Search Results (126)

Wheat, a globally vital food crop, faces severe threats from numerous foliar diseases, which often infect agricultural fields, significantly compromising yield and quality. Rapid and accurate identification of the specific disease is crucial for ensuring food security. Although progress has been made in wheat foliar disease detection using RGB imaging and spectroscopy, most prior studies have focused on identifying the presence of a single disease, without considering the need to operationalize such methods, and it will be necessary to differentiate between multiple diseases. In this study, we systematically investigate the differentiation of three wheat foliar diseases (e.g., powdery mildew, stripe rust, and leaf rust) and evaluate feature selection strategies and machine learning models for disease identification. Based on field experiments conducted from 2017 to 2024 employing artificial inoculation, we established a standardized hyperspectral database of wheat foliar diseases classified by disease severity. Four feature selection methods were employed to extract spectral features prior to classification: continuous wavelet projection algorithm (CWPA), continuous wavelet analysis (CWA), successive projections algorithm (SPA), and Relief-F. The selected features (which are derived by CWPA, CWA, SPA, and Relief-F algorithm) were then used as predictors for three disease-identification machine learning models: random forest (RF), k-nearest neighbors (KNN), and naïve Bayes (BAYES). Results showed that CWPA outperformed other feature selection methods. The combination of CWPA and KNN for discriminating disease-infected (powdery mildew, stripe rust, leaf rust) and healthy leaves by using only two key features (i.e., 668 nm at wavelet scale 5 and 894 nm at wavelet scale 7), achieved an overall accuracy (OA) of 77% and a map-level image classification efficacy (MICE) of 0.63. This combination of feature selection and machine learning model provides an efficient and precise procedure for discriminating between multiple foliar diseases in agricultural fields, thus offering technical support for precision agriculture. Full article

Rapid on-site detection of chemical warfare agents (CWAs) is vital for security and environmental monitoring. In this work, copper(I) oxide (Cu₂O) nanowire (NW) chemiresistors were investigated as gas sensors for low-concentration organophosphorus chemical warfare agent (CWA) simulants. The NWs were hydrothermally synthesized and deposited onto microheater platforms, enabling them to operate at elevated working temperatures. Their sensing performance was tested against a range of vapor-phase simulants, including dimethyl methylphosphonate (DMMP), triethyl phosphate (TEP), diethyl ethylphosphonate (DEEP), diphenyl phosphoryl chloride (DPPCl), parathion, diethyl phosphite (DEP), diethyl adipate (DEA), and cyanogen chloride (ClCN). Fully oxidized P(V) simulants (DMMP, DEEP, TEP) produced modest, predominantly reversible responses (~3–6% RR). On the contrary, DPPCl and DEP induced the strongest relative responses (RR −94.67% and >200%, respectively), accompanied by irreversible surface modification as revealed by SEM and EDS. ClCN produced a substantial but reversible negative response (RR −9.5%), consistent with transient oxidative interactions. Surface poisoning was confirmed after exposure to DEP and DPPCl, which left phosphorus or chlorine residues on the Cu₂O surface. These results highlight both the promise and limitations of Cu₂O NW chemiresistors for selective CWA detection. Full article

Avian influenza (AI), particularly the H5 subtypes, poses a significant and persistent threat globally. While the influence of environmental factors on AI seasonality is recognized, a comprehensive understanding of the hierarchical and interactive effects of multi-scale drivers in a vast and ecologically diverse country like China remains limited. We developed an interpretable machine learning framework (XGBoost with SHAP) to analyze the spatiotemporal risk of 1800 H5 AI outbreaks in mainland China from 2000 to 2023. We integrated multi-source data, including dynamic poultry density, Köppen climate classifications, Important Bird and Biodiversity Areas (IBAs), and daily meteorological variables, to identify key drivers and quantify their nonlinear and synergistic effects. The model demonstrated high predictive accuracy (5-fold cross-validation $R^2$ = 0.776). Our analysis revealed that macro-scale ecological contexts, particularly poultry density and specific Köppen climate zones (e.g., Cwa), and strong seasonality were the most dominant drivers of AI risk. We identified significant nonlinear relationships, such as a strong inverse relationship with temperature, and a critical synergistic interaction where high temperatures substantially amplified risk in areas with high poultry density. The final predictive map identified high-risk hotspots primarily concentrated in eastern and southern China. Our findings indicate that H5 AI risk is governed by a hierarchical interplay of multi-scale environmental drivers. This interpretable modeling approach provides a valuable tool for developing targeted surveillance and early warning systems to mitigate the threat of avian influenza. Full article

