Sci

The aim of the present study was to examine the impact of maturation and relative age on the anthropometric variables and physical performance of young elite volleyball players according to sex and playing positions. The sample included 207 girls (13.59 ± 1.74 years) and 59 boys (14.30 ± 1.48 years) who were selected to participate in the 2020–2025 Spanish National Volleyball Programme. Maturity status was estimated using a non-invasive method (percentage of predicted adult height). Relative age was calculated based on date of birth and expressed as decimal age (0–0.99). The physical tests carried out were spike jump reach, vertical jump, 3 × 9, and strength–endurance–agility–coordination (FRAC) tests. The results showed that there was no impact of the interaction between maturity status and relative age on physical performance, except in the 3 × 9 test by boys in the wing-spiker position. Moreover, maturity status had a greater influence on physical test performance than that of relative age. Specifically, maturation served as a statistically significant positive predictor of height in the SJR test for girls who were all-around players, explaining 71.58% of the variance. In addition, an advanced maturity status correlated with better physical performance outcomes, especially in the all-around player and wing-spiker playing positions in boys and the middle-blocker and all-around player playing positions in girls. Coaches and stakeholders should implement strategies to reduce bias, especially regarding maturation, with the aim of retaining the most physically talented late-maturing players, considering differences by playing position and sex. Full article

Large language models (LLMs) have recently demonstrated remarkable capabilities in natural language processing, mathematical reasoning, and code generation. However, their potential for solving differential equations—fundamental to applied mathematics, physics, and engineering—remains insufficiently explored. For the first time, we applied LLMs as translators from the textual form of an equation into the textual representation of its analytical solution for a broad class of equations. More precisely, we introduced a benchmark and fine-tuning protocol for differential equation solving with pre-trained LLMs. We curated a dataset of 300,000 differential equations and corresponding solutions to fine-tune T5-small, Phi-4-mini, DeepSeek-R1-Distill-Qwen, and two Mamba variants (130M and 2.8B parameters). Performance was evaluated using BLEU and TeXBLEU metrics. Phi-4-mini achieved the best results, with average BLEU > 0.9 and TeXBLEU > 0.78 across all considered equation classes, which shows the strong generalization abilities of the model. Therefore, this model should be further investigated on a broader class of differential equations and potentially can be used as a part of mathematical agents for solving more complex particular tasks, for example, from physics or engineering. Based on our results, DeepSeek-R1-Distill-Qwen consistently underperformed, while T5 showed strong results for the most frequent equation type but degraded on less common ones. Mamba models achieved the highest TeXBLEU scores despite relatively low BLEU, attributable to their production of lengthy outputs mixing correct expressions with irrelevant ones. Full article

Legumes contain variable amounts of bioactive substances, including protease inhibitors, which have a protective role against herbivorous insects and bacterial, fungal, and viral pathogens. However, their potential for application in agricultural and medicinal practices requires additional investigation. Bitter vetch (Vicia ervilia (L.) Willd.) is an ancient crop that is now underutilized, and its potential for various applications has recently been reevaluated. In this study, we report the purification, characterization, and bioactive properties of a protease inhibitor against trypsin/chymotrypsin-type proteases (vPI) from bitter vetch seeds. The inhibitor was purified by extraction under acidic conditions, ammonium sulfate fractionation, and size-exclusion chromatography. Its inhibitory specificity, thermostability, pH stability, and antioxidant and antimycotic activity against Alternaria alternata, Alternaria solani, Aspergillus fumigatus, Aspergillus niger, Candida albicans, Fusarium solani, Mucor michei, Penicillium griseofulvum, and Rhizopus oryzae were evaluated. Purified vPI presented superoxide anion scavenging power and antifungal activity in response to all tested strains except M. michei. It had the strongest effect on F.solani and A. solani, and a moderate effect on P. griseofulvum and C. albicans. The treatment of A. alternata, R. oryzae, A. fumigatus, and A. niger demonstrated high efficacy within the initial 24h but declined thereafter. The usefulness and limitations of the vPI application in practice are discussed. Full article

