Search Results (98)

Liquid chromatography—including thin-layer chromatography (TLC) and high-performance liquid chromatography (HPLC/LC)—and gas chromatography (GC) are fundamental separation techniques in analytical chemistry, used for the separation, identification, and quantification of individual components in a mixture across a wide range of applications, from biochemistry, pharmaceutical, food, and environmental research to forensic analysis [...] Full article

Exhaust gas temperature is a critical indicator of combustion efficiency and waste heat recovery in regenerative aluminum smelting furnaces, directly governing the energy intensity and carbon footprint of secondary aluminum production—a cornerstone of the circular economy. However, accurate prediction is challenged by pronounced long-range thermal lag and dynamically evolving spatial dependencies among process variables during operational transitions, which hinder real-time process optimization for energy savings. To address these challenges and advance sustainable manufacturing, a novel prediction model termed ChronoClassGAT (Chronological Class-aware Graph Attention Network) is proposed in this paper. The model integrates three key components: (1) a Temporal Convolutional Autoencoder (TCN-AE) with a Gaussian Mixture Model (GMM) for unsupervised identification of distinct operating regimes, providing categorical priors for dynamic graph construction; (2) a multi-graph fusion mechanism that builds operating-condition-specific spatial structures, enabling a Graph Attention Network (GAT) to adaptively model evolving inter-variable dependencies; and (3) a ChronoSwish activation function that modulates LSTM-based temporal encoding with time-aware periodic and switching signals, enhancing responsiveness to transient dynamics. Validated on real-world industrial datasets, ChronoClassGAT achieves superior prediction accuracy (RMSE of 2.2712, MAE of 1.8023, and $R^2$ of 0.9985) over state-of-the-art baselines. By enabling precise and robust exhaust gas temperature forecasting, our framework provides the decision-support intelligence needed for optimizing regenerator switching, minimizing thermal losses, and reducing fuel consumption, thereby contributing significantly to the operational energy efficiency and environmental sustainability of the energy-intensive non-ferrous metal industry. Full article

Understanding indoor and outdoor airborne organic mixtures, including semi-volatile organic compounds (sVOCs), remains challenging as quantitative monitoring is often costly and difficult to scale across buildings and individuals. Here we present a low-cost, miniaturised passive sampler-based methodology for static and wearable deployment to generate time-integrated chemical fingerprints and source prioritisation. New sampler devices containing replicate 9 mm sorbent discs (Tenax^® TA and/or polydimethylsiloxane) were deployed for 28 days in indoor (kitchen, bedroom) and outdoor (roadside) environments and worn by five participants; extracts were analysed by liquid extraction and gas chromatography–mass spectrometry (GC-MS) using conservative, transparent criteria for tentative compound identification. Across the household deployments, 52 compounds met inclusion criteria and distinct room-specific and outdoor chemical signatures were observed. Wearable deployments also produced differentiable chemical profiles, with greater similarity among co-inhabitants, but still could differentiate co-habitant activities based on exposure. These results demonstrate the feasibility of using miniature passive samplers to obtain reproducible, information-rich profiles that can help discriminate environments and exposure scenarios. Full article

This study presents the production of isoamyl, n-butyl and cyclohexyl esters of levulinic acid with an excellent yield under solvent-free conditions. The catalysts used were MgAlO and M²⁺MgAlO-mixed oxides containing the transition metals (M²⁺ = Co²⁺, Ni²⁺, Zn²⁺), obtained from calcined layered double hydroxides (LDH). They are easily accessible, low-cost, and environmentally friendly and possess the requisite acid–base properties for esterification reactions. The effect of reaction time and the molar ratio of levulinic acid to the alcohols used on the esterification reaction was investigated. The catalysts were characterized by X-ray diffraction (XRD), XRF, SEM and temperature-programmed desorption of CO₂ (TPD-CO₂). Gas chromatography–mass spectroscopy (GC/MS) was used for the identification and quantification of the product mixtures. Mixed oxides containing transition metals exhibited significantly higher activity than MgAlO. Under the selected reaction conditions, the conversion of levulinic acid and the yield of isoamyl ester reached 100% at a reagent ratio of 1:1. As a by-product of esterification, only dicyclohexyl ether was found at a reactant ratio of 1:1.5. Full article

