Search Results (4,656)

Enhancing indoor environmental quality while reducing building energy consumption represents a critical challenge for sustainable building design, particularly in hot arid climates where cooling loads dominate energy use. Despite extensive research on green wall systems (GWSs), robust quantitative data on their combined impact on air quality and thermal performance in real-world office environments remains limited. This research quantified the synergistic effects of an active indoor green wall system on key indoor air quality indicators and cooling energy consumption in a contemporary office environment. A comparative field study was conducted over 12 months in two identical office rooms in Dhahran, Saudi Arabia, with one room serving as a control while the other was retrofitted with a modular hydroponic green wall system. High-resolution sensors continuously monitored indoor CO₂, volatile organic compounds via photoionization detection (VOC_PID; isobutylene-equivalent), and PM2.5 concentrations, alongside dedicated sub-metering of cooling energy consumption. The green wall system achieved statistically significant improvements across all parameters: 14.1% reduction in CO₂ concentrations during occupied hours, 28.1% reduction in volatile organic compounds, 20.9% reduction in PM2.5, and 13.5% reduction in cooling energy consumption (574.5 kWh annually). Economic analysis indicated financial viability (2.0-year payback; benefit–cost ratio 3.0; 15-year net present value SAR 31,865). Productivity-related benefits were valued from published relationships rather than measured in this study; base-case viability remained strictly positive in energy-only and conservative sensitivity scenarios. Strong correlations were established between evapotranspiration rates and cooling benefits (r = 0.734), with peak performance during summer months reaching 17.1% energy savings. Active indoor GWSs effectively function as multifunctional strategies, delivering simultaneous air quality improvements and measurable cooling energy reductions through evapotranspiration-mediated mechanisms, supporting their integration into sustainable building design practices. Full article

Minimizing surface defects in perovskite films is crucial for suppressing non-radiative recombination and enhancing device performance. Herein, we propose the use of N-bromosuccinimide (NBS), a small molecule containing Lewis base carbonyl groups (C=O), to improve the quality of RbCsMAFA mixed-cation perovskite films. This surface treatment effectively reduces non-radiative charge-carrier recombination, in particular through the passivation of surface defects related to undercoordinated Pb²⁺ ions and halide vacancies, and significantly accelerates charge extraction from the perovskite into the Spiro-OMeTAD hole transporter. Consequently, NBS-treated PerSCs achieve a power conversion efficiency (PCE) of 18.24%, representing an 11% relative increase over the control device (16.48%). This enhancement is mainly attributed to a V_oc gain of up to 40 mV and modifications in the recombination dynamics. Supporting evidence from impedance spectroscopic analyses further confirms enhanced energy-level alignment and reduced interfacial losses, improved charge transport as well as prolonged charge lifetimes within the devices. This work provides a simple yet effective approach to reduce the non-radiative recombination losses towards more efficient and stable PerSCs. Full article

Volatile organic compounds (VOCs) are major contributors to air pollution, posing significant environmental and health risks. Here we report gallium oxide (Ga₂O₃)-coated mesh as a practical immobilized photocatalyst for VOC degradation under UVC irradiation. A 3 wt.% Ga₂O₃ suspension was spray-coated onto the stainless-steel mesh, yielding a uniform coating with strong adhesion properties, as confirmed by cross-sectional analysis. Under identical conditions to a Ga₂O₃ powder, the Ga₂O₃-coated mesh delivered comparable VOC degradation rates and first-order kinetics while offering superior mechanical stability and ease of handling. Over five consecutive cycles, 93–95% of the VOC degradation efficiency was retained with negligible loss of activity, confirming excellent reusability. Fourier Transform Infrared Spectroscopy (FTIR) spectra of the Ga₂O₃-coated mesh after degradation reaction revealed significantly reduced VOC peaks, such as C=O and C-O absorption peaks, whereas spectra for the uncoated mesh changed only slightly. These results indicate that VOC degradation originates from the coated photocatalyst. Overall, these findings demonstrate that Ga₂O₃-coated mesh is a highly efficient, stable, and reusable platform for VOC removal, suggesting its potential for practical applications in air purification and environmental remediation. Full article