Recent advancements in carbon nanotubes and graphene have driven significant research into other low-dimensional materials, with phosphorus-based materials emerging as a notable area of interest. Phosphorus nanowires and thin sheets show promise for applications in devices such as batteries, photodetectors, and field-effect transistors. However, the presence of multiple allotropes of phosphorus complicates their characterization. Accurate identification of these allotropes is essential for understanding their physical, optical, and electronic properties, which influence their potential applications. Researchers frequently encounter difficulties in consolidating literature for the confirmation of the structure of their materials, a process that can be time-consuming. This minireview addresses this issue by providing a comprehensive, side-by-side comparison of Raman and X-ray diffraction characteristic peaks, as well as electron microscopic images and lattice spacings, for the various phosphorus allotropes. To our knowledge, this is the first compilation to integrate all major structural fingerprints into unified summary tables, enabling rapid cross-referencing. This resource aims to support researchers in accurately identifying phosphorus phases during synthesis and device fabrication workflows. For example, distinguishing between red phosphorus polymorphs is crucial for optimizing anode materials in sodium-ion batteries, where electrochemical performance is phase-dependent. Full article

This study investigates the numerical prediction of the sonic boom phenomenon in supersonic aircraft by evaluating the near-field pressure signatures of three different aeroshapes. Two computational fluid dynamics (CFD) solvers, the open-source SU2 Multiphysics code and ANSYS Fluent, were employed to assess their effectiveness in modeling the aerodynamic flow field. A preliminary validation of numerical methods was conducted against numerical data available from the Sonic Boom Prediction Workshops (SBPW) organized by NASA, ensuring simulation reliability. Particular attention is paid to the topology of the mesh grid, exploring hybrid approaches that combine structured and unstructured grids to optimize the accuracy of pressure wave transmission. In addition, different numerical schemes were analyzed to determine the best practices for sonic boom simulations. The proposed methodology was finally applied to three supersonic aircraft developed within the European project MORE&LESS, demonstrating the capability of the model to estimate shock wave generation, evaluate the aeroacoustic performance of different supersonic aeroshapes from Mach 2 to Mach 5, and provide predictions to support ground-level noise assessment. The findings of this study contribute to the definition of a comprehensive workflow for sonic boom evaluation, providing a reliable methodology for exploring future supersonic aircraft designs. Full article

The growing interest in sustainable materials has encouraged the valorization of agro-industrial byproducts for pharmaceutical, nutraceutical, and food applications. This study evaluated yellow pitaya peel powder, obtained via convective and refractance window drying, as a diluent in immediate-release quercetin tablets. The powders were characterized by physicochemical, granulometric, morphological, and surface properties, and compared with conventional excipients, including partially pregelatinized corn starch and spray-dried lactose monohydrate. Refractance window drying improved solubility, flowability, and structural integrity, while convective drying produced finer, more porous particles with lower water activity. Tablets formulated with both powders showed adequate hardness, low friability, and disintegration times under five minutes. All systems achieved complete quercetin release. Kinetic modeling revealed anomalous, matrix-regulated transport, with Weibull and Modified Hill models providing the best fit. Based on these results, pitaya peel powder could be considered a suitable diluent for the development of immediate-release tablets, offering functional performance aligned with sustainable formulation strategies. Full article

