Search Results (84)

Photoacoustic spectroscopy (PAS) is a highly sensitive and non-destructive technique widely used for trace gas detection; however, the simultaneous quantification of methane (CH₄), ethane (C₂H₆), and ethylene (C₂H₄) remains challenging due to severe spectral cross-interference and non-linear responses across broad concentration ranges. In this work, we propose a high-precision, end-to-end detection framework based on a Deep Kernel Extreme Learning Machine (DKELM) optimized using a Mutation–Chaotic Particle Swarm Optimization (MCPSO) algorithm. To enhance diagnostic information in the photoacoustic signals, a multi-scale wavelet transform based on a db4 wavelet basis with 5-layer decomposition and a Heursure soft threshold strategy is first employed for denoising and enhancing absorption features. To address the hyperparameter sensitivity and local-optimum trapping inherent in deep models, the MCPSO algorithm integrates hybrid chaotic initialization, adaptive mutation probability control, Cauchy-based perturbation, temperature-controlled mutation amplitude, and elite-guided population updating. The proposed MCPSO-DKELM model is evaluated on an expanded dataset of 470 mixed-gas spectra and benchmarked against other frameworks, including the previously reported SVM-CPSO-KELM architecture. The experimental results demonstrate that MCPSO-DKELM achieves stable, segmentation-free quantification across the full dynamic range, with an average detection error below 3.5% and the maximum relative error constrained to under 15%, which represents a substantial improvement over existing approaches. Thus, the combination of deep kernel feature extraction and mutation–chaotic global optimization provides a robust and reliable solution for simultaneous multi-component hydrocarbon gas analysis in complex industrial environments. Full article

Ethylene oxide (EtO) has been banned in the European Union since 1991 as a fumigant for food commodities. Nevertheless, recurrent contamination incidents, especially since 2020, involving imports from India, have raised significant food safety concerns. Despite regulatory measures, EtO and its metabolite, 2-chloroethanol (2-CE), continue to be detected in a variety of food products, including dried foods, dietary supplements, and food additives. This study presents a QuEChERS-based method involving the conversion of EtO into 2-CE, which is subsequently quantified by isotope dilution gas chromatography–tandem mass spectrometry (GC-MS/MS). In contrast to previously published methods, this protocol utilises an Agilent HP-5ms Ultra Inert column (30 m × 250 μm × 0.25 mm), routinely employed in our laboratory for multi-residue pesticide analysis. The proposed approach is therefore readily adaptable to laboratories already performing multi-residue analyses, as it does not require modifications to existing instrumental configurations. The method was validated in accordance with SANTE/11312/2021 guidelines. A total of 84 samples, primarily imported from India, as well as from Brazil, Argentina, and the United Kingdom, were analysed. 2-CE was detected in four samples, and in two cases, the sum of EtO and 2-CE, expressed as EtO, exceeded the European Union (EU) maximum residue limit (MRL). Full article

Characteristic gas-based detection technology can facilitate the warning of lithium-ion battery thermal runaway with a high accuracy at an early stage. Microelectromechanical system (MEMS) metal–oxide–semiconductor (MOS) gas sensors have advantages of a low cost, a high accuracy, and low power consumption; therefore, they are ideal candidates for the lithium-ion battery thermal-runaway warning. MEMS MOS gas sensors are composed of a micro-hotplate and gas-sensitive materials. The micro-hotplate component strongly influences the device’s mechanical and thermal properties. Initially, we used COMSOL to optimize the micro-hotplate component. Then, we fabricated the device based on the optimal micro-hotplate. Next, gas-sensitive materials made of ZnO and ZnO-Au were deposited on the micro-hotplate by radio-frequency magnetic sputtering. The self-made and commercial MEMS MOS sensors were integrated to form an electronic nose. The as-made electronic nose can classify hydrogen, ethylene, acetylene, methane, carbon monoxide, and ethanol with a maximum accuracy of 99.4% using gas response data acquired over only 20 s. The reported work can provide a solution for an early and accurate lithium-ion battery thermal runaway warning. Full article

