by/4.0
Hollow mesoporous silica nanoparticles functionalized with Pluronic for controlled drug release
In recent years, innovative drug-delivery systems have been considered one of the most crucial techniques for enhancing cancer treatment. In this study, F127, a thermal-sensitive polymer, was utilized to conjugate to the surface of HMSN, and subsequently, the carriers were loaded with Dox. The obtained product was characterized by a series of experiments, including TEM, FT-IR, TGA, XRD, and zeta potential. The HMSN-F127 carrier system was successfully synthesized with a size of 152.9 ± 0.9 nm. Furthermore, the SEM results revealed that the pores were located within the material. The zeta potential was determined to be -4.03 ± 0.15 mV, with a corresponding surface area of 168.4 m2/g. The efficiency and drug loading capacity of HMSN-F127 were found to be higher than those of naked HMSN with DLE and DLC values of 72.08 ± 0.09 % and 11.09 ± 0.01 %, respectively. In particular, Dox was well controlled by HMSN-F127. At concentrations ranging from 0 - 250 μg/ml, HMSN-F127 was not toxic to HCC J5 cells. The findings demonstrate that the HMSN-F127 system provides an effective platform for enhancing drug loading and achieving controlled drug release, thereby highlighting its potential for improved anticancer drug delivery and future development of stimuli-responsive nanocarriers.
Mission-level energy efficiency optimization for multi-UAV data collection using a genetic algorithm
The efficient utilization of limited onboard energy remains a fundamental challenge in cooperative multi-UAV data collection missions. Existing routing approaches typically optimize surrogate objectives, such as travel distance or aggregate energy consumption, which do not directly reflect mission effectiveness. The present paper proposes a Mission-level Energy-aware Genetic Algorithm (ME-GA) that directly maximizes mission-level energy efficiency, defined as the ratio of successfully delivered sensing data to total energy consumption. The proposed framework integrates a mission-level simulator into the fitness evaluation, explicitly modeling UAV propulsion, sensing, data buffering, wireless communication, and return-to-base feasibility under energy constraints. Extensive simulations involving up to 9 UAVs and 100 Points of Interest (PoIs) under both grid and random spatial layouts demonstrate that ME-GA consistently achieves high and stable energy efficiency while maintaining near-complete task satisfaction and high data delivery reliability. In comparison to GA-based baselines, the proposed approach enhances energy efficiency by approximately 5–15% across the evaluated scenarios along with a reduction in total travel distance by up to 40% in larger fleet sizes. Overall, the results demonstrate that mission-level energy efficiency serves as a unified and physically meaningful objective for multi-UAV optimization, enabling robust and scalable performance across diverse operational scenarios.
The effect of pH on anthocyanin extraction from Clitoria ternatea L. and polyetherimide polymer membrane electrolyte on the efficiency of dye-sensitized solar cells (DSSCs)
This present study investigates the effect of pH variation (2-12) on anthocyanin extraction from Clitoria ternatea L. and polyetherimide (PEI) polymer membrane electrolyte performance in dye-sensitized solar cells (DSSCs). The extraction of anthocyanin was conducted through the utilization of microwave-assisted extraction (MAE) at 280 watts for 15 minutes, employing a distilled water ratio of 1:20 ratio. This was followed by a systematic pH conditioning procedure. The characterization employed a range of analytical techniques, including UV-Vis spectrophotometry (400-800 nm), FTIR (4000-500 cm-1), cyclic voltammetry for HOMO-LUMO analysis, SEM (1,000×-10,000× magnification), XRD for crystallinity determination, DSC for thermal stability (60-450°C), and electrochemical impedance spectroscopy. Results obtained demonstrated that pH 4 anthocyanin exhibited maximum dual absorption peaks at 571.21 nm and 612.85 nm, representing the magenta-colored quinoidal base structure with superior light-harvesting capabilities. The FTIR analysis confirmed the presence of stable functional groups, including O-H stretching (3338.08 cm-1), C=O stretching (1710 cm-1), and aromatic C=C (1416.91 cm-1) across all pH conditions without new chemical bond formation. The pH 4 dye demonstrated the narrowest energy bandgap (0.1316 eV) with HOMO at -4.1597 eV and LUMO at -4.0281 eV, optimally aligned with the TiO2 conduction band (-4.0 eV) for efficient electron injection. The PEI membrane exhibited asymmetric morphology with 12.77% crystallinity, a hierarchical porous structure, and excellent thermal stability up to 500°C. The performance of the DSCC reached its maximum at a pH of 4, with efficiency η = 2.37%, Voc = 597 mV, Jsc = 0.0119 mA/cm2, FF = 5.60%, and minimum charge transfer resistance Rct = 100–150 Ω. These findings demonstrate that pH 4 optimization is critical for enhancing the efficiency of DSSC through quinoidal base formation, enhanced molecular conjugation, and accelerated charge transfer processes in environmentally sustainable photovoltaic systems.