Grape pomace (i.e., the residual skins, seeds, and pulp left after vinification) retains up to 70% of the fruit’s original phenolic compounds and is also rich in dietary fiber. As such, because this by-product is generated in large quantities worldwide and its disposal is both technologically problematic and costly, reusing it as a food ingredient could simultaneously mitigate environmental burdens, lower winery waste-management expenses, and enhance the nutritional profile of fortified foods. In this context, this study investigated the nutritional enrichment of dark chocolate by incorporating flour produced from red (cv. Syrah) and white (cv. Arinto) grape pomace at three levels (5, 10, and 15% w/w). Formulated chocolates and controls were manufactured under industrial tempering conditions and subsequently analyzed for protein, lipids, sugars, dietary fiber, total phenolic content, antioxidant capacity (DPPH and ORAC), color, texture, and consumer perception (hedonic test). All fortified samples showed higher fiber and antioxidant activity than the control, with “White_15” showing higher fiber content (43.1%) and “Red_5” for ORAC (69,483 µmol TE/100 g) and DPPH (6587 µmol TE/100 g). Dietary fiber showed an increase in content with the increase in grape pomace incorporation, regardless of the type (red or white). Texture softening was observed in all fortified chocolates independently of the incorporation level or type (red or white). Principal Component Analysis (PCA) and hierarchical clustering confirmed clear separation between control and fortified chocolates based on the parameters analyzed. Sensory evaluation with untrained panelists revealed good overall acceptability across all formulations. These findings demonstrate that grape pomace flour can be effectively valorized as a functional ingredient in dark chocolates, supporting circular economy practices in the wine and confectionery sectors while delivering products with enhanced health-promoting attributes (nutritional and antioxidant). Full article

The aviation industry prioritizes safety and has embraced innovative approaches for both reactive and proactive safety measures. Machine learning (ML) has emerged as a useful tool for aviation safety. This systematic literature review explores ML applications for safety within the aviation industry over the past 25 years. Through a comprehensive search on Scopus and backward reference searches via Google Scholar, 87 of the most relevant papers were identified. The investigation focused on the application context, ML techniques employed, data sources, and the implications of contextual nuances for safety analysis outcomes. ML techniques have been effective for post-accident analysis, predictive, and real-time incident detection across diverse aviation scenarios. Supervised, unsupervised, and semi-supervised learning methods, including neural networks, decision trees, support vector machines, and deep learning models, have all been applied for analyzing accidents, identifying patterns, and forecasting potential incidents. Notably, data sources such as the Aviation Safety Reporting System (ASRS) and the National Transportation Safety Board (NTSB) datasets were the most used. Transparency, fairness, and bias mitigation emerge as critical factors that shape the credibility and acceptance of ML-based safety research in aviation. The review revealed seven recommended future research directions: (1) interpretable AI; (2) real-time prediction; (3) hybrid models; (4) handling of unbalanced datasets; (5) privacy and data security; (6) human–machine interface for safety professionals; (7) regulatory implications. These directions provide a blueprint for further ML-based aviation safety research. This review underscores the role of ML applications in shaping aviation safety practices, thereby enhancing safety for all stakeholders. It serves as a constructive and cautionary guide for researchers, practitioners, and decision-makers, emphasizing the value of ML when used appropriately to transform aviation safety to be more data-driven and proactive. Full article

Agricultural markets are increasingly exposed to global risks as climate change intensifies and macro-financial volatility becomes more prevalent. This study examines the dynamic interconnection between major agricultural commodities—soybeans, corn, wheat, rough rice, and sugar—and key uncertainty indicators, including climate policy uncertainty, global economic policy uncertainty, geopolitical risk, financial market volatility, oil price volatility, and the U.S. Dollar Index. Using a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model with monthly data, we assess both internal spillovers within the commodity system and external spillovers from macro-level uncertainties. On average, the external shock from the VIX to corn reaches 12.4%, and the spillover from RGEPU to wheat exceeds 10%, while internal links like corn to wheat remain below 8%. The results show that external uncertainty consistently dominates the connectedness structure, particularly during periods of geopolitical or financial stress, while internal interactions remain relatively subdued. Unexpectedly, recent global disruptions such as the COVID-19 pandemic and the Russia–Ukraine conflict do not exhibit strong or persistent effects on the connectedness patterns, likely due to model smoothing, stockpiling policies, and supply chain adaptations. These findings highlight the importance of strengthening international macro-financial and climate policy coordination to mitigate the propagation of external shocks. By distinguishing between internal and external connectedness under climate stress, this study contributes new insights into how systemic risks affect agri-food systems and offers a methodological framework for future risk monitoring. Full article