Resource-constrained edge processors deployed on unmanned aerial vehicles and wearable platforms require compact, drift-robust gas classification models for a range of environmental and security monitoring applications, including CBRN-motivated scenarios. Existing approaches rely on server-grade architectures incompatible with edge-board-scale deployment, or on classifiers that chemically degrade severely under long-term sensor drift. Each UCI gas class was mapped to a CBRN behavioral category based on physicochemical analogy (molecular functional group, vapor pressure, and metal-oxide semiconductor (MOS) cross-sensitivity pattern), following established precedent. Analyzed were Ammonia (NH₃), Acetaldehyde (CH₃CHO), Acetone ((CH₃)₂CO), Ethylene (C₂H₄), Ethanol (C₂H₅OH), Toluene (C₆H₅CH₃). We propose herein an end-to-end pipeline integrating a novel 1-D convolutional neural network with depth-wise separable convolutions (LiteSensor-Net), INT8 post-training quantization, structured magnitude pruning, and a knowledge-distillation domain-adaptation module (KD–DM) for sensor drift compensation. Using the UCI Gas Sensor Array Drift Dataset (13,910 measurements; 16 metal-oxide sensors; six analyte gases; a 36-month work span). LiteSensor-Net achieved accuracy = 92.63 ± 2.02%, macro-F1 = 0.898, model size = 5.99 kB INT8 pruned, inference latency = 6.3 ms, RAM footprint = 31.7 kB, and energy per inference = 0.04 mJ (all metrics on Raspberry Pi 4B, ARM Cortex-A72). Under chronological forward-chaining evaluation, KD–DM–20 achieved 47.91 ± 18.79% mean accuracy over Batches 2–10, representing a +9.25 pp improvement over uncompensated NC (38.66%). A six-metric benchmark framework—accuracy, macro-F1, model size, inference latency, RAM footprint, and energy per inference—is introduced to standardize edge-AI gas classifier evaluation. The proposed pipeline provides an open-source, deployable foundation for edge-class gas classification systems, with CBRN detection as a motivating application. Full operational validation on certified chemical simulants remains as future work. Full article

Microplastics (MPs) and nanoplastics (NPs) have emerged as pervasive pollutants across different aquatic systems on a global basis, yet integrated assessments linking wastewater, riverine, and marine environments remain scarce. The present study provides the first comprehensive evaluation of MPs in three interconnected aquatic matrices of Northern Greece, namely surface seawater from the Thermaic Gulf, surface freshwater from the Axios River, and influent and effluent wastewaters from the Thessaloniki WWTP (Sindos). During two sampling periods spanning late 2023 and spring 2024, suspected MPs were isolated, morphologically classified by stereomicroscopy, and chemically characterized through pyrolysis–gas chromatography/mass spectrometry (Py–GC/MS). MPs were ubiquitously detected in all substrates, exhibiting distinct spatial and compositional patterns. Seawater samples displayed moderate concentrations (1.5–4.8 items m⁻³) dominated by fibers and fragments, while riverine samples contained slightly higher levels (0.5–2.5 items m⁻³), enriched in fibrous forms and polyolefins (PE, PP). Wastewater influents showed the highest MP abundance (78–200 items L⁻¹; 155.6–392.3 µg L⁻¹), decreasing significantly in effluents (11–44 items L⁻¹; 27.8–74.3 µg L⁻¹), corresponding to a removal efficiency of 81–87.5%, being the first indicative removal efficiencies in a Greek WWTP. Among the different polymers detected, polyethylene, polypropylene, and poly(ethylene terephthalate) were identified as the most prevalent polymers across all matrices. Interestingly, a shift toward smaller size classes (125–500 µm) in effluents indicated in-plant fragmentation processes, while increased concentrations during December coincided with increased rainfall, highlighting the influence of hydrological conditions on MP fluxes. The combined morphological and polymer-specific approach provides a holistic zunderstanding of MP transport from inland to marine systems, establishing essential baseline data for Mediterranean environments and reinforcing the need for integrated monitoring and mitigation strategies. Full article

