Abstract An environmentally friendly magnetic metal–organic framework (MMOF) based on MIL-53(Al) was synthesized using terephthalic acid (TPA) recovered from polyethylene terephthalate (PET) waste as the organic linker. The Fe2O3@MIL-53(Al) adsorbent was fabricated through a layer-by-layer assembly, with polyethylenimine (PEI) bridging the Fe2O3 core (∼200 nm) and MIL-53(Al) shell (∼25 nm). A uniform MOF layer was achieved in a single cycle under green synthesis conditions with PEI concentrations ≥ 10 g/L. The MMOF efficiently removed organophosphate pollutants, particularly 2-phosphonobutane-1,2,4-tricarboxylic acid (PBTC), a common scale and corrosion inhibitor. Adsorption kinetics across a pH range of 3–10.5 closely match the pseudo-second-order kinetic model, indicating chemisorption. The adsorption capacity ranged from 250 to 460 mg/g, with a maximum surface-normalized capacity of 0.44 mg/m2 at pH 5.5, attributed to favorable electrostatic interactions. Adsorption performance was strongly influenced by both the MMOF zeta potential and the ionization state of PBTC. The well-fitted adsorption isotherms (R2 > 0.9) to the Freundlich, Temkin, and Henry models indicate heterogeneous surface adsorption mechanisms. Electrostatic attraction, hydrogen bonding, and surface complexation are proposed as the dominant mechanisms. Overall, the Fe2O3/PEI/MIL-53(Al) composite exhibits high adsorption capacity, broad pH stability, and low toxicity, highlighting its promise for efficient PBTC antiscalant removal applications.
Abstract A pilot-scale pyrolysis process was designed to produce gasoline and diesel-range fuels from waste plastic bags through pyrolysis reaction, ex-situ catalytic cracking, and staged condensation. The catalytic upgrading unit was operated at 400–440 °C and consisted of different catalysts such as ZSM-5, dolomite, or kaolin catalysts. The surface properties of the catalysts were analyzed using Brunauer–Emmett–Teller (BET) and scanning electron microscopy (SEM) techniques. The products were highly temperature-dependent, with a maximum liquid yield at 420 °C, above which secondary cracking led to increased gas yield. ZSM-5 catalysts facilitated extensive aromatization, yielding gasoline-range plastic pyrolysis oil (G-PPO) with a high heating value of 44.5 MJ kg–1 and high benzene and toluene content, while dolomite catalysts facilitated the production of stable diesel-range hydrocarbons with high cycloalkane content, whereas kaolin catalysts inhibited heavy wax production with high liquid yield. Gas chromatography mass spectrometer (GC–MS) analysis validated that radical cracking, β-scission, cyclization, and dehydrogenation were the dominant product evolution pathways, which were highly dependent on catalyst acidity and structure. Engine testing with a 20% G-PPO blend showed a predictable rise in the brake thermal efficiency with increasing load, with exhaust gas temperature and emissions correlating with the aromatic composition of the fuel. Exergy analysis showed that ZSM-5-derived G-PPO had the lowest exhaust exergy and irreversibility losses, suggesting a more efficient conversion of chemical energy to useful work. Although pyrolysis can help with waste disposal of plastic bag, but emission data from blend fuel (20% G-PPO) revealed higher NOx and particulate emissions compared to commercial gasoline.
Nanostructured NiO electrodes fabricated using glancing angle deposition (GLAD) are promising platforms for nonenzymatic electrochemical glucose sensing, owing to their high surface area and tunable morphology. With GLAD, adjusting the deposition angle and substrate rotation rate can significantly affect electrochemical performance; however, identifying optimal GLAD structures for specific use cases is still largely experimental, relying on trial and error. Here, we develop a multiscale modeling framework that links the GLAD film growth process to glucose electro-oxidation performance by combining on-lattice kinetic Monte Carlo (kMC) simulations with morphology characterization and a coupled reaction-diffusion electrochemical model. Key morphological features, including surface area, porosity, and directional tortuosities, are quantified from kMC-generated structures across a wide range of GLAD geometries, such as slanted posts, helical shapes, and vertical nanocolumns. These features are then incorporated into a homogenized porous electrode model for glucose electro-oxidation on Ni-based catalysts. The model clarifies how the trade-off between surface area and mass transport governs electrode sensitivity. We identify a slanted-post morphology deposited at 72.5° as the optimal design for maximizing glucose electro-oxidation, and we find that physical electrodes formed with similar geometries were approximately 25% more sensitive to glucose than those formed with the vertical post morphology previously considered to be the optimum (i.e., 1.38 mA/mM·cm2 vs 1.12 mA/mM·cm2). Beyond glucose, this modeling workflow provides general guidelines for designing GLAD-fabricated electrodes for other biosensing targets and for electrochemical applications, including energy conversion and hydrogen generation.