Rapid and accurate gas identification and concentration prediction are of critical importance for industrial safety, medical diagnostics, and environmental monitoring. However, signal distortion in complex environments and feature loss during data processing often degrade prediction accuracy and response speed. To address these challenges, this study proposes a dual-model framework for electronic nose systems. A gas classification model transforms time-series sensor data into two-dimensional feature maps using a composite Gramian Angular Field representation and end-to-end classification using a convolutional neural network (CNN). A gas concentration prediction model integrates a multi-branch attention mechanism, a CNN, and a bidirectional gated recurrent unit to capture spatial–temporal dependencies. A cascaded identification–prediction scheme is further developed to mitigate data distribution heterogeneity and enhance model robustness. The proposed method supports both single-label and multi-label tasks and exhibits strong adaptability under complex conditions, including low concentrations, varying humidity, and gas mixtures. Validation on public and laboratory-collected datasets demonstrates that, using only initial response-stage data, the classification model achieves 100% identification accuracy, while the prediction model attains R² > 0.99 for the majority of target gases. These results confirm that the proposed framework provides an efficient and robust solution for rapid qualitative identification and quantitative prediction in electronic nose systems. Full article

Classification of vehicle engines using the chemical composition of the exhaust from these engines can be used to identify the engine’s design and verify compliance with environmental regulations through the vehicle’s emissions. This paper describes a method to identify the type of vehicles using machine learning (ML), where low-cost MQ series sensors measure the gases and particle emissions from a vehicle exhaust system, while simultaneously collecting and measuring the vehicle’s temperature and humidity levels. A custom-designed multi-sensor exhaust sensing module is employed to capture real-time exhaust emissions prior to entering the atmosphere. Exhaust samples are collected from vehicles representing three major engine categories: petrol, diesel, and compressed natural gas (CNG). In addition, fresh air samples are collected as a baseline environmental reference for comparison. All exhaust measurements are collected under controlled and consistent engine operating conditions to ensure comparable emission profiling across vehicle classes. To ensure consistent combustion-based emission profiling, this study focuses on conventional fuel-powered vehicles. MQ-series gas sensors are sensitive to combustion by-products emitted during engine operation, such as carbon monoxide (CO), hydrocarbons (HC), while also exhibiting cross-sensitivity to other gaseous components present in exhaust mixtures. Nevertheless, the proposed system performs pattern-based classification using relative sensor response signatures. Standardization of data is achieved through z-score normalization. The best models developed (based on three separate experimental designs) are trained and validated using six supervised machine learning algorithms such as Logistic Regression, Support Vector Machine (RBF), k-Nearest Neighbors, Random Forest, Gradient Boosting Decision Tree, and XGBoost and are compared against one another. Evaluation of the tested algorithms using various evaluation metrics demonstrated that ensemble models outperformed all other algorithms, achieving the highest accuracy of 99.5%. Furthermore, noise analysis confirms that the proposed solution maintains high classification accuracy (more than 89%) even under substantial sensor perturbations mimicking the real-world deployment. The solution proposed below illustrates how using gas sensors and advanced algorithms can provide accurate exhaust identification and identify engines in real-time. Full article