Volatile organic compounds (VOCs) are critical indicators of the metabolic status of whole-plant maize silage (WPMS). However, the impact of inoculating various strains of fermentation agents on VOC changes has not been systematically explored. This study aimed to determine how inoculation with Lactiplantibacillus plantarum and Lentilactobacillus buchneri modulates the VOC profile and odor of WPMS after 90 days. VOCs were extracted by headspace solid-phase microextraction and analyzed by gas chromatography-mass spectrometry (HS-SPME-GC-MS). Key VOCs were screened using the variable importance in projection (VIP) and substantiated by relative odor activity values (rOAV) and odor descriptions. A total of 82 compounds were identified, including 22 esters, 19 alcohols, 3 acids, 9 aldehydes, 2 ethers, 6 hydrocarbons, 4 ketones, 10 phenols, and 8 terpenoids. L. plantarum enhanced green/fruity odors while strain L. buchneri significantly reduced undesirable phenolic and aldehydic compounds. Six key VOCs influencing the odor of WPMS were selected: 4-ethyl-2-methoxyphenol and benzaldehyde, which contribute smoky, bacon, and bitter almond aromas, and (E)-3-hexen-1-ol, benzyl alcohol, (E, E)-2,4-heptadienal and methyl salicylate, which impart green, fruity, and nutty aromas. These findings highlight the effects and contributions of various strain additives on VOCs in WPMS, providing new theoretical insights for regulating the flavor profile of WPMS. Full article

Biological control with beneficial bacteria offers a sustainable alternative to synthetic agrochemicals for managing plant pathogens and enhancing plant health. However, bacterial biocontrol agents (BCAs) remain underexploited due to regulatory hurdles (such as complex registration timelines and extensive dossier requirements) and limited strain characterization. Recent advances in omics technologies (genomics, transcriptomics, proteomics, and metabolomics) have strengthened the bioprospecting pipeline by uncovering key microbial traits involved in biocontrol. Genomics enables the identification of biosynthetic gene clusters, antimicrobial pathways, and accurate taxonomy, while comparative genomics reveals genes relevant to plant–microbe interactions. Metagenomics uncovers unculturable microbes and their functional roles, especially in the rhizosphere and extreme environments. Transcriptomics (e.g., RNA-Seq) sheds light on gene regulation during plant-pathogen-bacteria interactions, revealing stress-related and biocontrol pathways. Metabolomics, using tools like Liquid Chromatography–Mass Spectrometry (LC-MS) and Nuclear Magnetic Resonance spectroscopy (NMR), identifies bioactive compounds such as lipopeptides, Volatile Organic Compounds (VOCs), and polyketides. Co-culture experiments and synthetic microbial communities (SynComs) have shown enhanced biocontrol through metabolic synergy. This review highlights how integrating omics tools accelerates the discovery and functional validation of new BCAs. Such strategies support the development of effective microbial products, promoting sustainable agriculture by improving crop resilience, reducing chemical inputs, and enhancing soil health. Looking ahead, the successful application of omics-driven bioprospection of BCAs will require addressing challenges of large-scale production, regulatory harmonization, and their integration into real-world agricultural systems to ensure reliable, sustainable solutions. Full article

Neurodegenerative diseases (NDDs) are a group of progressive diseases affecting neuronal cells in specific areas of the brain, causing cognitive decline and movement impairment. Nowadays, NDDs play a significant role in the global burden of disease, and their incidence is increasing, particularly due to population aging. NDD onset is multi-factorial; based on the current knowledge, genetic, environmental, and cellular factors are believed to contribute to their occurrence and progression. Taking into account that at an early stage, the symptoms are not clearly defined, and diagnosis may be delayed, the development of innovative and non-invasive methodological approaches for early diagnosis of NDDs is strategic for timely and tailored disease management, as well as for the overall improvement of patients’ quality of life. The present review aims to provide, in the first part, an overview based on the current level of knowledge on the environmental risk factors that can explicate a role in the onset of the most common NDDs and on the main pathogenic mechanisms involved in disease initiation and progression. The second part aims to define the current state of the art regarding the significance of Volatile Organic Compounds (VOCs) in the volatome of different human biological matrices (exhaled breath, feces, and skin sebum) as candidate biomarkers of specific NDDs, with the aim of developing non-invasive diagnostic approaches for the early diagnosis and personalized management of the patients. A critical synthesis and discussion on the applied methodological approaches and on the relevant outcomes obtained across the studies is reported. Full article