Bioactive peptides have significant potential for applications in pharmaceuticals, the food industry, and cosmetics due to their wide spectrum of biological activities. However, their pronounced structural and functional heterogeneity complicates the classification and prediction of biological activity. This study uses data from the PepLab platform, comprising 2748 experimentally confirmed bioactive peptides distributed across 15 functional classes, including ACE inhibitors, antimicrobial, anticancer, antioxidant, toxins, and others. For each peptide, the amino acid sequence and key physicochemical descriptors are provided, calculated via the integrated DMPep module, such as GRAVY index, aliphatic index, isoelectric point, molecular weight, Boman index, and sequence length. The dataset exhibits class imbalance, with class sizes ranging from 14 to 524 peptides. An innovative methodology is proposed, combining descriptive statistical analysis, structural modeling via DEMATEL, and structural equation modeling with neural networks (SEM-NN), where SEM-NN is used to capture complex nonlinear causal relationships between descriptors and functional classes. The results of these dependencies are integrated into a multi-class machine learning model to improve interpretability and predictive performance. Targeted data augmentation was applied to mitigate class imbalance. The developed classifier achieved predictive accuracy of up to 66%, a relatively high value given the complexity of the problem and the limited dataset size. These results confirm that integrating structured dependency modeling with artificial intelligence is an effective approach for functional peptide classification and supports the rational design of novel bioactive molecules. Full article

This study presents a general 3 × 5 × 5 factorial experimental design to maximize the Power Coefficient (Cp) of an H-Darrieus hydrokinetic turbine equipped with external accessories. Five accessory configurations (standard, cycloidal, flat plate, curve, and blocking plate), three solidity levels, and five Tip-Speed Ratio (TSR) levels were evaluated as main factors under the hypothesis that these factors significantly influence Cp. The data analyzed were obtained from numerical simulations, and their processing was conducted using Analysis of Variance (ANOVA), linear regression models, and response surfaces in the software programs Minitab 21 and RStudio V4.4.2. ANOVA makes it possible to determine the statistical significance of the effect of each factor and their interactions on the obtained Cp, identifying the accessories, TSR, and solidity that have the greatest impact on turbine performance. The results indicate that the optimal configuration to maximize Cp includes the flat-plate accessory, a solidity of 1.0, and a TSR of 3.2. From the linear regression models, mathematical relationships describing the system’s behavior were established, while the response surface analysis identified optimal operating conditions. These findings provide an effective tool for optimizing H-Darrieus turbine designs, highlighting the positive impact of accessories on performance improvement. Full article

In Italy, several changes to academic and professional standards and rules in kinesiology and sport have recently occurred. On the university side, no data collection has started regarding these changes and effects on specific scholars. The aim of this study was to evaluate the opinions of Italian university scholars in Exercise and Sport Sciences regarding recent disciplinary reclassifications, the emergence of the kinesiologist as a formal profession, and related curricular updates. Specifically, this study aimed to measure scholars’ views on the usefulness of unification, hybridization with other fields of knowledge, interdisciplinarity with pedagogy, the distinctiveness of undergraduate education in light of the new kinesiologist profile, and the inclusion of Technical and Laboratory Activities (TLA) credited through the European Credit Transfer System (ECTS). These aspects were explored through an eight-question survey offering three multiple-choice answers. An exploratory survey was distributed to a defined population of 261 Italian scholars (48 full professors, 137 associate professors, and 76 researchers). A total of 83 responses were collected: 14 full professors, 45 associate professors, and 24 researchers (response rate: 31.8%). Descriptive statistics and inferential analyses (Chi-Square tests, Cramér’s V, and Pearson/Spearman correlations) were conducted. Results indicated that 72.3% perceived overlap between pedagogical and medical disciplinary groups, and 85.5% considered practical/laboratory activities essential to the kinesiologist’s role. Significant differences in keyword-sharing perceptions across academic ranks emerged (p = 0.012; V = 0.3), and a near-significant trend was found regarding the importance of discipline-aligned research (p = 0.058; V = 0.3). Full agreement was found on the use of updated scientific evidence in lectures (100%), and 81.9% supported standardized education for the kinesiologist profession (Q6). Positive correlations were observed between support for keyword sharing and belief in its usefulness for promoting interdisciplinarity among full professors (r = 0.58, p = 0.02), associate professors (r = 0.68, p < 0.01), and researchers (r = 0.83, p < 0.01). Conversely, negative correlations emerged between the importance placed on practical activities and support for interdisciplinarity among associate professors and researchers, with values ranging from r = −0.31 to −0.46. The results are significant and tended toward autonomy from pedagogy, training aligned with the bachelor’s and master’s degree kinesiologist, and interdisciplinarity inherent in typical Exercise and Sport Sciences (ESS) keywords. This study should be replicated to increase the sample and to expand the ad hoc questionnaire to other issues. These findings highlight the need for greater alignment between academic training, disciplinary definitions, and professional practice through shared epistemological frameworks and updated descriptors that reflect scientific and labor market developments. Full article