Statistical characteristics of time series temperature in cooling the electric vehicle controller using the bubble generator
The present study examines the thermal performance of the cooling system in an electric vehicle controller. This is achieved by injecting bubbles into the water block at various input airflow rates, using quiet-water and circulating-water cooling fluids. The thermal performance of a system is determined by two factors: the maximum temperature at the controller-water block interface and the corresponding thermal resistance. The temperature distribution is analyzed using statistical moments, the Probability Density Function (PDF), and Kolmogorov entropy to determine the irregularity of the temperature signal. The findings indicate that an increase in airflow rate can result in a decrease in maximum temperature and an enhancement in thermal resistance and flatness of the PDF curve. The maximum temperature decreases by 15.5% when the cooling medium is quiet water when compared to bubble injection at an airflow rate of Qa = 1.5 lpm. The bubble-cooling fluid, with an airflow rate of Qa = 1.5 lpm has a relatively uniform entropy across all thermocouple positions. The presence of low Kolmogorov entropy and low PDF flatness indicate a more stable controller response and fewer extreme spikes. In turn, it has an impact on the increasing system reliability and extending the durability of the controller's electronic components.
Recombinant production and characterization of the integrin-binding LERGDT peptide
Chronic kidney disease (CKD) remains a global health burden, necessitating the development of scalable therapeutic interventions. While the LERGDT peptide has previously demonstrated integrin-binding and anti-fibrotic activity, its transition toward clinical applications is hindered by the high cost of large-scale chemical synthesis. This study presents a robust bioprocess for the recombinant production of a 20-repeat tandem LERGDT construct (PEP-1) using an Escherichia coli expression system. To ensure structural stability and protection against proteolytic degradation, PEP-1 was engineered as a fusion protein with N-terminal ketosteroid isomerase (KSI) and a C-terminal six-histidine tag. In silico analysis using AlphaFold and HADDOCK predicted high structural stability and specific binding affinity to integrins. The bioprocess system achieved a significant total protein yield of 52.94 mg.L-1 , with the fused protein purified via nickel-affinity chromatography and successfully cleaved into functional fragments (hPEP-1) using hydroxylamine hydrolysis. Product identity was confirmed by means of SDS-PAGE at ~35 kDa and validated by using tandem LC-MS. ELISA-based assays confirmed that both the fusion protein and released fragments maintained specific integrin-binding functionality. By establishing a high-yield, reproducible production protocol, this work provides a cost-effective alternative to synthetic methods, facilitating large-scale testing required for peptide-based CKD therapeutics. This scalable bioprocess accelerates the translation of LERGDT-based interventions from bench to bedside, enhancing the accessibility of affordable long-term treatments for CKD patients.
Separation of heavy metal pollutants by micellar-enhanced ultrafiltration membrane system and its surfactant recovery
Micellar-enhanced ultrafiltration (MEUF) is an effective treatment for treating heavy metal effluents. However, excessive use of synthetic surfactants leads to more serious problems, thereby highlighting the significance of surfactant recovery. This present study focuses on the recovery of surfactants while preserving their capacity to entrap metal ions. In this study, the acidification method was utilized to dissociate metal ions (Cu2+ and Cd2+) from the surfactant micelles. The findings demonstrate that the lowest critical micelle concentration found at pH 1, with 91% and 87% of the surfactant recovered for Cu2+ and Cd2+ system, respectively. The spontaneous occurrence of acidic surfactant micellization was thermodynamically validated, as evidenced by negative ΔGm values. Furthermore, the recovered surfactant exhibits high level of rejection and indicates an intermediate-blocking mechanism (R2>0.99). The findings highlight acid-assisted MEUF as a scalable approach for efficient metal removal and surfactant recovery, in turn, reducing chemical consumption and environmental impact.
Structure-conditioned vulnerability of road networks under flood and landslide disruptions: evidence from Central Java, Indonesia
The vulnerability of road networks under disaster conditions is typically discussed in terms of hazard exposure. However, the impact of network structure on the consequence of disruption remains an under-explored area. The present paper examines the effect of road network structure on the degradation of connectivity under disruptions caused by floods and landslides. This is achieved through a graph-based analytical framework, with a representative empirical testbed: the primary road network of Central Java, Indonesia. The network is modeled as a graph and the segments at risk of hazard exposure are identified using spatial overlay. These segments are then systematically deleted based on hazard type, hazard level and spatial localization scenarios. The performance of connectivity is measured based on the largest connected component, the average shortest path length and global efficiency. These measurements are taken both with and without the integration of toll roads. The results obtained demonstrated the presence of disruption signatures, each of which is unique to each hazard. Despite the considerable spatial extent of disruptions, the disruptions caused by flooding result in a gradual degradation of the network in view of redundancy. Conversely, landslide-induced disruptions are spatially small, but they have a disproportionate effect on structurally critical links, thereby causing fragmentation. In addition, the integration of toll roads does not necessarily enhance the robustness of the network when dealing with disruption, indicating an alteration in the structural dependency of the network. These findings support a reframing of road network vulnerability as a structure-conditioned response to disruption, highlighting the significance of structural robustness in infrastructure planning beyond conventional connectivity-based assessments, thereby supporting more effective infrastructure planning and risk mitigation strategies.
