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Aerogels (AGs) are highly porous, low-density, disordered, ultralight macroscopic materials with immense surface areas. Traditionally synthesized using aqueous sol–gel chemistry, starting by molecular precursors, the nanoparticles (NPs) dispersions gelation method is nowadays the most used procedure to obtain AGs with improved crystallinity and broader structural, morphological and compositional complexity. The Sol–gel process consists of preparing a solution by hydrolysis of different precursors, followed by gelation, ageing and a drying phase, via supercritical, freeze-drying or ambient evaporation. AGs can be classified based on various factors, such as appearance, synthetic methods, chemical origin, drying methods, microstructure, etc. Due to their nonpareil characteristics, AGs are completely different from common NPs, thus covering different and more extensive applications. AGs can be applied in supercapacitors, acoustic devices, drug delivery, thermal insulation, catalysis, electrocatalysis, gas absorption, gas separation, organic and inorganic xenobiotics removal from water and air and radionucleotides management. This review provides first an analysis on AGs according to data found in CAS Content Collection. Then, an AGs’ classification based on the chemical origin of their precursors, as well as the different methods existing to prepare AGs and the current optimization strategies are discussed. Following, focusing on AGs of inorganic origin, silica and metal oxide-based AGs are reviewed, deeply discussing their properties, specific synthesis and possible uses. These classes were chosen based on the evidence that they are the most experimented, patented and marketed AGs. Several related case studies are reported, some of which have been presented in reader-friendly tables and discussed. Full article

The transport sector significantly contributes to global greenhouse gas emissions, making electromobility crucial in the race toward the United Nations Sustainable Development Goals. In recent years, the increasing competition among manufacturers, the development of cheaper batteries, the ongoing policy support, and people’s greater environmental awareness have consistently increased electric vehicles (EVs) adoption. Nevertheless, EVs charging needs—highly influenced by EV drivers’ behavior uncertainty—challenge their integration into the power grid on a massive scale, leading to potential issues, such as overloading and grid instability. Smart charging strategies can mitigate these adverse effects by using information and communication technologies to optimize EV charging schedules in terms of power systems’ constraints, electricity prices, and users’ preferences, benefiting stakeholders by minimizing network losses, maximizing aggregators’ profit, and reducing users’ driving range anxiety. To this end, accurately forecasting EV charging demand is paramount. Traditionally used forecasting methods, such as model-driven and statistical ones, often rely on complex mathematical models, simulated data, or simplifying assumptions, failing to accurately represent current real-world EV charging profiles. Machine learning (ML) methods, which leverage real-life historical data to model complex, nonlinear, high-dimensional problems, have demonstrated superiority in this domain, becoming a hot research topic. In a scenario where EV technologies, charging infrastructure, data acquisition, and ML techniques constantly evolve, this paper conducts a systematized literature review (SLR) to understand the current landscape of ML-based EV charging demand forecasting, its emerging trends, and its future perspectives. The proposed SLR provides a well-structured synthesis of a large body of literature, categorizing approaches not only based on their ML-based approach, but also on the EV charging application. In addition, we focus on the most recent technological advances, exploring deep-learning architectures, spatial-temporal challenges, and cross-domain learning strategies. This offers an integrative perspective. On the one hand, it maps the state of the art, identifying a notable shift toward deep-learning approaches and an increasing interest in public EV charging stations. On the other hand, it uncovers underexplored methodological intersections that can be further exploited and research gaps that remain underaddressed, such as real-time data integration, long-term forecasting, and the development of adaptable models to different charging behaviors and locations. In this line, emerging trends combining recurrent and convolutional neural networks, and using relatively new ML techniques, especially transformers, and ML paradigms, such as transfer-, federated-, and meta-learning, have shown promising results for addressing spatial-temporality, time-scalability, and geographical-generalizability issues, paving the path for future research directions. Full article