Most levees are composed of earthen materials, making their structural stability vulnerable under flood conditions, especially in the case of overtopping. This study aims to analyze the relationship between the channel Froude number and the flow behavior on the landward slope of a levee during overtopping, enabling the prediction of landward slope velocity (LSV) in advance. Accurate estimation of flow velocity on the landward slope is crucial for predicting the occurrence and intensity of erosion during overtopping events, and it serves as a critical criterion for designing protective armoring and assessing levee structural stability. Numerical simulations were conducted under various Froude numbers in the channel to estimate the corresponding LSV. Key variables, including channel discharge, velocity, levee height, and overtopping flow depth, were used to establish quantitative correlations between channel flow characteristics and LSV. The proposed model effectively predicts the LSV for channel Froude numbers approximately between 0.05 and 0.60. The findings allow for a simplified estimation of LSV based on changes in Froude number and overtopping flow depth, providing valuable baseline data for planning levee reinforcement and maintenance strategies. Full article

Perfluorooctane sulfonate (PFOS), a member of the per- and polyfluoroalkyl substance (PFAS) family, has been associated with adverse health effects, including potential links to autism spectrum disorder (ASD). This study investigates the impact of PFOS on metabolic, immunologic and behavioral profiles in BTBR-mt^B6 mice, a mouse strain that models ASD, to provide insights into the role of PFOS in ASD development and related health concerns. Three-month-old male and female BTBR-mt^B6 mice were divided into two groups (n = 6) and received daily administration of either 1 mg/kg PFOS or vehicle over a three-month period by gavage. Metabolic assessments included measurements of body weight and weekly blood glucose levels, glucose and insulin tolerance tests, organ weights, and body compositions (free fluid, fat and lean tissue). Immune profiling was conducted via flow cytometric analysis of splenic leukocytes, while behavioral evaluations included grooming, sniffing, and three-chamber social interaction tests. PFOS exposure disrupted glucose homeostasis, with both sexes exhibiting elevated blood glucose levels. Male mice showed impaired glucose tolerance, delayed glucose level recovery, and increased insulin resistance, while females displayed decreased insulin resistance. Additionally, PFOS exposure led to liver enlargement in both sexes. Behavioral assessments revealed heightened grooming in PFOS-treated males, commonly interpreted as stress- or ASD-related repetitive behaviors, whereas females exhibited reduced grooming, reflecting altered behavioral responses to exposure. Immune alterations were also sex specific. PFOS-treated males exhibited decreased granulocytes, increased macrophages, and enhanced surface expressions of B220 and CD40L. PFOS-treated females showed increased macrophages, B-cells, cytotoxic T-cells and CD25⁺ T-cell subsets, with enhanced surface expression of B220 and CD8, and reduced surface expression of Mac-3. In addition, PFOS exposure reduced spleen weight in females. Taken together, PFOS exposure induced significant physiological and behavioral changes in BTBR-mt^B6 mice, with sex-specific differences observed. These results raise concern that PFASs may contribute to the development or exacerbation of metabolic, immune and neurodevelopmental disorders, highlighting the need for sex-specific human risk assessment in environmental toxicology. Full article

