Search Results (368)

Historic blue bricks are fundamental to Beijing’s architectural heritage, yet cross-site compositional data for guiding material-compatible restoration remain scarce. This study applies WD-XRF, XRD, SEM, thermal expansion measurement, and physical property testing to 21 blue brick specimens from four Beijing-area sites spanning the Tang through Qing dynasties, with PCA and K-means clustering used to explore compositional grouping structures. Within this exploratory dataset, a compositional distinction separates the Ming and Qing Great Wall bricks: CaO falls from 7.7 to 1.5 wt.% as anorthite gives way to albite, while Qing specimens are denser (1.79 vs. 1.65 g·cm⁻³) with lower water absorption (15.9% vs. 20.9%). Two Wanping City bricks are strongly sulfate-enriched (SO₃ up to 9.8%), and WP-SE3 additionally carries a heavy chloride load (Cl 2.1%), masking their original clay signatures and illustrating how unrecognized weathering can distort compositional grouping and source-related interpretation from bulk chemistry. K-means clustering yields compositional types that overlap only partially with site boundaries, capturing raw material variation rather than site-specific manufacturing fingerprints. Despite constraints in sample size and physical property coverage, the integrated dataset offers preliminary compositional benchmarks and limited performance data to inform period-specific brick replacement at these heritage sites. Full article

The aim of this research was to identify the wealth of secondary metabolites in the Chilean lichen Sticta caulescens, applying green chemistry approaches and comparing the following two extraction methods: (a) conventional maceration with methanol, and (b) microwave-assisted extraction (MAE) using ethyl lactate as a solvent. In addition, chemoinformatic and chemotaxonomic studies were conducted on S. caulescens and other species of the genus Sticta, which have been reported in previous studies. A UHPLC/ESI-MS/MS analysis allowed for the identification of 32 metabolites obtained from maceration and 33 from MAE, considering carbohydrates, aromatic compounds, acids, depsides, depsidones, dibenzofurans, lipids, anthraquinones, and triterpenes. Maceration using methanol yielded a slightly higher extract percentage than with ethyl lactate (6.3% versus 5.0%), while MAE extracted an almost identical spectrum of metabolites using ethyl lactate,—though including one compound detected only under MAE conditions. This highlighted both the method efficiency and selectivity. This study also incorporates a comprehensive chemoinformatic and chemotaxonomic analysis of secondary metabolites across 12 Sticta species. A computational comparison (Morgan fingerprints, Jaccard similarity, hierarchical clustering, Murcko scaffolds) demonstrated that S. caulescens is one of the most chemically diverse species, closely related to S. cordillerana, and forming part of a major chemotaxonomic lineage, which is characterized by high scaffold richness and shared aromatic/depsidone biosynthetic pathways. Full article

Severe cutaneous adverse reactions (SCARs) are rare, life-threatening drug hypersensitivity syndromes. Although pharmacovigilance can identify drugs disproportionately reported with SCARs, it does not reveal which local chemistries recur among them. To address this, we assessed whether drugs with FAERS-derived SCAR disproportionality signals share interpretable chemical motifs. We screened FAERS data from 2004Q1 to 2024Q3, identified 5523 drugs with available Simplified Molecular-Input Line-Entry System (SMILES) representations, and constructed a signal-enriched dataset of 1676 compounds with nominally significant broad-SCAR associations after excluding predefined therapeutic/supportive confounders. Compounds were assigned to positive-signal [natural logarithm of reporting odds ratio (lnROR) > 0, n = 1219] or non-positive-signal (lnROR ≤ 0, n = 457) classes and encoded with 9753 explicitly mappable atom-centered local substructure descriptors. A LightGBM signal-classification model evaluated using random repeated nested cross-validation (six-fold outer × 50 repeats) achieved moderate internal discrimination (mean area under the receiver operating characteristic curve = 0.7041 ± 0.0337). Descriptor-space cluster-based repeated nested cross-validation, designed to reduce train–test structural leakage, yielded lower but still above-chance performance (mean ROC AUC = 0.6409; permutation p = 0.001), indicating that random-split estimates should be interpreted as optimistic for structurally novel compounds. Sensitivity analyses using minimum SCAR case-count thresholds and retention of predefined therapeutic/supportive drugs showed broadly similar performance and motif rankings. SHapley Additive exPlanations (SHAP) analysis revealed a fragment-level contrast: allylamine-like, ethanolamine-related, and diaminopropane-related motifs were associated with higher positive-signal class probability, whereas phenol and pyrimidine motifs were associated with lower positive-signal class probability. These findings suggest that FAERS-derived broad-SCAR signal direction is not chemically random within the selected dataset. Overall, the proposed framework should be viewed not as a direct predictor of absolute clinical SCAR risk but as an exploratory, pharmacovigilance-driven cheminformatics approach for prioritizing compounds and motif families for further SCAR-focused evaluation. Full article