Abstract This study aimed to evaluate pressurized hydrothermal extraction as a sustainable strategy for recovering bioactive compounds from coconut residues. The methodology involved a continuous-flow system at varying temperatures (60, 90, and 120 °C) to optimize phytochemical recovery, followed by stabilization via chitosan–alginate encapsulation, and a comprehensive sustainability assessment using EcoScale and Path2Green metrics. The study demonstrated that pressurized hydrothermal extraction at 120 °C is the most effective condition for recovering bioactive compounds from coconut residues, yielding the highest levels of total phenolic content (41.57 mg GAE g–1), total flavonoid content (57.52 mg CE g–1), and total tannin content (15.37 mg TAE g–1), along with the maximum antioxidant activity (69.74 μmol TEAC g–1). The encapsulation process using chitosan–alginate matrices achieved encapsulation efficiencies of up to 65.47%, with dry microcapsules showing enhanced phenolic retention (26.73 mg GAE g–1 capsules) compared to wet capsules (24.41 mg GAE g–1 capsules). The release kinetics of phenolic compounds followed a diffusion-controlled mechanism, with lyophilized microcapsules exhibiting a higher initial release (45% within 15 min) and greater long-term stability, retaining 86.79% of phenolic content after 30 days of refrigerated storage. The environmental assessment revealed favorable sustainability metrics, with an EcoScale score of 88.10 and a Path2Green global score of 0.320, highlighting the process’s alignment with green chemistry principles and its potential for industrial applications in food, cosmetics, and pharmaceuticals.
Abstract Efficient fractionation of lignocellulosic biomass remains a major challenge due to the complex and recalcitrant architecture of plant cell walls, requiring process intensification strategies capable of selectively promoting delignification while preserving the cellulosic fraction. This study investigated the combined influence of chemical (NaOH concentration), thermochemical (temperature), mechanical (high-intensity ultrasound), electrical (pulsed electric field, PEF), and physical (particle size) process intensification strategies on soybean hull fractionation using a Plackett–Burman screening design. Cellulose yield, water retention value (WRV), and fibrillation yield were adopted as complementary indicators of delignification efficiency, fiber accessibility, and structural disintegration. Temperature emerged as the dominant factor governing cellulose recovery and fibrillation, whereas particle size and NaOH concentration primarily influenced fiber swelling and structural accessibility, as reflected by WRV. In contrast, PEF showed no measurable contribution to cellulose recovery, WRV, or fibrillation yield within the investigated experimental domain, demonstrating that its influence was negligible compared to the thermochemical variables. The integration of statistical screening with scanning electron microscopy, Fourier transform infrared spectroscopy, and sustainability assessment enabled the organization of representative processing conditions into four empirical structural transformation regimes, describing the progressive evolution of the lignocellulosic matrix from mild delignification to extensive fibrillation. Rather than representing mechanistic transitions, these regimes constitute an empirical process-structure–property framework linking processing conditions, structural responses, and fractionation performance. The findings demonstrate that thermochemical variables should be prioritized for soybean hull fractionation while establishing the operational domain in which PEF is unlikely to provide meaningful gains. This integrated framework contributes to the rational design of intensified lignocellulosic fractionation processes and supports more efficient and sustainable biomass valorization strategies.