Severe fatigue, difficulty falling asleep, body stiffness, cognitive impairment, and widespread pain are some of the primary symptoms experienced by individuals with fibromyalgia (FM), leading to reduced physical function, increased frailty, and elevated fall risk. The present study aimed to evaluate the effects of the Virtual Square Step Exercise (V-SSE) program on physical function, frailty, FM impact, pain, fear, and risk of falling, and fall incidence in women with FM. A randomized controlled trial was conducted with 61 sedentary Spanish women with FM. Participants were randomly assigned to two groups: V-SSE and the control group. The V-SSE group completed an exercise program based on the V-SSE for 12 weeks (3 sessions/week), while the control group maintained their usual lifestyle and treatment. Physical function was assessed using the Timed Up and Go (TUG), Four-Step Square (FSST), 6-Minute Walking Test (6MWT), and others. Frailty was assessed with the Short Physical Performance Battery (SPPB). FM impact, pain, falls, and fear of falling were evaluated via questionnaires. Significant intergroup differences were only found in the 30 m Walking Test (p = 0.023; E.S. = 0.539), due to worsening in the control group. Although significant improvements were found in other variables in the V-SSE group, Dual Sit to Stand (p = 0.038), FM impact (p = 0.010), pain (p = 0.003) and falls (p = 0.037), these did not remain statistically significant after adjusting for multiple comparisons, nor were they corroborated in the intergroup comparison. A 12-week program based on the V-SSE was not effective in improving physical function, frailty, FM impact, pain, falls, fear, and risk of falling in Spanish women with FM. Full article

(1) Background: Violence against healthcare professionals is becoming a growing concern for healthcare systems and a public health issue, and in Portugal it remains undocumented at a national level, leaving a critical knowledge gap. This scenario compromises the development of effective public policies and evidence-based institutional strategies, which are essential for guiding policymakers in the implementation of preventive measures and appropriate safety protocols to assess the nature, frequency, and key factors contributing to violence against healthcare professionals (doctors and nurses) in clinical settings. (2) Methods: This is a quantitative, descriptive, and cross-sectional study. The sample size was 440 professionals (n = 440). Between January and May 2024, healthcare professionals (physicians and nurses) working in four local health units located in the south of Portugal were invited to participate in this study via institutional e-mail. Data was collected using a structured questionnaire on the healthcare professional’s sociodemographic and work-related characteristics and aspects related to violence towards healthcare professionals in the workplace. Unconditional logistic regression models were fitted to compute crude odds ratios (ORs) and 95% confidence intervals (95%CIs) for the association between sociodemographic and work-related characteristics and violence at work. (3) Results: Nearly 40% of the healthcare professionals sampled reported having been victims of violence in the workplace, and, among these, the majority reported experiencing psychological violence (94.2%), followed by physical violence (46.2%), another type of violence (39.1%), and sexual violence (4.1%). Incidents were mostly occasional (65.5%), occurring during the daytime (51.5%) and on weekdays (84.8%). Healthcare professionals aged between 34 and 55 years old were approximately twice as likely to experience violence compared to those who were 56 years old or older (OR = 2.28; 95%CI 1.33–3.90). Also, those who had been with the organization for more than 4 years (5–7 years: OR = 2.37; 95%CI 1.05–5.37. ≥8 years: OR = 1.87; 95%CI 1.00–3.50), as well as those who worked shifts (OR = 1.84; 95%CI 1.25–2.72), reported incidents of violence more frequently. (4) Conclusions: The low response rate (12.5%) and cross-sectional design limit the generalizability of the results, which should be interpreted considering these methodological limitations. Workplace violence in Portugal is a reality, and it requires solutions. Information related to violent incidents must be comprehensively gathered to understand the full extent of the problem and develop prevention strategies based on potentially changeable risk factors to minimize the negative effects of workplace violence. Full article