Global research trends in biodiesel production from palm fatty acid distillate (PFAD) using CaO, MgO, and TiO2 heterogeneous catalysts: a bibliometric analysis (2015-2025)
This bibliometric study provides a comprehensive of global research on biodiesel production from palm fatty acid distillate (PFAD) using heterogeneous CaO, MgO, and TiO2 catalysts during the period of 2015–2025. A total of 179 Scopus-indexed documents were analyzed by means of VOSviewer to examine publication trends, citation impact, co-authorship networks, and keyword co-occurrence. It is evident that Malaysia, Thailand, and Indonesia dominate both the publications and citations, reflecting their dominant palm oil production capacity. A substantial proportion of publications is concentrated on high-impact journals in the fields of energy and chemical engineering. A keyword clustering analysis reveals five main themes: process optimization, catalyst synthesis (including waste-derived CaO), feedstock diversification, activation/intensification techniques, and sustainability. The results demonstrate a predominance of laboratory-scale studies employing waste-based oxides, with a paucity of research addressing pilot-scale implementation, techno-economic assessment, and life cycle analysis. The findings provide a strategic roadmap for researchers, policymakers, and industry stakeholders to prioritize future developments in PFAD-based biodiesel production, particularly in catalyst scale-up, techno-economic assessment, and sustainable process integration. The primary benefit of this study is its capacity to systematically consolidate a decade of disparate research into a structured analytical framework, thereby enabling more targeted investment in under-explored areas such as pilot-scale implementation and life cycle analysis. In turn, this is expected to accelerate the transition of PFAD-based biodiesel from laboratory research to industrial application.
Integrated GEE–ERT–XRF framework for detecting in-situ rare oxide formation in tropical lowland clays
Understanding in-situ rare oxide formation in tropical lowlands remains challenging due to the extensive peat–clay cover and the limited surface accessibility. This study presents a reproducible integrated workflow combining cloud-based hydrographic analysis in Google Earth Engine (GEE), two-dimensional electrical resistivity tomography (ERT), and X-ray fluorescence (XRF) geochemistry to investigate rare oxide occurrence in South Kalimantan, Indonesia. MERIT Hydro data that had been processed within the GEE framework were utilized for the delineation of buried palaeochannel traces. This was followed by ERT profiling and core drilling to characterize the subsurface lithology. XRF analyses indicate Yb2O3 concentrations of 0.01–0.04 wt% and Re2O7 of 0.00–0.08 wt% within clay layers at approximately 3–4 m depth. The results of spatial correlation analysis demonstrate weak relationships between oxide distribution and palaeochannel proximity (|r| < 0.3) but strong positive relationship between resistivity and oxide concentrations (r > 0.75). The results obtained lend significant support to an in-situ formation model, primarily controlled by lithological and geochemical processes as opposed to fluvial transport. The proposed GEE–ERT–XRF workflow offers a preliminary operational framework for detecting subtle, clay-hosted rare oxide signatures in data-limited tropical lowland environments. The findings demonstrate that efficacy of subsurface resistivity as a proxy for identifying geochemical trapping horizons associated with rare oxide enrichment beneath peat–clay cover. The proposed workflow further provides a cost-effective, scalable, and reproducible approach for early-stage mineral exploration and subsurface resource screening in tropical lowland regions where conventional geological mapping is limited by poor surface exposure.
A review of technology evolution and risk of autonomous vehicles
The development of autonomous vehicles (AVs) have become a prominent research topic; however, many survey studies focus either on enabling technologies or on isolated risk issues. This approach therefore provides limited insight into how both dimensions of AV development evolve together. The present study employs scientometric analysis of Scopus-indexed journal articles to map the knowledge base of AV technology evolution and risk. The results of the study highlight influential documents and productive countries, identify major research clusters using the log-likelihood ratio (LLR), and reveal thematic shifts across three periods (early, middle, and late). To strengthen the analytical contribution, the revised manuscript synthesizes the interaction between technology phases, dominant methods, associated risks, and corresponding research responses. The findings indicate that early AV studies emphasized autonomy and dynamics. However, there was a subsequent shift towards systems, control, and learning. Moreover, there has been an increasing convergence with risk themes such as cybersecurity, safety assessment, and anomaly detection. These findings of this study offer a more integrated understanding of the co-evolution of AV research and indicate priority challenges for the safe deployment of learning-based methods. Ultimately, the insights provided in this review offer a valuable foundation for policymakers, automotive engineers, and researchers to develop holistic strategies that concurrently address technical innovations and their associated safety or regulatory risks.