The author reviewed and classified maintenance reports that cited smoke, odor, or fumes (SOFs) that US airlines sent to the FAA over four years between 2018 and 2023. The US fleet composition was also calculated to put the number of SOF reports on each aircraft type in perspective. “Fume events” (engine oil or hydraulic fluid) were the most common type of onboard SOFs reported by US airlines (43%), followed by electrical (20%), and fans (6.1%). During these years, A320fam aircraft made up 20% of the US fleet but 80% of the reported fume events. Conversely, B737fam aircraft made up 27% of the US fleet but only 3.0% of the reported fume events. Aircraft design features, airline reporting practices, and maintenance procedures that may contribute to these differences were reviewed. Pilots were most likely to document a fume event during descent (47%) and takeoff/climb (19%). The A320fam, MD80fam, A330, and ERJ140-145 aircraft were over-represented in other types of SOFs reports. Airline narratives show that the APU can be the primary source of oil/hydraulic fumes, even when it is not operating. Additionally, failure to find the source of fumes, rectify it, and clean any secondary sources of fumes can cause repeat events. Full article

Chemical warfare agents (CWAs), including hydrogen cyanide (AC), 2-[fluoro(methyl)phosphoryl]oxypropane (GB), 3-[fluoro(methyl)phosphoryl]oxy-2,2-dimethylbutane (GD), ethyl S-(2-diisopropylaminoethyl) methylphosphonothioate (VX), and di-2-chloroethyl sulfide (HD), pose a great threat to public safety; therefore, it is important to develop sensing technology for CWAs. Herein, a sensor array consisting of 24 metal oxide semiconductor (MOS)-based MEMS sensors with good gas sensing performance, a simple device structure (0.9 mm × 0.9 mm), and low power consumption (<10 mW on average) was developed. The experimental results show that there are always several sensors among the 24 sensors that show good sensing performance in relation to each CWA, such as a relatively significant response, a broad detection range (AC: 5.8–89 ppm; GB: 0.04–0.47 ppm; GD: 0.06–4.7 ppm; VX: 9.978 × 10⁻⁴–1.101 × 10⁻³; HD: 0.61–4.9 ppm), and a low detection limit that is lower than the immediately dangerous for life and health (IDLH) level of the five CWAs. This indicates that these sensors can meet the needs for qualitative detection and can provide an early warning regarding low concentrations of CWAs. In addition, features were extracted from the initial kinetic characteristics and dynamic change characteristics of the sensing response. Finally, principal component analysis (PCA) and machine learning algorithms were applied for CWA classification. The obtained PCA plots showed significant differences between groups, and the narrow neural network among the machine learning algorithms achieves a prediction accuracy of nearly 100.0%. In summary, the proposed MOS-based MEMS sensor array driven by pattern recognition algorithms can be integrated into portable devices, showing great potential and practical applications in the rapid, in situ, and on-site detection and identification of CWAs. Full article

This is a broad look at the history of phosphorus—from the element through its inorganic and organic compounds to the applications of organophosphates. In addition to commercial and peaceful applications, they were used as chemical warfare agents (CWA), both in military operations and for terrorist purposes. This article attempts to provide a concise history of their development and application in this shameful role. The origin of the chemistry of phosphorus compounds to obtain precursors for the production of CWA is presented. Rapid progress in organophosphorus chemistry in the second half of the 20th century is also described. A broad overview of chemical structures is presented, including lesser-known representatives. The mode of action and the associated toxicity of organophosphorus compounds are briefly mentioned. The Chemical Weapons Convention (CWC) schedules and their changes during their validity are indicated. They are also demonstrated to be used in proficiency tests organised by the Organization for the Prohibition of Chemical Weapons (OPCW). Organophosphates called “Novichok agents”, classified as fourth-generation chemical warfare agents, are also briefly discussed. Full article