Well integrity is paramount for the safe, environmentally responsible, and economically viable operation of wells throughout their lifecycle, encompassing conventional oil and gas production, unconventional resource extraction (e.g., shale gas and tight oil), and geological storage applications (CO₂, H₂, and natural gas). This review presents a comprehensive synthesis of well integrity challenges, failure mechanisms, monitoring technologies, and management strategies across these operational domains. Key integrity threats—including cement sheath degradation (chemical attack, debonding, cracking, microannuli), casing failures (corrosion, collapse, burst, buckling, fatigue, wear, and connection damage), sustained casing pressure (SCP), and wellhead leaks—are examined in detail. Unique challenges posed by hydraulic fracturing in unconventional wells and emerging risks in CO₂ and hydrogen storage, such as corrosion, carbonation, embrittlement, hydrogen-induced cracking (HIC), and microbial degradation, are also highlighted. The review further explores the evolution of integrity standards (NORSOK, API, ISO), the implementation of Well Integrity Management Systems (WIMS), and the integration of advanced monitoring technologies such as fiber optics, logging tools, and real-time pressure sensing. Particular emphasis is placed on the role of digital technologies—including artificial intelligence, machine learning, and digital twin systems—in enabling predictive maintenance, early failure detection, and lifecycle risk management. The novelty of this review lies in its integrated, cross-domain perspective and its emphasis on digital twin applications for continuous, adaptive well integrity surveillance. It identifies critical knowledge gaps in modeling, materials qualification, and data integration—especially in the context of long-term CO₂ and H₂ storage—and advocates for a proactive, digitally enabled approach to lifecycle well integrity. Full article

Molecular diversity is a key component of overall biodiversity, playing a vital role in evolution. It results from the adaptation of organisms to various habitats, which impacts their survival. The Amaryllidoideae subfamily is a significant group of monocotyledonous plants known for producing an exclusive and still-expanding group of molecules with diverse biological activities. Galanthamine (Gal), the most renowned metabolite from Amaryllidoideae subfamily, has been marketed for the palliative treatment of Alzheimer’s disease since 2001 due to its ability to inhibit the acetylcholinesterase enzyme. Due to the high cost and low yield of its synthesis, pharmaceutical companies extract this drug from Amaryllidoideae plants, such as Narcissus pseudonarcissus cv. Carlton in Europe and Lycoris radiata in China. The aim of this study was to describe the alkaloid profile of fifteen different species of Narcissus L. (commonly known as daffodils) collected in Spain using gas chromatography coupled with mass spectrometry. Fifty-one alkaloids were identified and quantified within these species through our private library of Amaryllidaceae alkaloids (AA) built over the last four decades, while thirty structures remained not identified in thirteen of these species. The highest concentration of these nitrogenate metabolites was quantified in N. confusus, 541 μg Gal·100 mg⁻¹ DW, which also exhibited a notably high concentration of Gal, 301 μg Gal·100 mg⁻¹ DW, which represents about 55% of the alkaloids identified in this species. The species N. bujei was also found to contain a significant quantity of this compound, amounting to 103.2 μg Gal·100 mg⁻¹ DW. The plant N. assoanus harbored a total of seven unidentified compounds, indicating that this species could be a potentially important source of novel alkaloids. In conclusion, this study facilitates a direct comparison of alkaloid profiles for fifteen Narcissus plant species. This serves as a valuable tool for identifying possible new sources of galanthamine, as well as other novel medicinal alkaloids. Finally, this work presents the first alkaloid profile of the species N. minor and N. nevadensis. Full article

(Y_xGd_3−x)(Al_yGa_5−y)O₁₂:Ce and (Lu_xGd_3−x)(Al_yGa_5−y)O₁₂:Ce ceramics were synthesized for the first time by direct exposure of a powerful electron flux to a mixture of the corresponding oxide components. Five-component ceramics were obtained from oxide powders of Y₂O₃, Lu₂O₃, Gd₂O₃, Al₂O₃, Ga₂O₃, and Ce₂O₃ in less than 1 s, without the use of any additional reagents or process stimulants. The average productivity of the synthesis process was approximately 5 g/s. The reaction yield, defined as the mass ratio of the synthesized ceramic to the initial mixture, ranged from 94% to 99%. The synthesized ceramics exhibit photoluminescence when excited by radiation in the 340–450 nm spectral range. The position of the luminescence bands depends on the specific composition, with the emission maxima located within the 525–560 nm range. It is suggested that under high radiation power density, the element exchange rate between the particles of the initial materials is governed by the formation of an ion–electron plasma. Full article