Palm oil biofuels demystification: The needs of standardized life cycle assessments and source derivatization
The implementation of binary renewable-versus-fossil policy frameworks has the effect of obscuring the environmental costs and overstating the sustainability of palm oil biofuel. This review examines life cycle assessment (LCA) studies and regulatory frameworks, demonstrating that land-use change (LUC) is the predominant source of greenhouse gas and deforestation poses a threat to over 80% of species in production regions. Inconsistent system boundaries have been found to hinder comparability, with certification schemes including RSPO, ISPO, and EU RED II frequently overlooking LUC data, thus perpetuating unsustainable plantation strategies. As one of the most imminent solutions, used cooking oil (UCO) appears as a viable alternative, exhibiting a carbon intensity that is more than 60 % lower than that of conventional jet fuel and potentially resulting in a reduced depletion of fossil resources. However, only approximately 20% of global domestic UCO is recycled and 85% of regions lack UCO-specific legislation. A three-pillar framework is proposed: harmonized LCA with mandatory LUC accounting, scaled UCO valorization, and biodiversity-friendly practices including agroforestry and castor cultivation on degraded lands. It is reported that these measures can reduce emissions by 67.2% without compromising land integrity or food security. The notion of directly comparing the non-use of palm oil with the complete disregard for its unsustainability may be alleviated in the future by regarding some of the aspects proposed in this study.
A comprehensive evaluation of broad learning system for deep feature- based chili leaf disease classification
The early detection of plant leaf diseases is essential for the enhancement of crop productivity and the promotion of sustainable agricultural practices. While deep learning models have been shown to be achieve remarkable success in the recognition of plant disease, conventional classifiers commonly rely on iterative gradient-based optimization, resulting in increased training complexity. This present study investigates a hybrid framework for the classification of chili leaf disease that combines DenseNet201-based deep feature extraction with a Broad Learning System (BLS) classifier. The DenseNet201 model is employed to generate discriminative feature representations, whereas the BLS approach employs a closed-form ridge regression solution for classification. The present study involved experiments conducted by means of the publicly available Chili Plant Leaf Disease Dataset containing 1,856 original images from six different disease categories. To prevent data leakage, the dataset was initially partitioned into training, validation, and test subsets at the original-image level, with data augmentation being applied exclusively to the training set, thereby increasing it to 9,093 images. The proposed DenseNet201+BLS framework achieved a test accuracy of 99.28% and a macro F1-score of 99.00%. Furthermore, the performance of the proposed model was compared with that of Softmax, Logistic Regression, Random Forest, Multilayer Perceptron, and Support Vector Machine (SVM) classifiers using identical DenseNet201 feature representations. Among the evaluated classifiers, SVM demonstrated the highest level of accuracy (99.64%), whereas BLS exhibited a favorable balance between predictive performance and computational efficiency, requiring less than one second for training while outperforming Softmax, Logistic Regression, and Random Forest. Grad-CAM visualizations further demonstrated that the extracted deep features focus on disease-relevant regions such as lesions, discoloration patterns, and abnormal leaf structures. The findings indicate that the integration of DenseNet201 feature extraction with a Broad Learning System offers a competitive and computationally efficient alternative for the automated classification of chili leaf disease. The proposed framework facilitates accurate disease recognition with substantially reduced training costs, making it a promising solution for resource-efficient agricultural monitoring and decision-support applications.
Dense porous monolithic carbon from Indonesian daikon leaf waste via NaOH–CO2 physicochemical activation for binder-free volumetric supercapacitors
High-performance volumetric supercapacitors are often limited by the trade-off between porosity and electrode density in biomass-derived carbon. Herein, binder-free monolithic carbon electrodes were produced from Indonesian daikon leaf waste through 0.5 M NaOH pretreatment followed by integrated N2–CO2 thermal processing. NaOH activation increased the surface area from 236 to 570 m2 g−1, increasing carbon purity from 88.34 to 95.45 wt% while retaining 4–5 wt% oxygen functionalities. The activated monolithic carbon electrode (VSAC) delivered a volumetric capacitance of 169 F cm−3 at 1 mV s−1 and 185 F cm−3 at 10 mA cm−2 in a symmetric two-electrode configuration without binders or conductive additives. VSAC also retained 83.2% capacitance at high scan rates and achieved 16.01 Wh L−1 at 838 W L−1. The improved performance arises from interconnected ion pathways, preserved electrode density, and surface oxygen functionalities, demonstrating a sustainable strategy for practical volumetric supercapacitors.