Object detection has a vital impact on the analysis and interpretation of visual scenes. It is widely utilized in various fields, including healthcare, autonomous driving, and vehicle surveillance. However, complex scenes containing small, occluded, and multiscale objects present significant difficulties for object detection. This paper introduces a lightweight object detection algorithm, utilizing YOLOv8n as the baseline model, to address these problems. Our method focuses on four steps. Firstly, we add a layer for small object detection to enhance the feature expression capability of small objects. Secondly, to handle complex forms and appearances, we employ the C2f-DCNv2 module. This module integrates advanced DCNv2 (Deformable Convolutional Networks v2) by substituting the final C2f module in the backbone. Thirdly, we designed the CBAM, a lightweight attention module. We integrate it into the neck section to address missed detections. Finally, we use Ghost Convolution (GhostConv) as a light convolutional layer. This alternates with ordinary convolution in the neck. It ensures good detection performance while decreasing the number of parameters. Experimental performance on the PASCAL VOC dataset demonstrates that our approach lowers the number of model parameters by approximately 9.37%. The mAP@0.5:0.95 increased by 0.9%, recall (R) increased by 0.8%, mAP@0.5 increased by 0.3%, and precision (P) increased by 0.1% compared to the baseline model. To better evaluate the model’s generalization performance in real-world driving scenarios, we conducted additional experiments using the KITTI dataset. Compared to the baseline model, our approach yielded a 0.8% improvement in mAP@0.5 and 1.3% in mAP@0.5:0.95. This result indicates strong performance in more dynamic and challenging conditions. Full article

The taste, aroma, and sensory characteristics of cherries are key factors influencing consumer acceptance. In this study, the sensory evaluation, biochemical characteristics, and their relationships with consumer satisfaction of several representative cherry cultivars were analyzed during cold storage to establish systematic quality evaluation parameters. Targeted metabolomics analysis revealed significant differences in physiological quality and metabolic profiles among the tested cultivars. Specifically, ‘Benitemari’ demonstrated more contents of soluble solids and titratable acid, while ‘Tieton’ and ‘Skeena’ showed higher concentrations of volatile organic compounds and polyphenolics. Furthermore, hexanal and (E)-2-hexenal were identified as the dominant VOCs, while cyanidin-3-O-rutinoside was confirmed as a major phenolic component across the cultivars. Finally, the comprehensive score of the principal component model was significantly positively correlated with the scores of firmness, chewiness, sweetness, sourness, and taste and bitterness in the sensory evaluation. The results were expected to provide valuable guidance for standardizing the sweet cherry supply chain and cultivating high-quality sweet cherry cultivars. Full article

Identifying tobacco smoke exposure in indoor environments is critical for public health, especially in vulnerable populations. In this study, we developed and validated a machine learning model to detect smoking households based on indoor air quality (IAQ) data collected using low-cost sensors. A dataset of 129 homes in Spain and Austria was analyzed, with variables including PM_2.5, PM₁, CO₂, temperature, humidity, and total VOCs. The final model, based on the XGBoost algorithm, achieved near-perfect household-level classification (100% accuracy in the test set and AUC = 0.96 in external validation). Analysis of PM_2.5 temporal profiles in representative households helped interpret model performance and highlighted cases where model predictions revealed inconsistencies in self-reported smoking status. These findings support the use of sensor-based approaches for behavioral inference and exposure assessment in residential settings. The proposed method could be extended to other indoor pollution sources and may contribute to risk communication, health-oriented interventions, and policy development, provided that ethical principles such as transparency and informed consent are upheld. Full article