Salt stress severely constrains agricultural productivity in arid and semi-arid regions. This study evaluated exogenous proline as an osmoprotector in Physalis ixocarpa Brot. (Mexican husk tomato) under salinity. Germination screening identified 75 mM NaCl as a threshold stress level, reducing germination by 38.9% while maintaining seedling viability. Proline pretreatment (30-min imbibition) at 8 mM restored germination to 78% and fresh weight to control levels under salt stress. In vitro experiments revealed that 8 mM proline enhanced chlorophyll content above salt-stressed controls while reducing root length from 9.72 to 5.08 cm, indicating resource reallocation toward photosynthetic protection. Infrared spectroscopy showed characteristic polysaccharide shifts and bands potentially associated with proline incorporation. Gas chromatography–mass spectrometry metabolomics of stem–leaf extracts revealed salt-induced synthesis of nitrogenous osmolytes (such as long-chain amines) and carbohydrate reorganization from α-D-glucopyranoside to β-D-riboside. Proline treatment restored the original carbohydrate profile while generating pyrrolidine derivatives (2.83%), evidence of active proline metabolism. Phenolic antioxidants (e.g., catechol) present in controls were absent under both salt stress and proline treatment, suggesting that proline’s protective mechanism may operate through metabolic regulation of osmolyte pathways and membrane stabilization rather than inducing phenolic antioxidant synthesis. These findings demonstrate proline’s multifaceted protective mechanisms and support its potential application for enhancing salt tolerance in this crop. Full article

The integrated process of CO₂ hydrogenation and catalytic methanol synthesis under plasma conditions holds great potential for CO₂ conversion from waste gases. This process connects a dielectric barrier discharge (DBD) plasma reactor and a methanol synthesis fixed-bed reactor through a pressurization device, achieving the stepwise conversion of CO₂ to CO and then to methanol, thereby establishing a low-carbon and high-efficiency energy conversion system. This study experimentally investigated the key parameters influencing the CO₂ hydrogenation process in the DBD plasma reactor and the methanol synthesis process in the fixed-bed reactor. The results show that in the plasma reaction, discharge power, discharge gap, gas flow rate, and gas composition significantly affect CO₂ conversion efficiency. In the methanol synthesis process, the CO/CO₂ mixed feed exhibits superior catalytic performance compared to pure CO₂. The optimal operating conditions for the integrated process are a plasma voltage of 40 V and a downstream reaction temperature of 240 °C, under which the system achieves the best overall performance. Full article

Data-driven forecasting is becoming increasingly central to modern energy management, yet nonspecialists without a background in artificial intelligence (AI) face significant barriers to entry. While Python is the dominant machine learning language, R remains a practical and accessible tool for users with expertise in statistics, engineering, or domain-specific analysis. To inform tool selection, we first provide an evidence-based comparison of R with major alternatives before reviewing 49 peer-reviewed articles published between 2020 and 2025 in Science Citation Index Expanded (SCIE)-level journals that utilized R for energy forecasting tasks, including electricity (regional and site-level), solar, wind, thermal energy, and natural gas. Despite such growth, the field still lacks a systematic, cross-domain synthesis that clarifies which R-based methods prevail, how accessible workflows are implemented, and where methodological gaps remain; this motivated our use of text mining. Text mining techniques were employed to categorize the literature according to forecasting objectives, modeling methods, application domains, and tool usage patterns. The results indicate that tree-based ensemble learning models—e.g., random forests, gradient boosting, and hybrid variants—are employed most frequently, particularly for solar and short-term load forecasting. Notably, few studies incorporated automated model selection or explainable AI; however, there is a growing shift toward interpretable and beginner-friendly workflows. This review offers a practical reference for nonexperts seeking to apply R in energy forecasting contexts, emphasizing accessible modeling strategies and reproducible practices. We also curate example R scripts, workflow templates, and a study-level link catalog to support replication. The findings of this review support the broader democratization of energy analytics by identifying trends and methodologies suitable for users without advanced AI training. Finally, we synthesize domain-specific evidence and outline the text-mining pipeline, present visual keyword profiles and comparative performance tables that surface prevailing strategies and unmet needs, and conclude with practical guidance and targeted directions for future research. Full article