Efficient lead removal from industrial wastewater using activated carbon synthesized from wood sawdust
The social economy's rapid expansion and unprecedented population growth are significantly contributing to environmental issues. Contamination of soil and water by heavy metals is a major environmental problem. Activated carbon synthesized from biomass possesses several qualities, including a large specific surface area, a hierarchically porous structure, robust adsorption capability, and high economic value. Wood sawdust, a plentiful agricultural by-product, was used to chemically produce activated carbon (AC). Lead removal from industrial wastewater was examined using this AC. The Langmuir and Freundlich models, along with first- and second-order kinetics, were applied for kinetic analysis. The novelty of this work lies in the combination of moderate-temperature chemical activation (600 °C) with Iraqi wood sawdust, achieving a remarkably high surface area (1477.54 m2/g) compared to most previously reported biomass-derived adsorbents. Results showed an impressive maximum adsorption capacity of 177.54 mg/g. This value compares favorably with many recently reported biomass-derived adsorbents. The boundary layer effect occurs, and the adsorption of Pb follows pseudo-second-order kinetics.
Green synthesis of mixed-phase copper nanostructures using Annona muricata extract: role of oxidation on selective antifungal activity
The green synthesis of copper-based nanomaterials utilizing plant extracts has attracted significant attention; however, controlling oxidation state and understanding its impact on biological performance remain challenging. In this present study, mixed-phase copper nanostructures were synthesized using aqueous stem bark extract of Annona muricata as a dual reducing and stabilizing agent under mild conditions. Phytochemical analysis revealed a high total phenolic content (310 mg GAE/g), thus confirming the strong redox capability for Cu2+ reduction. UV–Vis spectroscopy indicated the formation of nanoparticle with a characteristic absorption band at approximately 360 nm. XRD and EDS analyses were conducted to confirm the coexistence of Cu (0), Cu2O, and CuO phases. The crystallite sizes of these phases ranged from 22 to 28 nm, thereby suggesting partial oxidation during synthesis. The utilization of Fourier-transform infrared spectroscopy (FTIR) analysis has been demonstrated to be a valuable tool in the identification of the involvement of polyphenolic functional groups in reduction and surface stabilization processes. SEM and TEM observations revealed the presence of quasi-spherical nanoparticles with a mean diameter of 4 -16 nm, exhibiting signs of partial aggregation. The antimicrobial performance of the synthesized nanostructures exhibited a concentration-dependent response. While limited antibacterial activity was observed against Staphylococcus aureus and Escherichia coli, a pronounced antifungal effect was obtained against Candida albicans with an inhibition zone of 27.81 mm at 20% concentration. The pronounced antifungal activity exhibited a strong correlation with the presence of mixed copper phases, suggesting that the oxidation-induced surface chemistry and controlled Cu2+ ion availability may contribute significantly to the observed efficacy. These findings highlight the functional role of oxidation in plant-mediated copper nanostructures and demonstrate their potential as selective antifungal agents. This work provides insight into the relationship between phase composition and biological activity in green-synthesized copper nanomaterials. The remarkable selectivity exhibited by these mixed-phase nanostructures against fungal pathogens positions them as a viable, eco-friendly alternative for targeted antifungal applications, thus overcoming the limitations of conventional non-specific antimicrobial agents.
Physicochemical and antibacterial properties of green-synthesized bimetallic CuO-ZnO nanoparticles
Green-synthesized bimetallic copper oxide–zinc oxide (CuO/ZnO) nanoparticles have attracted considerable interest due to their enhanced physicochemical properties and antibacterial activity. This study proposes a green synthesis approach for CuO/ZnO nanoparticles and evaluates their physicochemical characteristics and antibacterial performance by means of a range of analytical techniques including XRD, XRF, FTIR, SEM–EDX, and UV–Visible spectroscopy. XRD analysis confirmed elevated crystallinity in all samples, while the CuO/ZnO bimetallic variant (Cbest) exhibited the smallest crystallite size (22.93 nm). Antibacterial assays, conducted utilizing the agar well diffusion method demonstrated that pure CuO nanoparticles exhibited the strongest inhibition against Escherichia coli (15.39 mm), whereas the CuO/ZnO bimetallic variant (Cbest) demonstrated the highest activity against Staphylococcus aureus (10.02 mm). The results of the one-way ANOVA indicated significant differences among the treatments (p < 0.05), thus confirming the influence of nanoparticle composition on antibacterial efficacy. These findings highlight the potential of green-synthesized CuO/ZnO nanoparticles as effective antibacterial agents.