In recent decades, adopting alternative resources in infrastructure applications has garnered global attention to address environmental concerns. Olive waste ash (OWA), a locally available byproduct obtained from the olive oil production process, is a promising green material that plays a vital role as a partial cement substitute. This paper evaluates the mechanical and durability properties of cement paste—a key component of paving blocks—incorporating OWA at replacement levels of 0, 5, 10, 15, and 20%, with a constant water-to-cementitious ratio of 0.45. Density, compressive strength, and flexural strength are assessed at 1, 7, 28, and 90 days, while total water absorption (TWA) and capillary water absorption (CWA) are measured at 28 days. The results reveal that OWA slightly reduces density, compressive strength, and flexural strength, with the optimal results observed at a substitution level of 10%. At 90 days, the compressive strength of the control cement paste is 50 MPa, whereas the 10% OWA mixture exhibits a value of 46 MPa, corresponding to only an 8% reduction. Additionally, two predictive models are proposed: the hyperbolic model for compressive strength variation with curing time and the capillary-diffusive model for capillary water absorption as a function of time. Both models demonstrate a strong fit with experimental data. Correlations between different properties indicate a strong correlation between compressive strength, density, and flexural strength, while a negative linear relationship exists between compressive strength and water absorption. This study underscores OWA’s potential to improve sustainable paving blocks by providing suitable mechanical and durability characteristics, offering both environmental and economic benefits. Full article

The decontamination of chemical warfare agents (CWAs) from contaminated surfaces is of critical importance due to the severe threats posed by CWAs to human health and the environment, particularly given the persistent threat of chemical weapons since World War I. In this study, a novel UiO-66-NH₂ crosslinked hyaluronic acid (HA) hydrogel was developed in the presence of polyvinyl alcohol under ambient conditions, leveraging the dual functionality of the amino-substituted zirconium-based metal–organic framework (Zr-MOF) as both a crosslinker and a catalytic site. The hydrogel demonstrated exceptional catalytic performance, achieving a degradation efficiency of over 90% for the chemical warfare agent simulant dimethyl 4-nitrophenyl phosphate (DMNP) within 1 h. Furthermore, the hydrogel demonstrated adequate mechanical and tensile strength for practical use, enabling easy peel-off from various contaminated surfaces without leaving residues. This peelable property, combined with its decontamination capabilities, highlights its significant potential for practical applications in the field of CWA decontamination. Full article

This review summarizes the application of two-dimensional metal–organic framework (2D MOF) nanostructures and their composites in the detoxification of chemical warfare agents (CWAs). Two-dimensional MOFs, characterized by their high specific surface area, abundant active sites, and structural tunability, exhibit promising catalytic performance in CWA detoxification. Various preparation methods, including top–down exfoliation and bottom–up assembly, are discussed for the synthesis of 2D MOF nanosheets. The catalytic performance of 2D MOFs and their composites in detoxifying CWAs is evaluated, highlighting their advantages in terms of reaction kinetics and ease of recycling. Additionally, the advances and challenges in this field are discussed, aiming to promote further research into and development of 2D MOF-based materials for CWA detoxification. Full article