This work reports a low-cost, microwave-assisted wet chemistry synthesis of zinc antimonate (ZnSb₂O₆) powders with a trirutile structure, yielding highly homogeneous, nanometric particles. X-ray diffraction (XRD) confirmed the formation of the trirutile phase with lattice parameters of a = 4.664 Å and c = 9.263 Å, and an estimated crystallite size of 42 nm. UV–vis spectroscopy revealed a bandgap of 3.35 eV. Scanning electron microscopy (SEM) showed that ethylenediamine, as a chelating agent, formed porous microstructures of microrods and cuboids, ideal for enhanced gas adsorption. Brunauer–Emmett–Teller (BET) analysis revealed a specific surface area of 6 m²/g and a total pore volume of 0.0831 cm³/g, indicating a predominantly mesoporous structure. The gas sensing properties of ZnSb₂O₆ pellets were evaluated in CO and C₃H₈ atmospheres at 100, 200, and 300 °C. The material exhibited high sensitivity at 300 °C, where the maximum responses were 5.86 for CO at 300 ppm and 1.04 for C₃H₈ at 500 ppm. The enhanced sensitivity at elevated temperatures was corroborated by a corresponding decrease in electrical resistivity. Furthermore, the material demonstrated effective photocatalytic activity, achieving up to 60% degradation of methylene blue and 50% of malachite green after 300 min of UV irradiation, with the process following first-order reaction kinetics. These results highlight that ZnSb₂O₆ synthesized by this method is a promising bifunctional material for gas sensing and photocatalytic applications. Full article

Organophosphates, especially their ester, are not only toxic to humans but equally toxic to aquatic and other animal life on Earth when exposed to them. Here, we designed an efficient and easy way to degrade hexamethyl phosphoramide and omethoate organophosphate catalytically in a natural way into non-toxic products. Both hexamethyl phosphoramide and omethoate are possible carcinogens and cause serious health issues in humans and other animals when exposed to them. In this work, a modified sonochemical method was used for the synthesis of ZnO nanoparticles using zinc acetate dihydrate, ethylenediamine dihydrochloride, and polyvinylpyrrolidone. Sodium hydroxide was used as the precipitating agent, and distilled water was used as the solvent. An Elmasonic ultra-sonicator with 240-watt power was used for the preparation of ZnO nanoparticles. The synthesized ZnO nanoparticles with a high surface area (250 m²/g), average particle size of 23 ± 1 nm, and a mesoporous structure with 1.858 nm average pore size were then used for the degradation of organophosphate, i.e., hexamethyl phosphoramide and omethoate pesticide, using 10 µL of concentration to check their catalytic efficiency for the first time. The degradation products were identified using gas chromatography–electron impact mass spectrometry (GC/EIMS). The results showed that omethoate was completely degraded, while hexamethyl phosphoramide showed partial degradation, both producing fewer toxic intermediates. Full article

Graphene was directly grown on SiO₂/Si substrates using microwave plasma-enhanced chemical vapor deposition (PECVD) to investigate how synthesis-driven variations in structure and doping influence carrier transport. The effects of synthesis temperature, plasma power, deposition time, gas flow, and pressure on graphene’s structure and electronic properties were systematically studied. Raman spectroscopy revealed non-monotonic changes in layer number, defect density, and doping levels, reflecting the complex interplay between growth, etching, and self-doping mechanisms. The surface morphology and conductivity were assessed by atomic force microscopy (AFM). Charge carrier mobility, extracted from graphene-based field-effect transistors, showed strong correlations with Raman features, including the intensity ratios and positions of the Two-dimension (2D) and G peaks. Importantly, mobility did not correlate with defect density but was linked to reduced self-doping and a weaker graphene–substrate interaction rather than intrinsic structural disorder. These findings suggest that charge transport in PECVD-grown graphene is predominantly limited by interfacial and doping effects. This study offers valuable insights into the synthesis–structure–property relationship, which is crucial for optimizing graphene for electronic and sensing applications. Full article

Membrane technology is widely used in gas separation processes due to its small footprint, high energy efficiency, and favorable economic viability. The current membrane market predominantly relies on polymer membranes, among which polyimide (PI) membranes stand out as highly promising materials due to their superior gas separation performance coupled with exceptional thermal and chemical stability. However, traditional polyimide membranes suffer from low gas permeability and insufficient plasticization resistance, hindering their broader industrial application. In order to meet the demands of more stringent application fields, it is crucial to further improve their gas performance and anti-plasticization to enhance their cost-effectiveness. Consequently, it is essential to modify traditional polyimides and formulate membrane fabrication strategies to solve these problems. This review introduces the monomer structures and synthesis approaches of polyimides, including solution-based and solid-state thermal condensation. Then, we propose representative preparation methods of polyimide-based membranes. Additionally, modification strategies, including thermal rearrangement, cross-linking, and physical blending, are summarized, which address the critical issues in contemporary polyimide-based gas separation membranes. Finally, this review critically discusses the current challenges and prospects for developing polyimide membranes for gas separation. Full article