The chemical complexity of post-consumer plastics represents a major challenge for achieving high-quality recycling. In this study, post-consumer high-density polyethylene (HDPE) packaging materials were analysed using pyrolysis–gas chromatography–mass spectrometry (Py-GC/MS) to investigate relationships between compound origin, degradation pathways, and contaminant profiles. More than one hundred organic compounds were detected and classified into four main groups: product-related inputs, polymer formulation chemistry, polymer degradation processes, and external contamination. Polymer degradation products, particularly radical rearrangement and cyclisation compounds, represented the most diverse group, indicating advanced transformation of the polymer matrix associated with repeated processing. Additive-derived compounds, including phenolic structures and epoxide-containing species, contributed to the pool of non-intentionally added substances (NIAS), while persistent compounds, such as fluoropolymer-derived residues, were detected across most samples. In contrast, product-related inputs showed high variability and a generally lower contribution. Multivariate analysis revealed that samples were not clustered according to product category but rather distributed along gradients defined by degradation, additive transformation, and contamination processes. These findings demonstrate that the chemical composition of recycled HDPE is determined or influenced by multiple independent factors. The results support the need for chemistry-informed recycling strategies. Full article

Organometallic chemistry deals with the synthesis, structure, reactivity, and applications of compounds containing metal–carbon covalent bonds. In recent years, there has been a growing interest in predicting the catalytic activity of organometallics using machine learning. However, the major drawback in developing algorithms that can be used in predicting organometallic reactions is the availability of organometallic reaction data and organometallic filtering tools. The main aim of the current study is to develop organometallic reaction-filtering tools that are crucial for building accurate and effective ML models in organometallic chemistry. Random Forest (RF), K-Nearest Neighbors (kNN), Support Vector Classifiers (SVC), and Multi-Layer Perceptrons (MLP) were employed, using feature subsets selected via Permutation Feature Importance from Morgan fingerprints and MACCS keys. The results demonstrate that the MACCS-based MLP architecture provides the most reliable filtering performance, achieving a superior F1 score of 0.85, a Recall of 0.85, and a high AUC-ROC of 0.837. Furthermore, the MACCS-MLP exhibited the highest predictive confidence, yielding the study’s lowest Log Loss of 0.312. In contrast, while Morgan fingerprints paired with kNN offered a specialized “strict” filter with absolute Precision (1.00), the sparse dimensionality of circular fingerprints generally resulted in lower calibration for probabilistic models. These findings underscore that dense, fragment-based descriptors refined by data-driven feature selection are most effective for identifying complex organometallic motifs. This study successfully provides a validated methodology for building precise filtering tools, establishing a critical foundation for automated catalyst discovery and the expansion of effective machine learning applications in organometallic chemistry. The study is limited to only identifying organometallic reactions and cannot filter based on organometallic reaction types. Future studies should also explore integrating multiple feature representations to classify or cluster the identified organometallic reactions based on the reaction types. Full article

Celestine (SrSO₄) is the principal ore of Sr and a sensitive tracer of diagenetic fluid–rock interaction in carbonate–evaporite successions. This study presents integrated petrographic mineral chemistry and Sr–S isotopic data for epigenetic celestine hosted by Asmari carbonates at Jabal Hafit, Al Ain (UAE), to constrain fluid source, and mechanisms of SrSO₄ precipitation during basin diagenesis. Field and SEM observations show celestine as stratabound, vug- and fracture-filling euhedral to subhedral crystals within dolomitised limestone, suggesting precipitation after initial lithification during early-to-mid burial diagenesis. Electron microprobe analyses show nearly stoichiometric SrSO₄ (55.15–57.30 wt.% SrO; 42.43–44.35 wt.% SO₃) with very low Ba and Ca. The characteristically high Sr/Ba signature of the celestine reflects a complex diagenetic history driven by efficient Sr remobilisation during carbonate recrystallisation within an inherently Ba-poor marine sequence. Measured ⁸⁷Sr/⁸⁶Sr ratios are tightly clustered (0.707841–0.707854) with a high degree of isotopic homogeneity, which indicates a stable, well-buffered fluid reservoir, while the absolute values align with an Oligocene marine signature. Sulphur isotope values (δ³⁴S = +27.3 to +29.1‰) are enriched relative to coeval marine sulphate, which could be attributed to closed-system Rayleigh fractionation driven by bacterial sulphate reduction. We propose that celestine precipitated from stable, marine-buffered burial brines, where supersaturation was achieved through coupled Sr enrichment from carbonate diagenesis and microbial modification of the sulphate reservoir. Full article