Abstract As a cornerstone of global energy consumption, coal is extensively utilized in power generation, iron and steel, building materials, chemical industries, and heating. It has made significant contributions to the global economy and will remain indispensable in the energy sector for the foreseeable future. However, coal utilization is accompanied by emissions of high ash, sulfur, heavy metals, and other harmful elements. Thus, implementing effective and viable pretreatment prior to its use is crucial. This review summarizes the current research progress, classification, underlying principles, and challenges of coal pretreatment technologies. It examines the influence of the physicochemical properties of coal on pretreatment processes and subsequent utilization, with particular emphasis on a comparative analysis of relevant processes oriented toward three primary objectives: deashing, desulfurization, and graded efficiency utilization. Physical pretreatment technologies feature mature processes, large treatment capacities, and low cost, but they struggle to remove intrinsic impurities that are chemically bonded or finely dispersed. Chemical pretreatment technologies can achieve deep desulfurization, deashing, and structural modification, yet they generally face severe challenges such as high reagent consumption, high energy intensity, equipment corrosion, and secondary pollution. Biological pretreatment technologies offer mild operating conditions and environmental friendliness, but they suffer from slow reaction rates, difficulties in process scale-up, and narrow adaptability to different coal types. Future advances in coal pretreatment technologies are expected to focus on intelligent process control, multitechnology integration, recovery of value-added components, and application-oriented pretreatment strategies, aiming to enable the clean and efficient utilization of coal and support the sustainable development of the energy sector.
Abstract The urgent requirement for battery materials to satisfy their rising demand due to electrification is straining limited mineral resources. Thus, recycling is becoming increasingly more important to reduce reliance on mining and delocalize the supply chain of critical battery metals. In this framework, the use of process-intensification techniques to improve recycling yields is fundamental to ensuring scalability and economic viability. The extraction (leaching) of metals from spent batteries is the core step of hydrometallurgical recycling, and its intensification using acoustic cavitation has shown impressive results, enabling the use of milder organic acids. This work aims at investigating the quantitative effects of the addition of hydrogen peroxide as a reducing agent during cavitation-assisted leaching, to pinpoint its effect on the extraction. This results in a deeper understanding of the action of both reducing agents during the cavitation-intensified process.
The structural stability of metal oxide-metal core-shell nanoparticles at the nanoscale is governed by atomistic interface effects that are not captured by conventional continuum approximations. In this work, we introduce an automated "virtual synthesis" framework for the deterministic construction, relaxation, and validation of Fe3O4@Au core-shell nanoparticles using a machine-learning interatomic potential (CHGNet). The pipeline integrates geometric assembly based on crystallographic symmetry and Wulff construction, CHGNet-assisted energetic relaxation, and postrelaxation quality control to ensure chemically and structurally consistent interfaces. Systematic parametric analysis reveals strict structural viability thresholds inherent to nanoscale heterostructures. Specifically, core diameters below D < 7.0 & Aring; fail to preserve spinel coordination, while Au shell thicknesses below T < 0.8 & Aring; do not yield a percolated metallic network for a 20 & Aring; particle, to generate a continuous shell. Radial distribution analysis confirms the formation of polycrystalline FCC Au shells anchored by strong Au-O interactions (approximate to 2.20 & Aring;), alongside a measurable lattice compression of similar to 4%, indicative of curvature-induced surface stress. Together, these results establish quantitative lower bounds for structural feasibility in magneto-plasmonic core-shell systems and demonstrate the capability of graph neural network-based interatomic potentials to bridge the gap between continuum design and atomistic realism.
Atmospheric pollution caused by volatile organic compounds (VOCs), such as acetone, poses a significant environmental challenge because of their toxicity and persistence. This study evaluated a hybrid photocatalytic/biological system for the treatment of acetone-contaminated gas streams, combining a UV-activated graphene oxide photocatalytic reactor with a biofilter packed with plant aerenchyma inoculated with activated sludge. The system operated for 15 weeks under controlled conditions, achieving a maximum removal efficiency of 80% and an overall average efficiency of 60.98%. In silico studies indicated viable microbial metabolic pathways for acetone, and the low binding energy of the fungus Penicillium chrysogenum suggests a complementary role in the degradation of byproducts generated in the photocatalytic stage. The synergistic combination of advanced oxidation and microbial biodegradation enabled an effective pollutant removal. Microbial succession was observed during operation with an increase in fungus tolerance to adverse conditions. These results demonstrate the technical feasibility of the hybrid system for the sustainable treatment of VOC-laden gas emissions and suggest its potential scalability for industrial applications.