Understanding the physiological characteristics of hyperhydric plantlets is ultimately necessary since hyperhydricity results in financial loss for in vitro plants from a commercial perspective. Although many studies report the possible causes and symptoms of hyperhydricity, knowledge of it remains limited. This review aims to provide an integrated overview of this phenomenon and outline the perspectives for its prevention. First, we summarize the factors of in vitro hyperhydricity, including gelling agents, growth regulators, vessel ventilation and gas exchange, light, and osmotic conditions. Second, we describe physiological and internal changes commonly observed in hyperhydric plants, such as ROS/ethylene imbalance, altered antioxidant capacity, defects in the cell wall, and lignification. Third, we outline ultrastructural characteristics and accumulate HPLC findings to recognize the metabolite profiles of hyperhydric plantlets. Fourth, we introduce emerging AI-assisted MLM (machine learning model) approaches to detect and optimize the culture parameters to prevent hyperhydricity. Finally, we evaluate the strategies for the protection of the culture from hyperhydric conditions. This structured overview intends to reduce hyperhydricity in commercial and research settings. Full article

In₂O₃ nanoparticles were obtained by annealing precursors that had been hydrothermally/solvothermally synthesized at 200 °C using In(NO₃)₃·4.5H₂O as the starting material. Three solvents were used for the synthesis, namely water, alcohol and ethylene glycol. Urea or glycine additives were introduced into the reaction mixtures as stabilizing and structure-forming agents. The nanopowders obtained were characterized using X-ray diffraction, scanning and transmission electron microscopy, low-temperature nitrogen adsorption and X-ray photoelectron spectroscopy. The gas-sensing characteristics of the indium oxide-based sensors were investigated for the detection of hydrogen in air. It has been established that the nature of the solvent determines the phase composition and structure of indium oxide, while organic additives reduce the particle size and increase the specific surface area. It should be noted that the addition of glycine to an alcohol solution of indium nitrate during synthesis produces a phase transformation. The results show that the sensor based on In₂O₃ synthesized using this mixture has the best hydrogen sensing properties of all the materials considered in this study. Full article

Ethylene glycol (EG) is a vital industrial raw material. However, it has the potential to be hazardous to the environment and human health. High operating temperatures and long response/recovery times limit the wide application of EG sensors. Thus, we need to develop high-performance room-temperature EG-sensing materials. This paper proposes the direct hydrothermal carbonization of magnolia hair to prepare porous microtubular carbon (CMH) for room-temperature EG sensing. SEM, TEM, and XPS characterization showed that the CMH exhibited a porous microtubular structure and contained Na, which enhanced the adsorption capacity of the CMH for ethylene glycol gas. The CMH sensor exhibits a high response (156.4) to 500 ppm ethylene glycol gas at room temperature with moderate response/recovery time (14.2/37.3 s). It exhibits good linearity in measuring EG gases in the 10–100 ppm range, with a 0.292 ppm theoretical detection limit. Additionally, CMH sensors provide excellent repeatability and long-term stability. The synergistic effect of microtubule porous structure and Na doping is the main reason for enhancing the response of the sensor to EG gas. On this basis, the gas-sensitive enhancement mechanism of CMH was analyzed. The results show that biomass carbon materials provide a new method to prepare high-performance EG gas sensors. Full article

The increasing environmental concerns surrounding plastic waste have intensified recycling efforts, particularly in the textile industry, where poly(ethylene terephthalate) (PET) is widely used for sustainable material production. The growing use of recycled PET (rPET) in textiles has prompted the need for reliable analytical methods to detect and quantify rPET content. This study differentiates between virgin and recycled PET by simulating mechanical recycling through five reprocessing cycles of three distinct PET grades, assessing changes in crystalline structure, intrinsic viscosity, molecular weight, and specific degradation markers. Differential Scanning Calorimetry revealed bimodal melting behaviour in reprocessed samples, while intrinsic viscosity and Gel Permeation Chromatography indicated molecular degradation. Notably, the release of dimethyl terephthalate (DMT) and dimethyl isophthalate (DMiP) was consistently observed as a function of degradation. These markers were identified and quantified using High-Performance Liquid Chromatography (HPLC) and Gas Chromatography (GC), with GC offering higher sensitivity and lower matrix interference. This study demonstrates that DMT and DMiP are robust chemical indicators of PET degradation and recycled content. This analytical approach, combining thermal, rheological, and chromatographic techniques, provides a scientifically sound and potentially cost-effective basis for traceability systems, certification protocols, and regulatory compliance in sustainable textile production. Full article