Tunnels excavated in non-coal oil- and gas-bearing strata may experience the seepage and intermittent ingress of an oil–gas–water mixture during construction, creating aggressive corrosive conditions that can compromise the integrity of primary support and the safety margin of the final lining. However, the coupled degradation mechanism of primary support and its cascading effect on lining safety under such conditions remain poorly understood. Based on the Huaying Mountain Tunnel project, this study investigates the corrosion-driven damage evolution of primary support and its implications for the structural safety of the secondary lining under wet–dry cycling exposure. Accelerated wet–dry cycling tests were performed on concrete specimens using an on-site crude-oil–formation-water mixture collected during tunnelling, with exposure levels ranging from 0 to 120 cycles. Laboratory observations were then combined with inverse identification of degradation-dependent material parameters to establish a corrosion-informed mechanical description, which was implemented in numerical simulations for structural response assessment. Results show a staged evolution of mechanical properties, with an initial increase followed by progressive deterioration. After 120 cycles, compressive strength, tensile strength, and elastic modulus decreased by approximately 18.9%, 23.1%, and 17.4%, respectively. Degradation is more pronounced in the corroded zone, with tensile capacity and stiffness deteriorating earlier than compressive resistance. Numerical results indicate that corrosion leads to significant stress redistribution and damage development. The sidewall tensile stress reaches 2.80 MPa after 120 cycles, exceeding the post-corrosion capacity, while the safety factor drops below the code threshold at 90 cycles. The overall safety probability decreases from 1.0 to 0.4, accompanied by a degradation in safety grade from Level I to Level IV. These findings provide a quantitative basis for deterioration assessment, safety verification, and maintenance planning for tunnels subjected to oil–gas corrosive environments. Full article

Estimating wildfire greenhouse gas (GHG) emissions in Mediterranean landscapes is challenging due to heterogeneous fuel mosaics and limited scalability of field-based approaches. This study presents a Geographic Information System (GIS) based framework that integrates land-cover data, pre-fire biomass estimates, fire severity mapping, and established emission factors to produce spatially explicit estimates of biomass consumption and GHG emissions. Fire severity was derived from multitemporal Sentinel-2 imagery using the differenced Normalized Burn Ratio (ΔNBR) and combined with land-cover information to define vegetation–severity classes for emission estimation. A key innovation is the identification of co-occurring vegetation types within the same spatial units, allowing emissions to be quantified across vegetation mixtures rather than single classes, providing a more realistic representation of Mediterranean forests. Applied to the 2022 Bejis wildfire, pre-fire biomass within the burned area was 673,601 tons. Coniferous forests dominated, but co-occurrence with shrubland and herbaceous layers produced the highest emission contributions, highlighting the role of vegetation interactions. Total emissions were estimated at 625,938 tons of equivalent CO₂, and comparison with large-scale datasets (CAMS Global Fire Assimilation System, Global Fire Emissions Database) shows general coherence. This severity-driven, vegetation-explicit framework demonstrates robust potential for quantifying wildfire emissions across heterogeneous Mediterranean landscapes, though uncertainties remain due to pre-defined biomass, burning efficiency, emission factors, assumptions in fire severity mapping, and limited field validation. The approach can support improved regional GHG inventories and wildfire management strategies. Full article