Flow modeling, reactor engineering, and process intensification (PI) have played a major role in shaping modern chemical engineering practice. Early work in flow modeling focused on the use of computational models to visualize flow fields and improve the design of process equipment. During this period, reactor engineering increasingly relied on flow modeling to optimize reactor geometry and internals across scales. The growing emphasis on PI, through strategies such as transforming batch operations into continuous ones, employing structured reactors or alternative energy sources, and enhancing driving forces, further accelerated the development and integration of these approaches. In this perspective, critical process metrics (CPMs) are presented as a useful framework for connecting flow modeling, reactor engineering, and PI and translating mechanistic insight into performance. Recent advances in machine learning (ML) and hybrid physics-ML models provide tools to address distributions, variability, and scale dependence of CPMs, and to relate them to critical quality and performance attributes (CQAs and CPAs). Anchoring models to measurable fingerprints and embedding them within decision frameworks (that account for uncertainty) can enable the convergence of flow modeling, reactor engineering, and PI toward robust and scalable product and process excellence.
Hydrothermal liquefaction (HTL) of lignocellulosic biomass is plagued with low biocrude yields owing to the tendency of highly reactive oxygenated intermediates to condense to form biochars. By contrast, the high protein content in food waste is comprised of substantial nitrogen species, which are known to interact strongly with oxygenates through Maillard, amide, and peptide bond formation reactions. Co-feeding food waste and lignocellulose opens new reaction pathways for biocrude formation but is currently poorly understood. This work evaluated the molecular level interactions between food waste and lignocellulose model compounds and the corresponding effect on product yields and quality. Food waste-cellulose and food waste-xylan feedstock blends achieved maximum biocrude carbon yield improvements of 12.2% and 10.1%, respectively, relative to a simple linear model that interpolates between the yields of the pure feedstocks. Increases in biocrude yield were balanced by corresponding decreases in char yield, indicating synergistic interactions between the feeds during HTL. Biocrude volatility analysis revealed that increased biocrude yield preferentially benefitted the jet fuel fraction, which comprised up to 22.6% of the total carbon yield for food waste-cellulose blends. Biocrude and char were analyzed using GC-MS and FT-IR spectroscopy to investigate the source of synergistic trends and provide greater mechanistic understanding. Key cofeeding effects included the promotion of retro-aldol condensation reactions and trans-esterification of fatty acids, sequestering carbon in the biocrude phase via the inhibition of char formation while increasing biocrude volatility toward jet fuel-range compounds. These results indicate the potential for judicious selection of HTL cofeeds to increase both biocrude yield and selectivity to desired fuel precursors, including sustainable aviation fuel.
Here we present a solvent decision-making pathway for less hazardous and scalable synthesis of TpPa-1 covalent organic framework (COF) underpinned by environmental compatibility (CHEM21 framework), processability (Hansen solubility parameters), and thermodynamic suitability (free energy of solvation). Our analyses suggest that solvents with moderate solvation, such as 1,4-dioxane, propylene carbonate, and diacetin, hinder excessive reaction reversibility and promote crystallite growth. Balancing solvent greenness, compatibility, and thermodynamics, we find that diacetin is optimal for a continuous-flow synthesis. Compared with TpPa-1 COF synthesized in diacetin under batch conditions, the Brunauer-Emmett-Teller surface area of flow-derived TpPa-1 reached 418 m2 g-1, increasing CO2 uptake by 50% at 298 K. The flow process achieved a 30-fold increase in space-time yield (STY), with excellent reproducibility and an 89% reduction in specific energy consumption. This solvent-thermodynamics-guided approach establishes a physically transparent and sustainable framework for optimizing COF synthesis under continuous-flow conditions.