Flat mixed matrix membranes incorporating MIL-53(Al) and polyethersulfone for highly selective H2/CO2 and H2/CH4 separation
The integration of metal-organic frameworks (MOFs) as fillers in hybrid membranes commonly termed mixed matrix membranes (MMMs) represents a significant advancement in the field of gas separation technology. This present study proposes novel MMMs for the separation of H2, CH4, and CO2. The Material Institute of Lavoisier framework (MIL-53(Al)) was incorporated into a polyethersulfone (PES) matrix at filler loadings of 10%, 20%, and 30% (w/w). IR and SEM analyses confirmed that the irregular MIL-53(Al) particles were uniformly dispersed within the PES matrix. The gas separation performance was evaluated using both single gas feeds (H2, CO2, CH4) and mixed gas feeds (H2/CO2 and CO2/CH4) under 2 bar pressure at 30 °C. In comparison to the pristine PES membrane, the incorporation of MIL-53(Al) considerably enhanced gas permeability, with the 30% MIL-53(Al)@PES membrane demonstrating remarkable single-gas permeation performance. It is also notable that the membrane containing 20% MIL-53(Al) achieved the highest selectivity for H2/CO2 and CO2/CH4 (3.28), representing a 54.72% improvement over the pristine PES membrane. Interestingly, almost all MIL-53(Al)@PES membranes exhibited exceptional H2/CO2 separation performance, exceeding the Robeson upper bound. However, for the H2/CH4 and CO2/CH4 mixed gas separation tests, the selectivity of 1.82 and 0.32, respectively, was observed for the 20% MIL-53(Al) membrane, closely resembling the performance of the pure PES membrane. The present work demonstrates that the integration of MIL-53(Al) into PES is an effective strategy to enhance membrane penetrability and H2/CO2 separation performance, thereby highlighting its potential for the improvement of MOF-based mixed matrix membranes for selective gas separation.
Erratum to: "Exploring fluidization dynamics and chemical performance in silicon tetrachloride (SiCl4) hydrochlorination processes within a fluidized bed reactor: Development and analysis of an Eulerian-granular model"
The present work examines the complex fluidization pattern and reactive interactions of silicon tetrachloride (SiCl₄) during hydrochlorination in a fluidized-bed reactor (FBR), a system that remains difficult to model accurately. To address this gap, we develop a new Eulerian–granular CFD framework that for the first time couples the Eulerian–Eulerian fluid model with KTGF specifically for SiCl₄ hydrochlorination, enabling prediction capabilities that are unavailable in previous FBR studies. The validity of the model was confirmed through comparisons with empirical bed-expansion correlations and Hsu’s gas-temperature data, that demonstrated strong agreement and the ability of the model to capture the coupled thermal–hydrodynamic behavior of the system. In addition to the conventional observations documented in previous studies, this study identifies distinct flow-regime transitions and bed-voidage evolution that are unique to SiCl₄. These findings demonstrated the impact of SiCl₄’s reactive transport behavior on fluidization stability. Under bubbling conditions, the model uncovered a characteristic SiCl₄ distribution pattern that more significantly enhanced gas–solid mixing in comparison to previous report. Additionally, it predicts rapid heat equilibration within ~13 mm of bed height - a behavior not documented in earlier hydrochlorination studies. Chemically, the model predicted a maximum SiHCl₃ concentration of 14.01% and an SiCl₄ conversion of 29.84%, thereby offering new mechanistic insight into how fluidization dynamics directly govern reaction performance. Overall, this work provides the first specialized CFD framework for SiCl₄ hydrochlorination, thus establishing a novel mechanistic understanding of its fluidization–reaction coupling. Furthermore, it offers a more accurate predictive basis for optimizing industrial FBR systems employed in silicon-based chemical manufacturing.