The course and kinetics of the reactions of sulfur mustard, nitrogen mustard and their selected analogues with sodium ethoxide were studied using a gas chromatograph coupled with a mass spectrometer. 2-chloroethyl ethyl sulfide (CEES), a monofunctional analogue of sulfur mustard (HD), bis(2-chloroethyl) ether (BCEE), an oxygen analogue of sulfur mustard, and bis(2-chloroethyl)amine, an analogue of nitrogen mustard HN-3, in which one hydrogen atom remains unsubstituted with a chloroethyl group, were used as imitators of mustards. For the study, the last mentioned compound was given the acronym HN-0. The research included checking how the form of sodium ethoxide influences the reaction rate. Two solutions were used: sodium ethoxide solution obtained by dissolving a commercially available compound in crystalline form and ethoxide solution obtained by dissolving sodium in ethanol. Additionally, the extent to which diethylenetriamine (DETA) accelerates the reactions of the studied compounds with sodium ethoxide was checked. The decontamination reactions were carried out in an anhydrous environment at a constant temperature of 25.0 °C. The rate of the mustard decontamination reaction increased significantly in systems containing DETA. Therefore, this amine can be used as a catalyst for this reaction. DETA has the most significant effect on the rate of the reaction of sodium ethoxide with CEES. The effect of the EtONa form was tested in the decontamination reaction of HD, revealing that both forms are equally effective, with only minor differences in reaction rates. Freshly synthesised sodium ethoxide reacts with HD 1.24 times faster. The study also assessed whether selected non-CWA compounds can be successfully used in studies as mustard imitators. Nitrogen mustard and bis(2-chloroethyl)amine reactions proceed according to the same mechanism—nucleophilic substitution. Bis(2-chloroethyl)amine reacts slightly faster than HN-3, both in solution with and without the addition of a catalyst. Sulfur mustard (HD) and CEES with sodium ethoxide and DETA undergo an elimination reaction, while BCEE undergoes a substitution reaction, which proceeds much slower. The observed differences disqualify BCEE as a sulfur mustard imitator. HD and CEES react with sodium ethoxide and DETA so quickly that the exact kinetic parameters under the developed experimental conditions could not be determined. Full article

This study proposes a deep neural network (DNN) as a downscaling framework with nonlinear features extracted by kernel principal component analysis (KPCA). KPCA utilizes kernel functions to extract nonlinear features from the source climatic data, reducing dimensionality and denoising. DNN is used to learn the nonlinear and complex relationships among the features extracted by KPCA to predict future regional rainfall patterns and trends in complex island terrain in Taiwan. This study takes Taichung and Hualien, on both the eastern and western sides of Taiwan’s Central Mountain Range, as examples to investigate the future rainfall trends and corresponding uncertainties, providing a reference for water resource management and usage. Since the Water Resources Agency (WRA) of the Ministry of Economic Affairs of Taiwan currently recommends the CMIP5 (AR5) GCM models for Taiwan regional climate assessments, the different emission scenarios (RCP 4.5, RCP 8.5) data simulated by two AR5 GCMs, ACCESS and CSMK3, of the IPCC, and monthly rainfall data of case regions from January 1950 to December 2005 in the Central Weather Administration (CWA) in Taiwan are employed. DNN model parameters are optimized based on historical scenarios to estimate the trends and uncertainties of future monthly rainfall in the case regions. The simulated results show that the probability of rainfall increase will improve in the dry season and will reduce in the wet season in the mid-term to long-term. The future wet season rainfall in Hualien has the highest variability. It ranges from 201 mm to 300 mm, with representative concentration pathways RCP 4.5 much higher than RCP 8.5. The median percentage increase and decrease in RCP 8.5 are higher than in RCP 4.5. This indicates that RCP 8.5 has a greater impact on future monthly rainfall. Full article

Di-isopropyl methyl phosphonate (DIMP) has no major commercial uses but is a by-product or a precursor in the synthesis of the nerve agent sarin (GB). Also, DIMP is utilized as a simulant compound for the chemical warfare agents sarin and soman in order to test and calibrate sensitive IMS instrumentation that warns against the deadly chemical weapons. DIMP was measured from 2 ppb_v (15 μg m⁻³) to 500 ppb_v in the air using a pocket-held ToF ion mobility spectrometer, model LCD-3.2E, with a non-radioactive ionization source and ammonia doping in positive ion mode. Excellent sensitivity (LoD of 0.24 ppb_v and LoQ of 0.80 ppb_v) was noticed; the linear response was up to 10 ppb_v, while saturation occurred at >500 ppb_v. DIMP identification by IMS relies on the formation of two distinct peaks: the monomer M·NH₄⁺, with a reduced ion mobility K₀ = 1.41 cm² V⁻¹ s⁻¹, and the dimer M₂·NH₄⁺, with K₀ = 1.04 cm² V⁻¹ s⁻¹ (where M is the DIMP molecule); positive reactant ions (Pos RIP) have K₀ = 2.31 cm² V⁻¹ s⁻¹. Quantification of DIMP at trace levels was also achieved by GC-MS over the concentration range of 1.5 to 150 μg mL⁻¹; using a capillary column (30 m × 0.25 mm × 0.25 μm) with a TG-5 SilMS stationary phase and temperature programming from 60 to 110 °C, DIMP retention time (RT) was ca. 8.5 min. The lowest amount of DIMP measured by GC-MS was 1.5 ng, with an LoD of 0.21 μg mL⁻¹ and an LoQ of 0.62 μg mL⁻¹ DIMP. Our results demonstrate that these methods provide robust tools for both on-site and off-site detection and quantification of DIMP at trace levels, a finding which has significant implications for forensic investigations of chemical agent use and for environmental monitoring of contamination by organophosphorus compounds. Full article