Hydrodynamic cavitation (HC) is being increasingly used for a variety of industrially relevant processes such as biomass pretreatment, emulsions, and crystallization. Many of these processes involve non-Newtonian fluids. However, most of the previous studies on characterizing flow in cavitating devices focus on low-viscosity, Newtonian fluids. In this study, we investigate the influence of non-Newtonian, particularly shear-thinning behavior of fluids on the flow characteristics and cavitation inception in a vortex-based hydrodynamic cavitation device (VD) using both experiments and computational fluid dynamics (CFD) simulations. Primary digestate from an anaerobic digester was considered as a model non-Newtonian fluid, considering increasing applications of HC devices for biomass pretreatment. The non-Newtonian behavior was represented using the Carreau rheological model. The parameters of the model were obtained via rheological measurements over a shear rate range of 10 to 1300 s-1 of two digestate slurries containing 50% (v/v) and 77% (v/v) with water. The case of water was used as the reference case. A three-dimensional (3D) CFD simulation of flow in VD was performed by using the Eulerian mixture model. The SST k-ω turbulence model and the Singhal cavitation model were used. Simulations were validated by comparing the simulated pressure drop across VD with the experimental measurements. Simulation results show that the 50% (v/v) and 77% (v/v) digestate slurries exhibit a reduction in swirl intensity in the vortex chamber and a delayed cavitation inception compared with the reference case of water. The results were analyzed to bring out the influence of higher effective viscosity and non-Newtonian behavior on the flow characteristics of VD. The presented results will be useful for designing VD for high-viscosity applications in waste valorization.
High-consistency (HC) suspensions containing several weight% of fibers have been studied in the past but mainly in relation to water-laid web-forming technology. The severe flocking problems deteriorating web quality can be partly solved by using aqueous foam as a transfer medium instead of water. The processability of highly viscous HC foam and its potential to produce low-density fiber products are revealed only in large-scale dynamic conditions with the proper flow properties of the foam. For this reason, a pilot-scale study was conducted at 10-14% fiber consistency and a 3 m/min web speed using softwood, hardwood, and a 50/50 fiber mixture as raw materials. To better understand the effects of additives on foam processability and the strength of the final product, the following additives were selected: guar gum (GG), carboxymethyl cellulose (CMC), citric acid, and a combination of the latter two. These studies were supported by rheological measurements and X-ray microtomography analysis of the product structures. In rheological measurements, the HC fiber foams turned out to have significant elasticity with a high storage modulus. Interestingly, the highest consistency in the pilot studies led to the smallest mean floc size and total floc volume in the corresponding samples. The best strength properties were gained with citric acid and CMC. Overall, the findings indicate that HC foam demonstrates significant technological potential for producing sustainable fiber products with efficient water usage.
As global energy systems face mounting pressure to enhance oil recovery while minimizing energy input and carbon intensity, optimizing extraction from mature oilfields has become a strategic imperative for both energy security and emissions reduction. In ultrahigh water cut reservoirs (water cut >98%), conventional water injection becomes increasingly inefficient, yet current evaluation methods-focused solely on oil output or water cut changes-fail to reflect the energy performance of recovery strategies. Here, we propose an energy-efficiency-based evaluation framework centered on the oil-power ratio (eta), a novel index grounded in mass conservation and energy conversion principles. This metric quantifies the incremental oil output per unit of energy input, offering a physics-informed approach to evaluating six enhancement strategies: water flooding, increased extraction rate, flow line transformation, interfacial tension reduction, displacement fluid viscosity enhancement, and water plugging. Using micro-CT imaging and pore-scale numerical simulations, we reconstruct core-scale pore networks and classify remaining oil into five morphological types-clustered, porous, columnar, film-like, and droplet-for targeted analysis of recovery mechanisms. Our findings show that eta not only captures the energy efficiency of different measures but also identifies the optimal timing for their application. Among all tested strategies, water plugging achieves the highest oil-power performance, followed by flow line transformation and tension reduction. Furthermore, strategy-specific mobilization pathways for different residual oil types are revealed, providing mechanistic insights into microscale sweep efficiency. By combining the energy efficiency index with the pore-scale crude oil classification, the oil-power ratio is coupled with the microscopic remaining oil morphology for the first time, and the quantitative evaluation from' only production' to' increasing oil per unit energy consumption' is realized, which provides a basis for the evaluation of low-carbon oil production in ultrahigh water cut oilfields.