A significant fraction of the HF waves is absorbed by the lowest ionospheric layer, the D-region. This region is perturbed by solar flares, which notably cause fast increases in the Sun’s X-ray flux. We present here a new chemistry model, the “Lower Ionosphere Region–Absorption and Chemistry Modelling” (LIR-ACheM), to study the D-region behaviour. It is based on the Mitra–Rowe scheme and takes into account four distinct sources (EUV, Lyman-$\alpha$, X-rays and cosmic rays) and seven species (electrons, $N{O}^{+}$, $O_2^{+}$, $O_4^{+}$, positive cluster ions, $O_2^{-}$ and other negative ions). It thus offers a compromise between accuracy and computing time. The D-region’s sluggishness and its recovery time after a flare are analysed, highlighting the importance of detachment at low altitudes and soft X-ray fluxes above 80 km. Full article

Molybdenum blue dispersions were synthesized via an ion-exchange approach using hydroquinone and glucose as reducing agents to clarify the influence of reductant chemistry on redox evolution and colloidal stability. Electrolyte-free conditions enabled controlled self-assembly of reduced polyoxomolybdate clusters. UV–Vis spectroscopy revealed characteristic absorption bands at ~750 and ~1100 nm associated with intervalence charge transfer in mixed-valence Mo⁵⁺/Mo⁶⁺ clusters, with hydroquinone stabilizing more deeply reduced clusters, while glucose-derived systems demonstrated a higher degree of reduction with a higher ratio of reducing agent to metal. Time dependence of oxidation–reduction potential and optical density measurements demonstrated prolonged redox equilibration and gradual self-organization over several weeks. Dynamic light scattering confirmed the formation of nanoclusters with comparable hydrodynamic diameters of approximately 3.5 nm for both reducing agents. Raman and FT-IR spectroscopy indicated structurally similar polyoxomolybdate frameworks. In contrast, electrokinetic measurements revealed pronounced differences in surface chemistry and stability: hydroquinone-derived dispersions exhibited robust, pH-independent electrostatic stabilization, whereas glucose-derived systems showed weaker, pH-dependent stabilization and rapid electrolyte-induced aggregation. These results demonstrate that the nature of the reducing agent has an impact on the synthesis and colloidal behavior of molybdenum blue dispersions synthesized by the ion-exchange route. Full article

Direct Coal Liquefaction (DCL) is a promising route for converting abundant coal resources into liquid fuels, yet its efficiency remains strongly dependent on catalyst performance. In this work, we present an integrated computational framework combining density functional theory (DFT) calculations with machine learning (ML) to investigate iron–sulfur (FeS) cluster catalysts for DCL. DFT calculations were employed to examine hydrogen-donor dissociation and coal-derived radical hydrogenation on representative FeS clusters. The results indicate that the most favorable catalytic pathways arise from the cooperation between metallic Fe sites (Fe_2) and interfacial Fe sites adjacent to sulfur (Fe_1), while sulfur atoms mainly play an indirect structural and electronic modulation role. Based on these mechanistic insights, a database containing thermodynamic and kinetic data for 636 reactions across 50 FeS cluster models was constructed. This dataset was then used to train three ML classifiers, among which the Random Forest model showed the best performance, reaching accuracies of 80% for H-donor cleavage and 93% for radical hydrogenation on the held-out test sets. SHapley Additive exPlanations (SHAP) analysis further showed that descriptors associated with Fe active-site identity were among the most influential variables in both tasks. Overall, this work provides a mechanistically informed and interpretable computational framework for understanding FeS-catalyzed DCL chemistry and for the preliminary screening of catalyst motifs within the chemical space covered by the present FeS cluster library. Full article

