Search Results (758)

The growing need for rapid screening of adsorption energies in organic materials has driven substantial progress in developing various architectures of equivariant graph neural networks (eGNNs). This advancement has largely been enabled by the availability of extensive Density Functional Theory (DFT)-generated datasets, sufficiently large to train complex eGNN models effectively. However, certain material groups with significant industrial relevance, such as aromatic compounds, remain underrepresented in these large datasets. In this work, we aim to bridge the gap between limited, domain-specific DFT datasets and large-scale pretrained eGNNs. Our methodology involves creating a specialized dataset by segregating aromatic compounds after a targeted ensemble extraction process, then fine-tuning a pretrained model via approaches that include full retraining and systematically freezing specific network sections. We demonstrate that these approaches can yield accurate energy and force predictions with minimal domain-specific training data and computation. Additionally, we investigate the effects of augmenting training datasets with chemically related but out-of-domain groups. Our findings indicate that incorporating supplementary data that closely resembles the target domain, even if approximate, would enhance model performance on domain-specific tasks. Furthermore, we systematically freeze different sections of the pretrained models to elucidate the role each component plays during adaptation to new domains, revealing that relearning low-level representations is critical for effective domain transfer. Overall, this study contributes valuable insights and practical guidelines for efficiently adapting deep learning models for accurate adsorption energy predictions, significantly reducing reliance on extensive training datasets. Full article

Ternary choline chloride-based deep eutectic solvents (TDESs) exhibit unique physicochemical properties, including lower viscosities, lower melting points, higher thermal stabilities, and enhanced solvations compared to binary deep eutectic solvents (BDESs). Although BDESs have been widely studied, the addition of a third component in TDESs offers opportunities to further optimize their performance. This review aims to evaluate the physicochemical properties of TDESs and highlight their potential applications in sustainable industrial processes compared to BDESs. A comprehensive analysis of the existing literature was conducted, focusing on TDES properties, such as phase behavior, density, viscosity, pH, conductivity, and the effect of water, along with their applications in various fields. TDESs demonstrated superior physicochemical characteristics compared to BDESs, including improved solvation and thermal stability. Their applications in biomass conversion, CO₂ capture, heavy oil upgrading, refrigeration gases, and as solvents/catalysts in organic reactions show significant promise for enhancing process efficiency and sustainability. Despite their advantages, TDESs face challenges including limited predictive models, potential instability under certain conditions, and scalability hurdles. Overall, TDESs offer significant potential for advancing sustainable and efficient chemical processes for industrial applications. Full article

The contamination of drinking water by per- and polyfluoroalkyl substances (PFASs) presents a global concern due to their extreme persistence, driven by strong C–F bonds. This study investigated the potential of graphene as a filtration material for PFAS removal, focusing on six key compounds regulated by the U.S. EPA: PFOA, PFNA, GenX, PFBS, PFOS, and PFHxS. Using molecular simulations, adsorption energy, diffusion coefficients, and PFAS-to-graphene distances were analyzed. The results showed that adsorption strength increased with molecular weight; PFOS (500 g/mol) exhibited the strongest adsorption (−171 kcal/mol). Compounds with sulfonic acid head groups (e.g., PFOS) had stronger interactions than those with carboxylate groups (e.g., PFNA), highlighting the importance of head group chemistry. Shorter graphene-to-PFAS distances also aligned with higher adsorption energies. PFOS, for example, had the shortest distance at 8.23 Å (head) and 6.15 Å (tail) from graphene. Diffusion coefficients decreased with increasing molecular weight and carbon chain length, with lower molecules like PFBS (four carbon atoms) diffusing more rapidly than heavier ones like PFOS and PFNA. Interestingly, graphene enhanced PFAS mobility in water, likely by disrupting the water structure and lowering intermolecular resistance. These results highlight graphene’s promise as a high-performance material for PFAS removal and future water purification technologies. Full article

Current work investigates the fabrication and performance of nanocomposite membranes, modified with varying concentrations of hybrid nanostructures comprising titanium nanowires coated with iron nanoparticles (TiO₂ NWs-Fe₂O₃), for the removal of Naphthol Blue Black (NBB) dye from industrial wastewater. A series of analytical tools were employed to confirm the successful modification including scanning electron microscopy and EDX analysis, porosity and hydrophilicity measurements, Fourier-transform infrared spectroscopy, and X-Ray Diffraction. The incorporation of TiO₂ NWs-Fe₂O₃ has enhanced membrane performance significantly by increasing the PWF and improving dye retention rates of nanocomposite membranes. At 0.7 g of nanostructure content, the modified membrane (M8) achieved a PWF of 93 L/m²·h and NBB dye rejection of over 98%. The flux recovery ratio (FRR) analysis disclosed improved antifouling properties, with the M8 membrane demonstrating a 73.4% FRR. This study confirms the potential of TiO₂ NWs-Fe₂O₃-modified membranes in enhancing water treatment processes, offering a promising solution for industrial wastewater treatment. These outstanding results highlight the potential of the novel PES-TiO₂ NWs-Fe₂O₃ membranes for dye removal and present adequate guidance for the modification of membrane physical properties in the field of wastewater treatment. Full article