The monitoring of gases and vapors using portable instruments is critical in a variety of fields, such as industrial and household safety, environmental monitoring, process control, and national security, owing to gas pollution. In this study, we design a portable and simple two-dimensional photonic crystal microcavity sensor for detecting gases such as ammonia, methane, carbon monoxide, acetylene, ethylene, and ethane. The basic structure of the sensor consists of silicon rods arranged in a square lattice pattern in air. Waveguide input and output channels are realized by engineering line defects within the lattice structure. Moreover, the sensor’s performance is continuously optimized by adding point defects, introducing a ring cavity, and varying the radius of the dielectric rods in the microcavity. Using the transmission spectrum obtained from the output waveguide, the performance parameters of the gas sensor are calculated. Based on the simulation analysis, the optimized gas sensor exhibits excellent performance, achieving a sensitivity S of 932.43 nm/RIU and a quality factor Q of 2421.719. Full article

Rhododendron spp., valuable ornamental plants, frequently suffer from infestations of the azalea lace bug (Stephanitis pyrioides Scott, ALB). However, the hormonal regulatory mechanisms underlying Rhododendron defense against ALB are not well understood. In this study, integrated transcriptomic and metabolomic analyses were performed to investigate the phytohormone responses under ALB stress in two Rhododendron cultivars with distinct insect susceptibility: the resistant ‘Taile’ (TL), and the susceptible ‘Yanzhimi’ (YZM). Transcriptomic sequencing identified 10,052 and 3113 differentially expressed genes (DEGs) in ‘TL’ and ‘YZM’, respectively, after ALB infestation. KEGG pathway enrichment analysis revealed that the DEGs in ‘TL’ were significantly enriched in hormone signal transduction pathways, including gibberellin (GA), jasmonic acid (JA), salicylic acid (SA), and ethylene (ETH), with 21 out of 25 hormone-related DEGs being upregulated. In contrast, ‘YZM’ exhibited 18 upregulated and 13 downregulated DEGs and suppressed auxin and cytokinin signaling. Non-targeted metabolomic analysis detected increased indole-3-acetic acid (IAA), abscisic acid (ABA), and jasmonoyl–isoleucine (JA-Ile) levels in both cultivars. ‘TL’ also showed elevated levels of SA precursor (benzoic acid) and ethylene precursor (1-aminocyclopropane-1-carboxylate, ACC). These findings indicate that ALB infestation induces endogenous hormone signaling-related genes in Rhododendron leaves and regulates hormones such as SA and JA to counteract insect stress. This study provides theoretical insights into the molecular mechanisms of Rhododendron defense against insect herbivory and lays the foundation for breeding resistant cultivars. Full article

Copper is an essential micronutrient for plants, but excessive levels can induce toxicity and impair physiological functions. This study evaluates the toxic effects of copper sulfate (CuSO₄) on the germination of common wheat (Triticum aestivum), with emphasis on the gas emission dynamics and oxidative stress biomarkers. Seeds were germinated in agar and exposed to CuSO₄ at concentrations of 1 µM, 100 µM, 1 mM, and 10 mM; distilled water served as the control. Ethylene and ammonia emissions were quantified using CO₂ laser photoacoustic spectroscopy, while electron paramagnetic resonance (EPR) spectroscopy was employed to detect free radicals and Cu²⁺ complexes. Exposure to Cu concentrations ≥ 1 mM significantly inhibited germination and biomass accumulation. Enhanced ethylene and ammonia emissions, particularly at 10 mM, indicated stress-related metabolic responses. The EPR spectra confirmed the presence of semiquinone radicals and Cu²⁺ complexes under higher Cu levels. These results demonstrate that photoacoustic and EPR techniques are effective tools for the early detection of metal-induced phytotoxicity and offer a non-invasive approach to environmental toxicity screening and plant stress assessment. Full article