The effect of ultrasound-assisted enzymatic hydrolysis (UAEH) on the physicochemical properties and bioactivity of edible swiftlet’s nest hydrolysates
This study investigates the effect of ultrasound-assisted enzymatic hydrolysis (UAEH) compared with non-hydrolyzed samples on the physicochemical properties and bioactivity of edible swiftlet’s nest (ESN) co-product hydrolysates using bromelain under optimized processing conditions. ESN was subjected to UAEH treatment (40 °C, pH 7.0, 40 kHz) followed by freeze-drying. The results showed that hESN-C achieved the highest yield (62.13%) and degree of hydrolysis (37.95%), while hESN-CP gave the lowest (28% yield, 9.57% degree of hydrolysis). FTIR spectroscopy confirmed the retention of key glycoprotein functional groups across all hydrolysate fractions: characteristic O–H/N–H stretching vibrations at 3270-3272 cm-1, Amide I at 1630-1631 cm-1, and Amide II at 1526-1537 cm-1, together with glycan-associated C–O/C–N stretching vibrations, indicating selective peptide bond cleavage by bromelain without disruption of glycosidic linkages and preservation of the carbohydrate architecture in the resulting bioactive glycopeptides. HPLC qualitatively confirmed sialic acid retention in all fractions (retention time 6-7 min). Amino acid profiling revealed complete essential amino acid profiles in all samples, with the highest concentrations in hESN-C dominated by L-valine (36,507.33 mg/kg), L-proline (37,083.32 mg/kg), and L-serine (37,635.48 mg/kg). Fatty acid analysis identified oleic acid (0.1491%) and linoleic acid (0.0428%) as the predominant unsaturated fatty acids, particularly in hESN-CP. Particle size analysis demonstrated that UAEH produced smaller, more uniformly distributed nanoparticles (14.17-27.03 nm) with low polydispersity indices (<0.3). Scanning electron microscopy revealed distinct morphological differences: hESN-C and hESN-D exhibited homogeneous laminar structures, whereas hESN-CP displayed heterogeneous porous morphology attributed to keratin from feather residues. UAEH treatment significantly increased soluble protein content (hESN-C: 1.246 μg/μL; hESN-CP: 0.844 μg/μL) and DPPH radical scavenging activity (up to 10.70% for hESN-CP) relative to non-hydrolyzed controls. Heavy metal concentrations (Hg: 0.0172–0.0236 mg/L; Cd: 0.1194–0.1444 mg/L; Pb: 0.11–1.4 mg/L) remained within Chinese safety thresholds, and water activity was reduced to safe levels (<0.60). Collectively, these results demonstrate that UAEH is an effective and sustainable strategy for valorizing ESN co-products into physicochemically improved, bioactive glycopeptide hydrolysates with potential as functional food ingredients. Future studies employing quantitative in vitro bioactivity assays and in vivo validation are warranted to substantiate broader health-benefit claims.
Composite copper(I)-hydroxyapatite: a heterogeneous catalyst for the homocoupling reaction of biaryl compounds
In this present study, a reusable Cu(I)-hydroxyapatite (HAp) catalyst was developed for the homocoupling of arylboronic acids under mild conditions. The investigation encompassed the examination of how different solvents and atmospheric conditions affect the performance of catalyst. The catalyst, was prepared by means of wet impregnation with an acetone-water (24:1) mixture purposely to load 10 wt% CuI onto HAp. XRD, FT-IR, and SEM-EDS analyses demonstrated that CuI was successfully deposited on the HAp surface without changing its crystal structure or shape. During the experiment, it is established that both the solvent and the atmosphere exhibited a significant impact on the homocoupling of p-tolylboronic acid. The most optimal results were achieved through the utilization of dry DMF in air at room temperature for a duration of 24 hours. In these conditions, 4,4'-dimethylbiphenyl was isolated in approximately 80% yield, as confirmed by GC-MS, 1H NMR, and 13C NMR. The catalyst stayed active over four cycles, although the yield fell to 53% by the fourth run. XRD analysis of the used catalyst revealed that the HAp structure remained mostly unchanged. When we tested different substrates, it was found that Cu(I)-HAp worked most effectively with para-substituted arylboronic acids that were less densely populated. Overall, Cu(I)-HAp has been identified to a potentially effective heterogeneous catalyst for the aerobic biaryl synthesis. The device is characterized by Its ease of use, its capacity for recycling, and its operationalization at room temperature.
Enhanced bioelectricity recovery and melanoidin degradation in CO2- capturing microbial fuel cells via biochar-immobilized whole-cell biocatalysts
Melanoidin-rich wastewater from agro-industrial processes, particularly palm oil mill effluent, poses significant environmental problem owing to its recalcitrant nature and high organic load. The present study developed a carbon-capturing microbial fuel cell (MFC) integrated with biochar-immobilized laccase-producing microbial consortia for an integrated system enabling concurrent melanoidin removal, bioelectricity recovery, and carbon fixation under the tested conditions. Empty fruit bunch (EFB) biochar produced at 600 °C (BC600) was selected as an immobilization support due to its superior adsorption capacity and functional surface properties. The immobilized system demonstrated a maximum melanoidin removal efficiency of 73.15±1.10% and significant COD reduction with degradation exhibiting a close association with enhanced laccase activity. In the MFC, enhanced electrochemical performance was observed, with a maximum open circuit voltage of 619.17±10.49 mV, along with current and power densities of 8.29±0.15 A/m3 and 1.00±0.20 W/m3, respectively. Coupling with microalgae resulted in the simultaneous fixation of carbon (0.13±0.00 g/L/day). A phytotoxicity assessment confirmed no inhibitory effects on rice seed germination. This finding indicates that the substance is environmentally safe. It is imperative to note that the present study proposes a novel integration of biochar-based immobilization with a carbon-capturing MFC for the concurrent removal of pollutants, energy recovery, and CO2 mitigation. This sustainable approach offers a promising solution for the treatment of melanoidin-rich wastewater.