Platinum has emerged as an optimal catalyst for the selective hydrogenation of nitroarenes owing to its high hydrogenation activity, selectivity, and stability. In this study, we report the fabrication of platinum nanoparticles stabilized on a composite support consisting of gum acacia polymer (GAP) and TiO₂. It was engineered for the targeted reduction of nitroarenes to arylamines via selective hydrogenation in methanol at ambient temperature. The non-toxic and biocompatible properties of GAP enable it to act as a reducing and stabilizing agent during synthesis. The synthesized nanocatalyst was characterized using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Morphological and structural analyses revealed that the fabricated catalyst consisted of minuscule Pt nanoparticles integrated within the GAP framework, accompanied by the corresponding TiO₂ nanoparticles. Inductively coupled plasma optical emission spectrometry (ICP-OES) was employed to ascertain the Pt content. The mild reaction conditions, decent yields, trouble-free workup, and facile separation of the catalyst make this method a clean and practical alternative to nitroreduction. Selective hydrogenation yielded an average arylamine production of 97.6% over five consecutive cycles, demonstrating the stability of the nanocatalyst without detectable leaching. Full article

The global atmospheric concentration of carbon dioxide (CO₂) has exceeded 420 ppm. Adsorption-based carbon capture technologies, offer energy-efficient, sustainable solutions. Relying on classical adsorption models like Langmuir to predict CO₂ uptake presents limitations due to the need for case-specific parameter fitting. To address this, the present study introduces a universal machine learning (ML) framework using multiple algorithms—Generalized Linear Model (GLM), Feed-forward Multilayer Perceptron (DL), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosted Trees (GBT)—to reliably predict CO₂ adsorption capacities across diverse zeolite structures and conditions. By compiling over 5700 experimentally measured adsorption data points from 71 independent studies, this approach systematically incorporates critical factors including pore size, Si/Al ratio, cation type, temperature, and pressure. Rigorous Cross-Validation confirmed superior performance of the GBT model (R² = 0.936, RMSE = 0.806 mmol/g), outperforming other ML models and providing comparable performance with classical Langmuir model predictions without separate parameter calibration. Feature importance analysis identified pressure, Si/Al ratio, and cation type as dominant influences on adsorption performance. Overall, this ML-driven methodology demonstrates substantial promise for accelerating material discovery, optimization, and practical deployment of zeolite-based CO₂ capture technologies. Full article

Metakaolin-based geopolymers with different molar ratios of Si/Al were synthesized and utilized as an efficient adsorbent for the removal of methylene blue (MB) as a model cationic dye from aqueous solution. Various analytical techniques were employed to characterize the synthesized geopolymers. The influence of the main operation conditions on the adsorption kinetics of MB onto the geopolymer was examined under various operating conditions. Results showed a significant maximum MB adsorption capacity at the temperature of 30 °C for all four types of geopolymers studied (designated as A, B, C, and D) up to 35.3, 23.6, 25.5, and 19.0 mg g⁻¹, respectively. The corresponding order of Si/Al ratio was A < C < B < D. Adsorption kinetics was so fast and reached equilibrium in 10 min, and the experimental results were described using the adsorption dynamic intraparticle model (ADIM). The equilibrium data for MB removal was in agreement with the Langmuir isotherm. Full article

In the context of industrial digitalization, the Industry 5.0 paradigm introduces digital twins as a cutting-edge solution. This study explores the concept of digital twins and their integration with the Industrial Internet of Things (IIoT), offering insights into how these technologies bring intelligence to industrial settings to drive both process optimization and sustainability. Industrial digitalization connects products and processes, boosting the productivity and efficiency of people, facilities, and equipment. These advancements are expected to yield broad economic and environmental benefits. As connected systems continuously generate data, this information becomes a vital asset, but also introduces new challenges for industrial operations. The work presented in this article aims to demonstrate the possibility of generating advanced tools for process optimization. This, which ultimately impacts the environment and empowers people in the processes, is achieved through data integration and the development of a digital twin using open tools such as NodeRed v4.0.9 and Python 3.13.5 frameworks, among others. The article begins with a conceptual analysis of IIoT and digital twin integration and then presents a case study to demonstrate how these technologies support the principles of the Industry 5.0 framework. Specifically, it examines the requirements for applying the golden batch concept within a biological production environment. The goal is to illustrate how digital twins can facilitate the achievement of quality standards while fostering a more sustainable production process. The results from the case study show that biomaterial concentration was optimized by approximately 10%, reducing excess in an initially overdesigned process. In doing so, this paper highlights the potential of digital twins as key enablers of Industry 5.0—enhancing sustainability, empowering operators, and building resilience throughout the value chain. Full article