Saccharina japonica (S. japonica) is a large-scale intertidal aquatic plant that exhibits characteristics such as rhizoid, holdfast, and blade differentiation. It demonstrates remarkable environmental adaptability. However, compared with higher plants, details about its phytohormone content, distribution, synthesis, and accumulation remain poorly understood. In this study, the phytohormone contents distribution and expression patterns of synthetic genes in different parts of S. japonica, including the rhizoid, petiole, basis, middle, and tip, were analyzed in detail by combining targeted metabolomics and transcriptomics analyses. A total of 20 phytohormones were detected in S. japonica, including auxin, abscisic acid (ABA), cytokinin (CTK), ethylene (ETH), gibberellin (GA), jasmonate acid (JA), and salicylic acid (SA), with significant site-differentiated accumulation. ABA and JA were significantly enriched in the tips (28.01 ng·g⁻¹ FW and 170.67 ng·g⁻¹ FW, respectively), whereas SA accumulated specifically only in the rhizoid. We also identified 12 phytohormones, such as gibberellin A1, methyl jasmonate, and trans-zeatin for the first time in S. japonica. Transcriptomic profiling revealed the tissue-specific expression of phytohormone biosynthesis genes, such as CYP735A (CTK synthesis), in the rhizoids and LOX/NCED (JA/ABA synthesis) in the tips. Key pathways, such as carotenoid biosynthesis and cysteine methionine metabolism, were found to be differentially enriched across tissues, aligning with hormone accumulation patterns. Additionally, an enrichment analysis of differentially expressed genes between various parts indicated that different parts of S. japonica performed distinct functions even though it does not have organ differentiation. This study is the first to uncover the distribution characteristics of phytohormones and their synthetic differences in different parts of S. japonica and elucidates how S. japonica achieves functional specialization through non-specific phytohormone regulation despite lacking organ differentiation, which provides an important theoretical basis for research on the developmental biology of macroalgae and their mechanisms of response to adversity. Full article

The importance and challenges of ethylene detection based on combustion-type low-cost commercial sensors for agricultural and industrial applications are well-established. This work summarizes the significant progress in ethylene detection based on an innovative Gas Metal Oxide Semiconductor (GMOS) sensor and a new catalytic composition of metallic nanoparticles. The paper presents a study on ethylene and ethanol sensing using a miniature catalytic sensor fabricated by Complementary Metal Oxide Semiconductor–Silicon-on-Insulator–Micro-Electro-Mechanical System (CMOS-SOI-MEMS) technology. The GMOS performance with bimetallic palladium–platinum (Pd-Pt) and monometallic palladium (Pd) and platinum (Pt) catalysts is compared. The synergetic effect of the Pd-Pt catalyst is observed, which is expressed in the shift of combustion reaction ignition to lower catalyst temperatures as well as increased sensitivity compared to monometallic components. The optimal catalysts and their temperature regimes for low and high ethylene concentrations are chosen, resulting in lower power consumption by the sensor. Full article

The complex nature of the hydrothermal liquefaction (HTL) of lignin product downstream requires an effective separation strategy. In this study, the use of adsorption separation was undertaken using deep eutectic solvent (DES)-modified amberlite XAD-4 adsorbents to achieve this goal. XAD-4 was modified with a choline chloride: ethylene glycol DES and characterized using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and the Brunauer–Emmett–Teller (BET) test. In addition, the HTL product was characterized using Gas Chromatography with Flame Ionization Detection (GC-FID). The performance of unmodified and DES-modified adsorbents was initially tested on the model compounds of guaiacol, phenol and catechol, followed by the HTL product in a batch adsorption system. The Freundlich model best described the model compound adsorption system with a preferential affinity for guaiacol (k_f = 12.52), outperforming phenol and catechol. Adsorption experiments showed an increase in capacity and selectivity for all species when the DES-modified adsorbents were used at all mass loadings. GC-FID analytics showed the DES-modified XAD-4 (300 mg) as having the highest selectivity for guaiacol, with an equilibrium concentration of 121.45 mg/L representing an 85.25% uptake, while catechol was the least favorably adsorbed. These results demonstrate the potential of DES-functionalized XAD-4 adsorbents in selectively isolating high-value aromatics from the HTL of the lignin product stream. Full article