Isolation and identification of endophytic Streptomyces strain and its antifungal activity against root-pathogenic fungi from watermelon roots
Watermelon production is frequently constrained by soil-borne fungal diseases, particularly those caused by Fusarium fujikuroi, which have been shown to severely impair plant growth and productivity. The objective of this study is to isolate, identify, and evaluate the antifungal potential of Streptomyces strains associated with watermelon-growing soils and to explore their functional traits related to biological control. A total of 22 actinomycete isolates were recovered and screened for chitinolytic and antifungal activities, leading to the selection of three promising strains designated NVC-TL3, strain 4, and NVC-TX. Morphological characterization and 16S rRNA gene sequencing identified these isolates as Streptomyces cyaneochromogenes NVC-TL3, Streptomyces toxytricini strain 4, and Streptomyces lydicus NVC-TX. All strains exhibited extracellular chitinase activity and significantly inhibited the growth of *F. fujikuroi* under in vitro conditions. Among them, S. lydicus NVC-TX demonstrated the strongest antagonistic activity, achieving 74.9% mycelial growth inhibition after 7 days of incubation. The efficacy of the substances was further confirmed by greenhouse experiments, which demonstrated a reduction in disease severity to 14.9% and a significant enhancement in plant vigor in comparison to the pathogen-inoculated control. The study revealed substantial functional variation among phylogenetically distinct Streptomyces isolates and demonstrated a close association between chitinolytic capacity and disease suppression. These findings contribute to the existing body of knowledge concerning the biodiversity of Streptomyces associated with watermelon and highlight the potential of S. lydicus NVC-TX as a promising biological control agent. The application of this strain could contribute to the reduction of dependence on synthetic fungicides, thereby promoting environmentally sustainable disease management, and supporting healthier and more productive watermelon cultivation systems.
Sugar composition profiling of Indonesian stingless bee (Meliponini) honey from multiple geographical regions using HPLC-RID and multivariate analysis
Stingless bee honey (Meliponini) has attracted attention due to its unique sugar composition, particularly trehalulose, a low-glycemic disaccharide with potential health benefits. This study investigated the sugar profiles of 23 stingless bee honey samples collected from East Java, Central Java, Bali, Belitung, and Jambi using High-Performance Liquid Chromatography-Refractive Index Detection (HPLC-RID). Fructose and glucose were the dominant sugars, while trehalulose concentrations varied markedly among regions. The highest fructose content was observed in Bali honey (47.09 g/100 g), whereas Belitung honey exhibited the highest trehalulose concentration (43.22 g/100 g). Jambi samples contained relatively higher sucrose levels. Dehumidification treatment reduced trehalulose concentration while increasing fructose and glucose contents. Principal Component Analysis (PCA) revealed clear regional clustering patterns, explaining 83.3% of the total variance. Belitung honey samples were distinguished by exceptionally high trehalulose concentrations compared with samples from other regions. These findings provide a comparative overview of regional variation in sugar composition and trehalulose distribution in Indonesian stingless bee honey. The results contribute to the scientific database of Indonesian stingless bee honey and may support future efforts in quality control, honey characterization, and the development of high-value honey products.
Decision-layer interpretability for CNN-based glaucoma classification via sparse feature selection and ANFIS
Glaucoma is a leading cause of irreversible vision loss, and its early detection remains challenging due to the presence of subtle structural varitions in retinal fundus images. Convolutional neural networks (CNNs) have demonstrated strong performance for automated classification of glaucoma; however, the relationship between extracted features and prediction outcomes frequently proves challenging to interpret. Most existing explainable artificial intelligence (XAI) approaches rely on post hoc visualizations, which provide limited insight into the decisions-making process. To address this limitation, this present study proposes a hybrid CNN–feature selection–ANFIS framework (CNN–FS–ANFIS) that integrates interpretability directly within the decision layer. In this framework, the first stage of the process involves adapting a CNN backbone to the glaucoma classification task through the use of transfer learning. This is then used as a fixed feature extractor to obtain retinal representations for decision-layer modeling. Subsequently, a feature selection stage is applied driven by sparsity to construct a compact and structured subset of informative features. These features are then fed into an Adaptive Neuro-Fuzzy Inference System (ANFIS), enabling predictions to be expressed through explicit fuzzy rule-based reasoning. The impact of feature compactness is examined in a controlled experimental setting, where the feature subset size is varied from three to nine. The findings demonstrate that compact feature subsets can achieve consistent and competitive performance. By means of LASSO-selected features, the ANFIS decision layer achieved an AUC of 0.84±0.01, sensitivity of 0.82±0.13, specificity of 0.74±0.10, and an F1-score of 0.79±0.04. Rule-base analysis further exhibited that two-to three-rule ANFIS configurations-maintained AUC values of approximately 0.84 while preserving a transparent and manageable decision structure. The proposed framework, therefore, enables direct analysis of the relationship between selected CNN features, fuzzy rules, and model outputs. This traceable decision pathway has the potential to support more transparent and auditable glaucoma screening systems.