Among the various available synthesis approaches, hydrolytic precipitation offers a simple, cost-effective, and scalable route for producing phase-pure NiO with a controlled morphology and crystallite size. However, the influence of calcination temperature on its crystalline phase, particle size, and antimicrobial activity remains an active field of research. This study aims to investigate the structural, morphological, and antibacterial properties of NiO nanoparticles synthesized via hydrolytic methods and thermally treated at different temperatures. XRD data indicate the presence of the hexagonal crystallographic phase of NiO (space group 166: R-3m), a structural variant less commonly reported in the literature, stabilized under mild hydrolytic synthesis conditions. The average crystallite size increases significantly from 4.97 nm at 300 °C to values of ~17.8 nm at 500–700 °C, confirming the development of the crystal lattice. The ATR-FTIR analysis confirms the presence of the characteristic Ni–O band for all samples, positioned between 367 and 383 cm⁻¹, with a reference value of 355 cm⁻¹ for commercial NiO. The displacements and variations in intensity reflect a thermal evolution of the crystalline structure, but also an important influence of the size of the crystallites and the agglomeration state. The results reveal a systematic evolution in particle morphology from porous, flake-like nanostructures at 300 °C to dense, well-faceted polyhedral crystals at 900 °C. With an increasing temperature, particle size increases. EDS spectra confirm the high purity of the NiO phase across all samples. Additionally, the NiO nanoparticles exhibit calcination-temperature-dependent antibacterial activity, with the complete inhibition of Escherichia coli and Enterococcus faecalis observed after 24 h for the sample calcined at 300 °C and over 90% CFU reduction within 4 h. A significant reduction in E. faecalis viability across all samples indicates time- and strain-specific bactericidal effects. Due to its remarkable multifunctionality, NiO has emerged as a strategic nanomaterial in fields ranging from energy storage and catalysis to antimicrobial technologies, where precise control over its structural phase and particle size is essential for optimizing performance. Full article

Vanillin photoinduced deprotonation was evaluated and analyzed. Vibronic states and transitions were computationally investigated. Optimizations and vertical electron transitions in the gas phase and with the continuum solvation model were computed using the time-dependent density functional theory. Static absorption and emission (photostatic optical) spectra were statistically averaged over the excited instantaneous molecular conformers fluctuating on quantum–classical molecular dynamic trajectories. Photostatic optical spectra were generated using the hybrid quantum–classical molecular dynamics for explicit solvent models. Conical intersection searching and nonadiabatic molecular dynamics simulations defined potential energy surface propagations, intersections, dissipations, and dissociations. The procedure included mixed-reference spin–flip excitations for both procedures and trajectory surface hopping for photodynamics. Insignificant structural deformations vs. hydroxyl bond cleavage followed by deprotonation were demonstrated starting from different initial structural conditions, which included optimized, transition state, and several other important fluctuating configurations in various environments. Vanillin electronic structure changes were illustrated and analyzed at the key points on conical intersection and nonadiabatic molecular dynamics trajectories by investigating molecular orbital symmetry and electron density difference. The hydroxyl group decomposed on transition to a σ-molecular orbital localized on the elongated O–H bond. Full article

Risk management is crucial in engineering projects, especially in highly complex environments like petrochemical plants producing polyvinyl chloride (PVC). This study proposes a tailored risk management model, using analytic hierarchy process (AHP) and linear regression analysis, alongside MS Excel and IBM SPSS^® version 23, to identify, assess, and prioritize key risks. Surveys and interviews revealed seven management factors (budget, schedule, safety, productivity, contracting, quality, and environment) and 18 critical risks, including design errors and procurement delays. The model quantifies risk impacts, provides a regression equation for risk classification, and supports effective mitigation strategies. Based on this model, decision-making can be facilitated for the implementation of effective mitigation strategies. It also promotes continuous improvement, optimizing economic resources and minimizing environmental impacts, addressing a research gap in Colombia’s petrochemical sector and paving the way for broader industrial applications. Full article