Selecting aqueous extraction technologies for biomass valorization requires balancing process selectivity and energy demand rather than maximizing extraction yield alone. In this study, ultrasound-assisted extraction (UAE) and high-shear mechanical extraction (HSME) were systematically compared for the aqueous recovery of phenolic compounds from mangosteen peel byproduct. Both processes were evaluated in terms of extraction performance, phenolic selectivity, energy demand, temperature rise, and process scalability. While HSME promoted a broader and less selective release of phenolic compounds, reaching total phenolic concentrations of up to 1161 +/- 8 mu g GAE/mL, UAE favored the enrichment of specific phenolic subclasses, particularly anthocyanin-rich fractions, achieving 97 +/- 3 mu g C3OG/mL of total anthocyanins and a higher proportion of red pigments (44 +/- 1% vs. 38 +/- 1% for HSME), and resulting in higher antioxidant capacity despite comparable total phenolic contents. Energy demand was assessed by distinguishing between energy effectively transferred to the medium, determined through a calorimetric approach, and electrical energy required at the system level, revealing contrasting energy delivery modes between shear- and cavitation-driven processes. Although UAE delivered higher specific energy to the extraction medium (up to 130.4 kJ/kg vs. 56.2 kJ/kg for HSME), its batch operation and process complexity impose scalability constraints, whereas HSME exhibited lower energy demand per unit mass and greater operational robustness. A process-level sustainability assessment highlighted clear trade-offs between selectivity, energy demand, and scalability. Overall, the results demonstrate that energy delivery mode is a key design variable in aqueous phenolic extraction, providing a rational basis for selecting extraction strategies according to targeted phenolic profiles and process constraints.
Microbial electrosynthesis (MES) is a promising bioelectrochemical technology for converting CO2 into value-added products. However, downstream processing remains a major constraint for its application at larger scales. There is limited understanding of which separation technologies can be effectively integrated with MES effluents, and how key operating conditions impact on the product recovery and energy efficiency. This study assesses the use of electrodialysis (ED) as a solvent-free downstream separation process for acetate recovery from a multicomponent simulated MES effluent. The effects of operating pH (3, 5.5, and 7.5), applied current density (1.9, 3.8, 9.5, and 15.0 mA cm-2), and operation mode (continuous or fed-batch) were investigated. The highest acetate titer (13.73 +/- 1.65) in the concentrate stream was reached at pH 5.5, a current density of 1.9 mA cm-2, and under fed-batch operation. Ion competition was identified as the primary limiting factor, with preferential Cl- transport, which reduced the Coulombic efficiency of acetate transport to 34.97 +/- 0.63%. Under these conditions, the ED system recovered 1.12 +/- 0.03 kg of acetate per kWh, corresponding to approximately 5% of the total energy demand associated with MES production. Overall, this study provides quantitative guidance on the optimization of electrodialysis as a recovery technology from a simulated MES effluent as a low-energy downstream solution for MES systems, helping to address key bottlenecks faced by this technology.
Efficient data selection is critical in domains where data acquisition is expensive and time-consuming, such as material science. In this work, we introduce a novel active learning framework that integrates proximal policy optimization (PPO) with Gaussian process regression (GPR) to strategically select informative data points and thereby enhance predictive modeling. Leveraging the inherent stability and sample efficiency of PPO, achieved through a clipped surrogate objective, the framework guides data acquisition via a custom-designed Gymnasium environment tailored for GPR. In this environment, the PPO agent dynamically chooses data points based on their potential to improve the GPR's performance, as measured by the R 2 score, while preventing redundancy through an action masking mechanism. We apply the proposed methodology to predict the selectivity of methane (CH4) over higher alkanes in metal-organic frameworks (MOFs), focusing on CuBTC and IRMOF-1. The framework is evaluated using both ternary and quaternary gas mixtures, where the performance of the GPR is assessed through metrics such as R 2, mean absolute error (MAE), and root mean squared error (RMSE). Across CuBTC and IRMOF-1 in ternary and quaternary hydrocarbon mixtures, PPO-guided acquisition achieves 77-86% data savings relative to full GCMC grids, typically querying only ∼14-23% of the candidate pool while the clipped-update PPO policy converges stably by focusing selections in the pressure-temperature-composition regions where selectivity changes most rapidly. This work shows the potential of combining advanced reinforcement learning techniques with regression models to accelerate material discovery and optimize gas separation processes.