Fusarium wilt, caused by Fusarium oxysporum f. sp. cubense tropical race 4 (Foc TR4), threatens banana and plantain production throughout South America. Because Colombian biosafety regulations restrict in vitro work with Foc TR4, we tested the antifungal activity of Nostoc commune against F. oxysporum race 2 isolated from cv. ‘Manzano’ (Musa AAB). An ethanolic extract of the cyanobacterium (EEC) was profiled by gas chromatography and evaluated with a Kirby–Bauer assay (1000–4000 ppm; n = 4). Synthetic Sico^® and botanical Timorex^® served as positive controls, and solvent-free plates were the negative control. Growth reduction (GR) and percentage inhibition of radial growth (PIRG) were analysed with Student’s t-test (α = 0.05). Forty-two compounds—mainly fatty and carboxylic acids associated with antifungal activity—were detected. Sico achieved complete inhibition (100 ± 0%), Timorex suppressed 76 ± 2%, and 4 000 ppm EEC curtailed mycelial expansion by 45 ± 3% (p < 0.01). Although less potent than commercial fungicides, EEC impeded F. oxysporum growth, demonstrating that N. commune synthesises bioactive metabolites. Optimising cyanobacterial cultivation and formulation could yield a sustainable biocontrol alternative for managing Fusarium wilt in the region. Full article

In this work, ZnO nanorods were grown on vertically aligned and randomly aligned silica nanosprings using the hydrothermal method. The initial step was the deposition of a ZnO seed layer by atomic layer deposition to promote nucleation. For hydrothermal growth, equimolar (0.2 M) solutions of Zinc nitrate hexahydrate and hexamethylene tetraamine prepared in DI water were used. The ZnO NR grown on the VANS were flower-like clusters, while for the RANS, the ZnO NR grew radially outward from the individual nanosprings. The lengths and diameters of ZnO NR grown on VANS and RANS were 175 and 650 nm, and 35 and 250 nm, respectively. Scanning electron microscopy confirmed the formation of ZnO nanorods, while X-ray diffraction and Raman spectroscopy verified that they have a hexagonal wurtzite crystal structure with preferential growth along the c-axis. X-ray photoelectron spectroscopy, in conjunction with in vacuo annealing, was used to examine the surface electronic structure of ZnO nanorods and defect healing. Photoluminescence of the ZnO nanorods indicates high crystal quality, as inferred from the weak defect band relative to strong excitonic band edge emission. Full article

Ternary chalcogenide-based sulfide materials with distorted morphologies such as BaZrS₃, CaZrS₃, and SrZrS₃, have recently gained much attention in optoelectronics and photovoltaics due to their high structural and thermal stability and compatibility with low-cost, earth-abundant synthesis routes. However, their relatively large bandgaps often limit their suitability for near-infrared (NIR) photodetectors. Here, we conducted a comprehensive investigation of SrHfSe₃, a ternary chalcogenide with an orthorhombic crystal structure and distinctive needle-like morphology, as a promising candidate for NIR photodetection. SrHfSe₃ exhibits a direct bandgap of 1.02 eV, placing it well within the NIR range. Its robust structure, high temperature stability, phase stability and natural abundance make it a compelling material for next-generation, self-powered NIR photodetectors. An in-depth analysis of the SrHfSe₃-based photodetector was performed using SCAPS-1D simulations, focusing on key performance metrics such as J–V behavior, photoresponsivity, and specific detectivity. Device optimization was achieved by thoroughly altering each layer thickness, doping concentrations, and defect densities. Additionally, the influence of interface defects, absorber bandgap, and operating temperature was assessed to enhance the photoresponse. Under optimal conditions, the device achieved a short-circuit current density (Jsc) of 45.88 mA/cm², an open-circuit voltage (Voc) of 0.7152 V, a peak photoresponsivity of 0.85 AW⁻¹, and a detectivity of 2.26 × 10¹⁴ Jones at 1100 nm. A broad spectral response spanning 700–1200 nm confirms its efficacy in the NIR region. These results position SrHfSe₃ as a strong contender for future NIR photodetectors and provide a foundation for experimental validation in advanced optoelectronic applications. Full article