CO₂ adsorption on subnanometric metal clusters is highly sensitive to the computational protocol used to describe the potential energy surface, particularly when several low-lying geometries and spin states are accessible. In this work, CO₂ adsorption on Cu₄ and Sc₄ clusters was investigated using density functional theory (DFT) to evaluate how the choice of functional/basis-set protocol, spin multiplicity, initial geometry, and vibrational stability affects the predicted adsorption behavior. Four representative computational protocols (TPSSh, r²SCAN-3c, PBE-D4/def2-TZVP, and PBE0-SDD) were assessed for isolated clusters and cluster–CO₂ complexes. The lowest harmonic vibrational frequency, ω_min, was used as a diagnostic criterion to distinguish true minima from unstable or weakly defined stationary points. Selected cases were also cross-checked using the ORCA and Gaussian quantum-chemistry packages to assess whether comparable computational settings yielded consistent stationary-point character. The results show that Cu₄ generally exhibits weak CO₂ binding, whereas Sc₄ displays stronger but more protocol-dependent adsorption, consistent with its higher structural flexibility and more pronounced Lewis-acid character. Low-frequency and imaginary modes were found in several optimized structures, indicating that adsorption energies should not be interpreted without prior vibrational validation. The comparison also shows that variations in functional/basis-set treatment and spin multiplicity can alter both the optimized geometry and the predicted adsorption strength. Therefore, CO₂ adsorption on small metal clusters should be discussed using combined structural, vibrational, and energetic criteria rather than electronic adsorption energies alone. Overall, this study provides a protocol-oriented framework for evaluating the reliability of DFT predictions in CO₂ adsorption on Cu₄ and Sc₄ clusters. Full article

Standard laboratory protocols for simulating short-term asphalt aging, including the Thin-Film Oven Test (TFOT) and Rolling Thin-Film Oven Test (RTFOT), are widely adopted but frequently lack sensitivity to the distinct thermo-oxidative kinetics of high-viscosity and polymer-modified systems. This study evaluates a severity-graded aging matrix incorporating the Pressure Aging Vessel (PAV) at variable durations (2, 5, and 10 h at 163 °C/2.1 MPa) as a potential alternative to conventional thin-film methods. Three binder systems BA-70 (PG 64-22), SBS-modified, and compatibilized functional thermoplastic (CFT)-modified asphalt were subjected to TFOT, RTFOT, and PAV variants. Comprehensive rheological characterization (DSR frequency/temperature sweeps, rutting parameter, MSCR) and SARA fractionation were employed to quantify oxidative stiffening, permanent deformation resistance, and compositional evolution. An Aging Severity Index (ASI) was developed to normalize multi-parameter responses and establish quantitative protocol equivalence thresholds. BA and SBS-modified binders exhibited pronounced protocol-dependent stiffening, with PAV-5h vs. RTFOT ASI gaps of 30.0% and 33.0%, respectively, confirming distinct aging severity under the tested conditions. Conversely, the CFT-modified binder demonstrated a compressed aging signature, maintaining stable complex modulus, minimal non-recoverable compliance escalation, and near-complete elastic recovery across all protocols. The ASI gap between PAV-5h and RTFOT for CFT was 6.0%, falling within the pre-defined ≤7% equivalence threshold established from combined rheological test uncertainty, specification-aligned engineering tolerance, and empirical gap clustering. SARA analysis corroborated these findings, showing CFT retained higher aromatic/resin fractions while limiting asphaltene accumulation compared to BA-70 and SBS. Importantly, the observed interchangeability between PAV-5h and RTFOT is strictly limited to the specific CFT-modified binder formulation tested under laboratory conditions. Broader specification adoption requires targeted validation across diverse modifier chemistries, dosages, and field-aged binders before generalization. Full article

Thiocarbohydrazone derivatives represent a highly significant class in medicinal chemistry, characterized by a versatile scaffold defined with a thiocarbonyl (C=S) core and one or two imine (–C=N–) functionalities, allowing for precise modulation of their physicochemical and biological properties. The biological potential of a series of novel bis-substituted thiocarbohydrazone derivatives was predicted and evaluated through comprehensive in silico analysis. All investigated compounds complied with Lipinski’s Rule of 5, with most also satisfying the Rule of 3 while simultaneously exhibiting favorable pharmacokinetic properties and low predicted ecotoxicity. To substantiate these findings and elucidate the influence of para-substituents, chromatographic behavior of the studied derivatives was evaluated using reversed-phase thin-layer chromatography (RP-TLC). Initial linear regression analysis revealed statistically significant correlations between chromatographic parameters and in silico-derived descriptors of lipophilicity, pharmacokinetics, and ecotoxicity. Furthermore, cluster analysis and principal component analysis provided a robust and unambiguous interpretation of the structure–property relationships, highlighting substituent polarity as the leading factor controlling the bioactivity of bis-substituted thiocarbohydrazones, although the contribution of electronic effects cannot be neglected. Moreover, R_M⁰ correlates with lipophilicity and pharmacokinetics, whereas m reflects ecotoxicity. Collectively, these findings emphasize the critical role of subtle structural variations in shaping the overall properties of these novel derivatives. Full article