Mustard gas (HD) is a well-known chemical warfare agent, recognized for its extreme toxicity and severe hazards. Metal–organic frameworks (MOFs), with their unique structural properties, show significant potential for HD adsorption applications. Due to the extreme hazards of HD, most experimental studies focus on its simulants, but molecular simulation research on these simulants remains limited. Simulation analyses of simulants can uncover structure–performance relationships and enable experimental validation, optimizing methods, and improving material design and performance predictions. This study integrates molecular simulations, machine learning (ML), and molecular fingerprinting (MFs) to identify MOFs with high adsorption performance for the HD simulant diethyl sulfide (DES), followed by in-depth structural analysis and comparison. First, MOFs are categorized into Top, Middle, and Bottom materials based on their adsorption efficiency. Univariate analysis, machine learning, and molecular fingerprinting are then used to identify and compare the distinguishing features and fingerprints of each category. Univariate analysis helps identify the optimal structural ranges of Top and Bottom materials, providing a reference for initial material screening. Machine learning feature importance analysis, combined with SHAP methods, identifies the key features that most significantly influence model predictions across categories, offering valuable insights for future material design. Molecular fingerprint analysis reveals critical fingerprint combinations, showing that adsorption performance is optimized when features such as metal oxides, nitrogen-containing heterocycles, six-membered rings, and C=C double bonds co-exist. The integrated analysis using HTCS, ML, and MFs provides new perspectives for designing high-performance MOFs and demonstrates significant potential for developing materials for the adsorption of CWAs and their simulants. Full article

The increasing incidence of organophosphate (OP) pesticide poisoning and the use of OP chemical warfare agents (CWA) in conflicts and terrorist acts need sustainable methods for sensing, decontamination, and detoxification of OP compounds. Enzymes can serve as specific, cost-effective biosensors for OPs. We will report on recent advancements in the use of carboxylesterases from the Hormone-Sensitive Lipase for the detection of OP compounds. In addition, enzymatic-based OP detoxification and decontamination offer long-term, environmentally friendly benefits compared to conventional methods such as chemical treatment, incineration, neutralization, and volatilization. Enzymatic detoxification has gained attention as an alternative to traditional OP-detoxification methods. This review provides an overview of the latest research on enzymatic sensing and detoxification of OPs, by exploiting enzymes, isolated from thermophilic/extremophilic Bacteria and Archaea that show exceptional thermal stability and stability in other harsh conditions. Finally, we will make examples of integration between sensing and decontamination systems, including protein engineering to enhance OP-degrading activities and detailed characterization of the best variants. Full article

We report the exfoliation of ultrathin gallium oxide (Ga₂O₃) films from liquid metal balloons, formed by injecting air into droplets of eutectic gallium–indium alloy (eGaIn). These Ga₂O₃ films enable the selective adsorption of carbon nanotubes (CNTs) dispersed in water, resulting in the formation of a dense, percolating CNT network on their surface. The self-assembled CNT network on Ga₂O₃ provides a versatile platform for device fabrication. As an example application, we fabricated a chemiresistive gas sensor for detecting simulants of chemical warfare agents (CWAs), including diisopropyl methylphosphonate (DIMP), dimethyl methylphosphonate (DMMP), and triethyl phosphate (TEP). The sensor exhibited reversible responses, high sensitivity, and low limits of detection (13 ppb for DIMP, 28 ppb for DMMP, and 53 ppb for TEP). These findings highlight the potential of Ga₂O₃ films derived from liquid metal balloons for integrating CNTs into functional electronic devices. Full article