Exhaled breath (EB) contains numerous volatile organic compounds (VOCs) that can reflect pathological metabolic processes, making breath analysis a promising non-invasive diagnostic approach. In particular, volatile aldehydes and ketones have been identified as disease biomarkers in EB. Gas sensors are expected to play a crucial role in the diagnosis of numerous diseases at an early stage. Among the various available approaches, sensors stand out as especially attractive tools for diagnosing diseases such as lung cancer (LC) and diabetes, due to their affordability and operational simplicity. There is an urgent need in the field of disease detection for the development of affordable, non-invasive, and user-friendly sensors capable of detecting various biomarkers. Devices of the new generation should also demonstrate high repeatability of measurements and extended operational stability of the employed sensors. Due to these demands, the past few years have seen significant advancements in the development and implementation of electronic noses (ENs), which are composed of an array of sensors for the determination of VOCs present in EB. To meet these requirements, the development and integration of advanced receptor coatings on sensor transducers is essential. These coatings include nanostructured materials, molecularly imprinted polymers, and bioreceptors, which collectively enhance selectivity, sensitivity, and operational stability. However, reliable biomarker detection in point-of-care (PoC) mode remains a significant challenge, constrained by several factors. This review provides a comprehensive and critical evaluation of recent studies demonstrating that the detection of VOCs using gas sensor platforms enables disease detection and can be implemented in PoC mode. Full article

All inorganic CsPbBr₃ possesses ideal stability in halide perovskites, but its wide bandgap and relatively poor film quality seriously limit the performance enhancement and possible applications of perovskite solar cells (PSCs). In this work, a triple-functional poly(3-Hexylthiophene) (P3HT) modifier was introduced to realize color-tunable semi-transparent CsPbBr₃ PSCs. From the optical perspective, the P3HT acted as the assistant photoactive layer, enhanced the light absorption capacity of the CsPbBr₃ film, and broadened the spectrum response range of devices. In view of the hole transport layer, P3HT modified the energy level matching between the CsPbBr₃/anode interface and facilitated the hole transport. Simultaneously, the S⁻ in P3HT formed a more stable Pb-S bond with the uncoordinated Pb²⁺ on the surface of CsPbBr₃ and played the role of a defect passivator. As the P3HT concentration increased from 0 to 15 mg/mL, the color of CsPbBr₃ devices gradually changed from light yellow to reddish brown. The PSC treated by an optimal P3HT concentration of 10 mg/mL achieved a champion power conversion efficiency (PCE) of 8.71%, with a V_OC of 1.30 V and a J_SC of 8.54 mA/cm², which are remarkably higher than those of control devices (6.86%, 1.22 V, and 8.21 mA/cm²), as well its non-degrading stability and repeatability. Here, the constructed CsPbBr₃/P3HT heterostructure revealed effective paths for enhancing the photovoltaic performance of CsPbBr₃ PSCs and boosted their semi-transparent applications in building integrated photovoltaics (BIPVs). Full article

End-of-life tyre (ELT) management is still a hot topic due to implications for sustainability and human health. This review aims to summarise the findings concerning the chemicals’ bio-accessibility/availability from the granular tyre-derived infill material used in sport surfaces. We included 14 original research articles and 5 reports (grey literature). The results included the analysis concerning volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs), phthalates, metal(loid)s and other substances. The release of some dangerous chemicals was demonstrated, even though results must be considered critically. However, the chemicals’ bioaccessibility shows a highly nuanced picture and is not, per se, sufficient to determine the risk for the exposed subjects. The lack of bioavailability and epidemiological studies analysing the exposures in real scenarios resulted in one of the main issues concerning a proper evaluation of the potential risks for human health. Full article