Anhua dark tea, a protected geographical indication product in Hunan, China, derives its value from specific mountain micro-terroirs, including Furongshan, Gaoma Erxi, and Yuntaishan. Across these areas, micro-terroir and cultivar variations impart distinctive chemical components to the decisive raw material (locally known as Hei Mao Cha). For the authentication of these specific origins, we developed a rapid HPLC-DAD method coupled with the ATLD algorithm, enabling the quantification of caffeine and seven major flavan-3-ols within five minutes. Our method achieved satisfactory accuracy, with average recoveries of 84.73–119.88% and RMSEP values ranging from 0.28 to 4.39 μg/mL. We subsequently applied PCA and FCA, which revealed distinct clustering patterns of the tea samples by their mountain origin. Notably, Furongshan and Gaoma Erxi exhibited markedly distinct chemical profiles, while Yuntaishan showed intermediate characteristics. This integrated HPLC-DAD/ATLD protocol, coupled with non-linear t-distributed stochastic neighbor embedding (t-SNE) followed by random forest (RF) classification (validation accuracy: 85.7%), offers a practical solution for the fine-scale geographical traceability of raw dark tea, supporting quality control and providing insights into how micro-terroir shapes tea chemistry. This approach can be readily adapted for the authentication of other geographical indication products. Full article

Polluted mangroves are ecologically sensitive habitats that provide ecosystem services. In a selected polluted forest of Simaisma, viable aerobic, halophilic, and heterotrophic hydrocarbon-degrading bacterial strains were isolated from both rhizosphere and non-rhizosphere regions. The chemical composition of sediment showed a clear distinction between the rhizosphere and non-rhizosphere sites, as well as coastal and non-coastal sediments, as per Principal Component Analysis (PCA) clustering. Anthracene, an indicator of oil pollution, was present along with vanadium, another marker of oil pollution. Through selective enrichment cultures, a total of 25 hydrocarbon-degrading bacterial strains were isolated, including Lysinibacillus xylanilyticus, Bacillus cereus, Lysinibacillus sphaericus, Pseudomonas stutzeri, Acinetobacter calcoaceticus, and Staphylococcus warneri. To link the adaptation of bacteria to sediment chemistry, nine B. cereus strains were investigated using their MALDI-TOF MS protein profiles combined with their dendrogram. The relationship between protein profiles of B. cereus strains with their biosurfactant production capabilities was explained by a tanglogram. The tanglegram suggests that biosurfactant production is an important functional trait in B. cereus, but it is not consistently reflected in the overall protein profile. This suggests that bacterial adaptation in the polluted mangrove sediments may involve changes at multiple cellular levels, including metabolic activity and variation in protein expression profiles. These findings confirm the involvement of mangrove-associated bacteria in the sustainability of mangrove forests by promoting bioremediation of oil pollution, thereby protecting coastal ecosystems and their environmental and socio-economic aspects. Full article

Groundwater–surface water interactions in volcanic hydrogeological systems represent a key process in river dynamics and were preliminarily investigated along a river draining the southern sector of the Sabatini Mountains (central Italy) using an integrated hydrogeological and geochemical approach. Serial discharge measurements, combined with physico-chemical parameters, major ions, stable oxygen isotopes, and radon analyses, reveal marked spatial variability in river–aquifer exchanges along distinct river reaches. The Arrone River exhibits clear differences between upstream, intermediate, and downstream sections, reflecting the relative influence of localized anthropogenic inputs, diffuse groundwater discharge from the volcanic aquifer, and subsurface flow contributions. Upstream reaches are characterized by pronounced modifications in discharge and chemistry, whereas intermediate and downstream reaches show progressive groundwater influence, resulting in distinct geochemical signatures and changes in water quality. Correlation and cluster analyses identify reach-specific processes controlling water composition and support the recognition of gaining and mixed river conditions under varying hydrological regimes. These results constrain a conceptual model in which river behavior is governed by spatially heterogeneous groundwater inflows, modulated by seasonal discharge dynamics and local human pressures. This study highlights the importance of reach-scale investigations for understanding SW–GW interactions in volcanic settings and provides transferable insights relevant to groundwater-dependent river systems. Full article