Traditionally, Lasiocephalus ovatus Schltdl. (Asteraceae) has been used as an aromatic medicinal plant, particularly in the treatment of kidney-related ailments. However, scientific evidence validating its chemical composition and bioactivity remains limited. According to our literature search, there are no previous studies on the in vitro antibacterial, antioxidant, or anti-inflammatory activities of the essential oil from the aerial parts of Lasiocephalus ovatus; therefore, this study provides the first experimental evidence of these biological activities for this species. An essential oil (EO) was steam-distilled from the aerial parts of L. ovatus, grown at 4410 m above sea level in the paramos of Chimborazo Province (Ecuador), and subsequently analyzed. The distillation yield was 0.21% (w/w) based on dry plant material. Gas chromatography was employed for qualitative (GC-MS) and quantitative (GC-FID) analyses, using two different capillary columns, coated with 5% phenyl methyl polysiloxane (non-polar) and polyethylene glycol (polar) stationary phases. Dual stationary phases were required to provide complementary selectivity, which reinforced the identification and quantification of compounds. The major components of the EO were silphinene (3.4–3.5%), δ-selinene (3.6–3.1%), β-cyclogermacrene (18.7–18.1%), kessane (4.5–4.2%), spathulenol (13.3–13.3%), viridiflorol (3.1–3.0%) and neophytadiene (4.8–4.4%), values referred to the non-polar and polar phase respectively. The enantioselective analysis revealed that (1S,5S)-(−)-α-pinene, (1S,5S)-(+)-β-pinene and (R)-(−)-α-phellandrene were enantiomerically pure, whereas germacrene D was present as a scalemic mixture. The essential oil of L. ovatus exhibited a minimum inhibitory concentration (MIC) of 250 µg/mL against Staphylococcus aureus and 500 µg/mL against Escherichia coli. Its antibacterial activity is likely associated with the presence of bioactive sesquiterpenes such as silphinene, δ-selinene, and spathulenol, which are known for their membrane-disruptive properties. Regarding its antioxidant potential, the observed moderate radical scavenging activity (SC₅₀ = of 375.7 µg/mL) can be attributed to its complex mixture, particularly to oxygenated terpenoids like viridiflorol and spathulenol, which are recognized for their radical-neutralizing capacity. In the anti-inflammatory assay, the EO’s moderate potency (IC₅₀ = 165.29 ± 4.75 μg/mL) is also consistent with the anti-inflammatory profile reported for several of its major constituents, including spathulenol and viridiflorol. While significantly lower than that of aspirin (28.85 ± 7.66 μg/mL), this bioactivity is considerable within the context of a plant extract. Overall, the antibacterial, antioxidant, and anti-inflammatory effects are consistent with the EO’s terpene-rich composition, particularly oxygenated sesquiterpenes, while the enantiomeric distribution of chiral monoterpenes may further modulate bioactivity; consequently, future studies should include enantioselective quantification, broader antioxidant assays (e.g., ABTS, FRAP, ORAC, CUPRAC), cytotoxicity at active concentrations, and mechanistic and in vivo validation. Full article

Composting chicken manure is a source of significant ammonia (NH₃) emissions, which, because of propagation, contributes to the eutrophication of the environment and decreases in air quality. Therefore, it is reasonable to use methods to limit its emission into the atmosphere. Biofiltration, using the metabolic activity of nitrifying and heterotrophic microorganisms capable of oxidizing ammonia, is an effective method to reduce ammonia emissions. In addition, the performance of the biofiltration process depends on operational parameters such as the humidity of the medium, the temperature, the contact time of the gas with the biofiltering medium, and the chemical composition and structure of the filter material. The aim of the study was to evaluate the effectiveness of biofilter fillings in reducing ammonia emissions from composting chicken manure along with the identification of factors allowing us to determine the proposed design solution as the most advantageous in terms of efficiency. Experiments on reducing odour emissions with biofiltration were carried out in two compact composting reactors, in which a compost mixture with a C:N ratio of 10:1 was used. The mixture was prepared in a ratio of 5:1 of chicken manure to the structuring material, with wheat straw used as the structuring material. Based on the results of the research on the course of the composting process, high values of ammonia concentration were recorded. Ammonia concentrations of 886 ppm (composter 1) and 811 ppm (composter 2) were recorded, which confirms the intensive nature of this gas emissions during the process of stabilizing the chicken manure. As part of the conducted research, the effectiveness of biofiltration in reducing ammonia emissions was evaluated by analysing the influence of the aeration intensity of the biofilter (20 dm³/h and 50 dm³/h), directly determining the time of contact of the gas with the bed (EBCT—Empty Bed Contact Time). Coconut-activated carbon was used as a filter bed, which was an effective carrier for the development of microorganisms responsible for the biological removal of ammonia from waste gases generated during composting. In addition, this material showed the ability to physically adsorb ammonia, thus supporting the process of its elimination. Each of the test stations has been equipped with a biofiltration installation. To determine the effectiveness of biological removal of ammonia and to assess the legitimacy of the use of selected strains of microorganisms in the process of biological removal of ammonia, the bed of one of the biofilters (biofilter 2) was inoculated with a strain of nitrifying bacteria. During the study, the high efficiency of ammonia removal because of biofiltration was noted in each of the configurations. In the case of an aeration intensity of 20 dm³/h, a reduction in emissions of 99% was achieved; with a higher aeration value, i.e., 50 dm³/h, the efficiency was 89%. These results indicate that the intensity of aeration has a significant impact on the efficiency of the biofiltration process. The analysis of a biofilter enriched with a strain of nitrifying bacteria requires long-term testing. This is important to reliably determine the effect of inoculation on the efficiency of the biological removal of ammonia in biofilters. It has been shown that optimizing these factors allows us to achieve a reduction in ammonia emissions of up to 90%, while minimizing the formation of unpleasant odours. The use of biofiltration in composting systems for organic waste of animal origin is an effective, sustainable solution that fits into the idea of sustainable development, combining the efficiency of air purification technology with environmental protection and the responsible management of resources. This study demonstrates that biofiltration using coconut-shell-activated carbon is an effective and economical method for reducing ammonia and odour emissions from composting chicken manure. The results provide valuable theoretical and practical information on emissions management in organic waste composting processes. Data from this study could be useful in developing strategies to minimize odour emissions, including from the agricultural sector. Full article