H₂S detection is critical for personal and industrial safety. Generally, metal oxide-based H₂S sensors exhibit no response at room temperature (RT). In this study, CuO/ZnWO₄ (C-ZWO) nanocomposites were prepared via a two-step hydrothermal process and applied to RT H₂S sensing. The results show that the C-ZWO sensors exhibit an elevated response value at RT and balanced gas-sensing properties at 100 °C. Significantly, the response value of a 10% C-ZWO sensor to 25 ppm of H₂S at RT is 651.6 with a response time of 78 s, which is 310.3 times that of the ZnWO₄ sensor (2.1). The systemic characterization results suggest that the enhanced RT H₂S-sensing properties are ascribed to the synergistic effects of the growth-specific surface area and oxygen vacancy occupancy, the enhanced oxygen reduction ability, and the formation of the p–n heterojunction structure between CuO and ZnWO₄. The C-ZWO nanocomposites possess added active sites for H₂S adsorption and dissociation, with the p–n heterojunction giving rise to higher electrical resistance, and thus, the follow-up produces a high response value even at RT. Full article

Background: [⁶⁸Ga]Ga-DOTATOC is widely used in PET imaging of neuroendocrine tumors (NETs) due to its high affinity for somatostatin receptors. Given the short physical half-life of gallium-68 (~68 min), rapid, reproducible, and GMP-compliant synthesis is essential for clinical application. Methods: An optimized cassette-based automated synthesis protocol was developed using a commercial cassette. Improvements included direct generator elution into the reactor without pre-purification, use of a SepPak^® C18 Plus Light cartridge for purification, replacement of HEPES with 0.3 M sodium acetate buffer (final pH ~3.8), and implementation of a non-vented sterile filter enabling automated pressure-hold integrity testing. Results: Across all batches, the synthesis yielded [⁶⁸Ga]Ga-DOTATOC with high radiochemical purity (> 97%) and reproducible decay-corrected radiochemical yields up to 88.3 ± 0.6%. Total synthesis time was approximately 13 min. The final product remained stable for at least 3 h post-synthesis. The use of acetate buffer eliminated the need for HEPES-specific testing, streamlining the workflow. Automated filter testing improved GMP-compliant documentation and reduced radiation exposure for personnel. Conclusions: This optimized, cassette-based synthesis protocol enables fast, high-yield, and GMP-compliant production of [⁶⁸Ga]Ga-DOTATOC. It supports clinical theranostic workflows by ensuring product quality, process standardization, and regulatory compliance. Full article

This study aimed to identify and evaluate yeasts and lactic acid bacteria (LAB) isolated from Mexican cocoa mucilage (Theobroma cacao) and coffee pulp (Coffea arabica) for their potential use as sourdough starter co-cultures to improve bread quality. Functional screens included assessments of amylolytic, proteolytic, and phytase activities, CO₂ production, acidification capacity, and exopolysaccharide (EPS) synthesis. Saccharomyces cerevisiae YCTA13 exhibited the highest fermentative performance, surpassing commercial baker’s yeast by 52.24%. Leuconostoc mesenteroides LABCTA3 showed a high acidification capacity and EPS production, while Lactiplantibacillus plantarum 20B3HB had the highest phytase activity. Six yeast–LAB combinations were formulated as mixed starter co-cultures and evaluated in sourdough breadmaking. The B3Y14 co-culture (LABCTA3 + YCTA14) significantly improved the bread volume and height by 35.61% and 17.18%, respectively, compared to the commercial sourdough starter, and reduced crumb firmness by 59.66%. Image analysis of the bread crumb revealed that B3Y14 enhanced the crumb structure, resulting in greater alveolar uniformity and a balanced gas cell geometry. Specifically, B3Y14 showed low alveolar regularity (1.16 ± 0.03) and circularity (0.40 ± 0.01), indicating a fine and homogeneous crumb structure. These findings highlight the synergistic potential of selected allochthonous yeast and LAB strains in optimizing sourdough performance, positively impacting bread texture, structure, and quality. Full article