Furfural is a fascinating bio-based platform molecule that can be converted into useful cyclic compounds, among others. In this work, the hydrogenative rearrangement-dehydration of furfural towards cyclopentanone using a commercially available Pt/C catalyst was investigated in terms of its reaction performance to assess its feasibility as an industrial process. However, acquiring an acceptable cyclopentanone yield proved very difficult, and the reaction was constrained by unforeseen parameters, such as the relative liquid volume in the reactor and the substrate concentration. Most strikingly, the sacrificial formation of furanoic oligomers that precipitated onto the catalyst’s surface was a troublesome key factor that mediated the product’s selectivity versus the carbon mass balance. By applying a biphasic water–toluene solvent system, the yield of cyclopentanone was somewhat improved to a middling 59%, while tentatively positive distributions of reaction components over these solvent phases were observed, which could be advantageous for anticipated down-stream processing. Overall, the sheer difficulty of controlling this one-pot cascade transformation towards a satisfactory product output under rather unfavorable reaction parameters renders it unsuitable for industrial process development, and a multi-step procedure for this chemical transformation might be considered instead. Full article

White-light-emitting diodes (wLEDs) are central to next-generation lighting technologies, yet their reliance on critical raw materials (CRMs), such as rare-earth elements, raises concerns regarding sustainability and supply security. In this work, we present a simple, low-cost method to produce photoluminescent carbon-based nanostructures—known as oxidative debris (OD)—via alkaline treatment of graphene oxide (GO) using KOH solutions ranging from 0.04 M to 1.78 M. The resulting OD, isolated from the supernatant after acid precipitation, exhibits strong and tunable photoluminescence (PL) across the visible spectrum. Emission peaks shift from blue (~440 nm) to green (~500 nm) and yellow (~565 nm) as a function of treatment conditions, with excitation wavelengths between 300 and 390 nm. Optical, morphological. and compositional analyses were performed using UV-Vis, AFM, FTIR, and Raman spectroscopy, confirming the presence of highly oxidized aromatic domains. The blue-emitting (S2) and green/yellow-emitting (R2) fractions were successfully separated and characterized, demonstrating potential color tuning by adjusting KOH concentration and treatment time. This study highlights the feasibility of reusing GO-derived byproducts as sustainable phosphor alternatives in wLEDs, reducing reliance on CRMs and aligning with green chemistry principles. Full article

In the present study, a process for the production of 2,2,3,3,4,4,4-heptafluorobutyl acetate (HFBAc) is proposed for the first time. The production process of HFBAc from acetic acid (AAc) and 2,2,3,3,4,4,4-heptafluorobutan-1-ol (HFBol) was carried out at laboratory scale using batch reactive distillation (BRD). The process was conducted at atmospheric pressure in the presence of an acid catalyst, with an excess of AAc relative to HFBol (initial molar ratio of reagents HFBol/AAc is 45/55). During the BRD, the aqueous phase of the distillate was withdrawn from the system, while the organic phase of the distillate was returned as reflux. Since part of AAc is lost along with the aqueous phase of the distillate, a minor excess of AAc is reasonable for maximizing the conversion of the most expensive reagent—HFBol. The losses of AAc and HFBol with the aqueous phase of the distillate were less than 2 mole % and less than 0.5 mole % of the feed, respectively. The purity of HFBAc after BRD was 97.9 wt. %, and the conversion of HFBol exceeded 99 mole % of the feed. The purity of certain product fractions of HFBAc was greater than 99.6 wt. %. The obtained data can be used for industrial technology development to obtain HFBAc. Full article

The aim of this study was to improve the water–gas shift efficiency of Co/CeO₂ catalyst by incorporating praseodymium and rhenium. The catalysts were synthesized via combustion method and characterized using X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) surface area analysis, Scanning Electron Microscope (SEM), H₂-temperature programmed reduction (H₂-TPR), NH₃-temperature programmed desorption (NH₃-TPD), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS). These characterization techniques evaluate the increase of the surface acidity and oxygen vacancies in Co-based catalysts, which leads to an increase in water–gas shift performance because CO molecules prefer to react with surface oxygen, then followed by the production of CO₂ and oxygen vacancies which act as active sites for H₂O dissociation. The 1%Re4%Co/Ce-5%Pr-O catalyst exhibited a maximum CO conversion of 86% at 450 °C, substantially outperforming the 5%Co/Ce-5%Pr-O catalyst, which showed only 62% CO conversion at 600 °C. In addition, 1%Re4%Co/Ce-5%Pr-O catalyst is more resistant towards deactivation than 5%Co/Ce-5%Pr-O. The result presented that the catalytic activity of 1%Re4%Co/Ce-5%Pr-O catalyst was kept constant for the whole period of 50 h, while a 6% decrease in water–gas shift activity was found for the 5%Co/Ce-5%Pr-O catalyst. Moreover, the addition of rhenium into the Co/Ce-Pr-O catalyst reveals that the enhancement of oxygen vacancy concentration, oxygen mobility, and surface acidity, thereby enhances CO conversion efficiency. Full article