The rapid emergence of novel psychoactive substances (NPSs) after 2020 has created one of the most dynamic analytical challenges in modern forensic science. Hundreds of new synthetic cannabinoids, synthetic cathinones, synthetic opioids, hallucinogens, and dissociatives, appearing as hybrid or structurally modified analogues of conventional drugs, have entered the illicit market, frequently found in complex polydrug mixtures. This review summarizes recent advances in gas chromatography-mass spectrometry (GC-MS) for their detection, structural elucidation, and differentiation between 2020 and 2025 based on the ScienceDirect and Google Scholar databases. Due to its reproducible electron-ionization spectra, established reference libraries, and robustness toward complex matrices, GC-MS remains the primary tool for the separation and identification of emerging NPS. The current literature highlights significant improvements in extraction and pre-concentration procedures, derivatization strategies for thermally unstable analogues, and chromatographic optimization that enable discrimination between positional and stereoisomers. This review covers a wide range of matrices, including powders, herbal materials, vaping liquids, and infused papers, as well as biological specimens such as blood, urine, and hair. Chemometric interpretation of GC-MS data now supports automated classification and prediction of fragmentation pathways, while coupling with complementary spectroscopic techniques strengthens compound confirmation. The review emphasizes how continuous innovation in GC-MS methodology has paralleled the rapid evolution of the NPS landscape, ensuring its enduring role as a reliable, adaptable, and cost-effective platform for monitoring emerging psychoactive substances in seized materials. Full article

The selective identification of CH₄ in alkane gas mixtures remains challenging due to overlapping infrared absorption spectra among alkane species. This study introduces a novel algorithmic filter paradigm that fundamentally shifts from hardware-based to software-defined selectivity in Nondispersive Infrared (NDIR) sensing. Instead of relying on costly, fixed-wavelength optical filters, we employ a simplified four-source NDIR platform that deliberately captures composite spectral signals from mixed gases. A CNN-SVM hybrid model then serves as the algorithmic filter: the Convolutional Neural Network extracts discriminative features from overlapping spectra, while the Support Vector Machine performs robust classification. This integrated system achieved 89% accuracy in CH₄ identification within complex alkane mixtures. By replacing expensive optical components with intelligent algorithms, this work demonstrates a cost-effective, flexible, and scalable approach. Full article