Aroma and flavor are central to consumer perception, product acceptance, and market positioning of animal-derived foods such as meat, milk, and eggs. These sensory traits arise from volatile organic compounds (VOCs) formed via lipid oxidation (e.g., hexanal, nonanal), Maillard/Strecker chemistry (e.g., pyrazines, furans), thiamine degradation (e.g., 2-methyl-3-furanthiol, thiazoles), and microbial metabolism, and are modulated by species, diet, husbandry, and post-harvest processing. Despite extensive research on food volatiles, there is still no unified framework spanning meat, milk, and eggs that connects production factors with VOC pathways and links them to sensory traits and consumer behavior. This review explores how production systems, feeding strategies, and processing shape VOC profiles, creating distinct aroma “fingerprints” in meat, milk, and eggs, and assesses their value as markers of quality, authenticity, and traceability. We have also summarized the advances in analytical techniques for aroma fingerprinting, with emphasis on GC–MS, GC–IMS, and electronic-nose approaches, and discuss links between key VOCs and sensory patterns (e.g., grassy, nutty, buttery, rancid) that influence consumer perception and willingness-to-pay. These patterns reflect differences in production and processing and can support regulatory claims, provenance verification, and label integrity. In practice, such markers can help producers tailor feeding and processing for flavor outcomes, assist regulators in verifying claims such as “organic” or “free-range,” and enable consumers to make informed choices. Integrating VOC profiling with production data and chemometric/machine learning pipelines can enable robust traceability tools and sensory-driven product differentiation, supporting transparent, value-added livestock products. Thus, this review integrates production variables, biochemical pathways, and analytical platforms to outline a research agenda toward standardized, transferable VOC-based tools for authentication and label integrity. Full article

Organismal biosensing leverages the olfactory acuity of living systems to detect volatile organic compounds (VOCs) associated with cancer, offering a low-cost and non-invasive complement to conventional diagnostics. Early studies demonstrate its feasibility across diverse platforms. In C. elegans, chemotaxis assays on urine samples achieved sensitivities of 87–96% and specificities of 90–95% in case–control cohorts (n up to 242), while calcium imaging of AWC neurons distinguished breast cancer urine with ~97% accuracy in a small pilot cohort (n ≈ 40). Trained canines have identified prostate cancer from urine with sensitivities of ~71% and specificities of 70–76% (n ≈ 50), and AI-augmented canine breath platforms have reported accuracies of ~94–95% across ~1400 participants. Insects such as locusts and honeybees enable ultrafast neural decoding of VOCs, achieving 82–100% classification accuracy within 250 ms in pilot studies (n ≈ 20–30). Collectively, these platforms validate the principle that organismal behavior and neural activity encode cancer-related VOC signatures. However, limitations remain, including small cohorts, methodological heterogeneity, and reliance on binary outputs. This review proposes a Dual-Pathway Framework, where Pathway 1 leverages validated indices (e.g., the Chemotaxis Index) for high-throughput screening, and Pathway 2 applies machine learning to high-dimensional behavioral vectors for cancer subtyping, staging, and monitoring. By integrating these approaches, organismal biosensing could evolve from proof-of-concept assays into clinically scalable precision diagnostics. Full article

Rice aroma is influenced by many factors, including selenium (Se) fertilizer. In this study, we investigated the effects of different Se species on the volatile organic compounds (VOCs) in three indica rice varieties over 2022 and 2023 by forliar spray. The VOCs were analyzed using HS-SPME-GC-MS. The results showed that both Se nanoparticles (SeNPs) and sodium selenite (Na₂SeO₃) significantly increased the contents of most VOCs in all three varieties, with SeNPs exhibiting a more pronounced effect. PCA and OPLS-DA revealed distinct clustering of the VOCs based on Se treatments and rice varieties. By variable importance in projection (VIP) analysis with FDR correction, Na₂SeO₃ yielded 7 markers, whereas SeNP treatment identified 18. Every marker detected under Na₂SeO₃ was fully encompassed within the SeNPs set. Three-factor ANOVA indicated that there are significant interaction effects among Se species, rice variety, and planting year. Additionally, the effect sizes were evaluated in the key VOCs to quantify the effect of Se species, rice variety, and planting year. The findings highlight Se fertilizers to enhance rice aroma and suggest selecting appropriate Se species and rice varieties for aroma improvement. Full article