Legal requirements are increasingly promoting the thermal treatment of sewage sludge in Germany, and alternative disposal methods are being investigated. Oxyfuel combustion is one promising thermal process for treating sewage sludge. However, the flue gas produced during the combustion process contains high levels of CO₂, a greenhouse gas that poses environmental harm. To address this issue, this study analyzed oxyfuel combustion and various CO₂ capture methods, aiming to utilize CO₂ as a feedstock for methanol production. Energy and material balance simulations were carried out using Aspen Plus. Four distinct carbon capture methods: membrane carbon capture, cryogenic carbon capture, monoethanolamine carbon capture, and ionic liquid carbon capture were modeled. Three different oxygen configurations were tested: pure air, pure oxygen, and a 50/50 air–oxygen mixture. The oxygen separation systems, including air separation units and alkaline electrolyzers, were also studied and modeled. As a result, 14 different scenarios were created. The performances, energy efficiency, and economic results of each scenario were compared to one another and to existing literature, allowing for the identification of the most effective approaches. The oxyfuel combustion scenarios achieved the highest methanol output. MEA and ionic liquid capture combined with air combustion proved to be the most cost-effective options, while cryogenic capture incurred the highest costs due to its helium-based cooling requirements. Although ASU-based oxyfuel combustion achieved the lowest specific energy requirement for methanol production, electrolysis-integrated configurations remained economically disadvantageous, underscoring the critical influence of electricity prices on the overall feasibility of the system. Full article

Substantial agro-industrial waste is generated by the food industry, including pistachio green hulls (PGH), which can constitute 40% to 60% of the fresh fruit weight. This by-product contains bioactive functional components, especially phenolic compounds (PCs). An overview of research focused on PCs extracted from PGH is presented, highlighting their chemical composition, extraction methods, compound identification, and antioxidant and antibacterial activities. Extraction techniques such as ultrasound, microwave-assisted extraction, and solid-state fermentation are utilized, with mild organic solvents like water, ethanol, methanol, or their mixtures employed. The quantification of PCs is commonly performed using the Folin–Ciocalteu assay, HCl-Butanol technique, and aluminum chloride colorimetric assays. Furthermore, identification of compounds is generally accomplished through high-performance liquid chromatography (HPLC) or gas chromatography (GC), often coupled with mass spectrometry or photodiode-array detectors to enhance accuracy and reliability. Gallic acid, kaempferol, quercetin, cyanidin, and catechin are the main PCs identified, with their antioxidant activity validated by ABTS (2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)), DPPH (2,2-diphenyl-1-picrylhydrazyl), and FRAP (ferric-reducing antioxidant power) assays. Antibacterial effectiveness has been demonstrated against bacteria using disk diffusion and minimum inhibitory concentration methods. These findings indicate potential uses of PGH by-products in the food, cosmetic, and pharmaceutical industries, contributing to a circular economy and enhancing agro-industrial waste management. Full article

Component identification and concentration estimation of a gas mixture component are important for gas detection. However, the accuracy of traditional gas identification will decrease if the sample is not balanced or the number of samples is too few. In this paper, a method based on sample expansion is proposed to solve the aforementioned problem. Firstly, the ADASYN-ELM method is proposed to identify the composition of a gas mixture component. The KPCA is used to extract the feature of the sensor signal and the ADASYN method is used to expand the samples. The PSO and GA algorithms were used to optimize the parameters of the ELM classification model to complete the qualitative analysis. Secondly, the S-SMOTE-MLSSVR method was put forward to quantitatively estimate. The S-SMOTE method was used to expand the samples, and the regression model MLSSVR was optimized by PSO and GA algorithms to complete the quantitative analysis. The results show that the accuracy rate after sample expansion is generally higher and the MAPE and RMSE are generally lower than before sample expansion, indicating that the sample expansion method has a positive effect on classification and concentration estimation of mixed gases with extremely unbalanced samples and too few samples. Full article