Syngas is a feedstock for platform chemicals and fuels like methanol, ammonia and hydrocarbon liquids via Fischer-Tropsch synthesis. Chemical looping reforming has been under development for decades to produce syngas and comprises two interconnected vessels that cycle between a net reduction atmosphere and a net oxidation atmosphere. Rather than cycling solids, switching the gas feeds is an alternative approach to forced concentration cycling. Here, we tested a fixed bed reactor containing 5 g of Ni/NiAl2O4 as the catalyst, which alternated between NiC (reduction step) and Ni (oxidation step). A 4-way valve fed methane at 0.15 L/min during the reduction cycle and 21% O2/Ar mixture during the oxidation cycle, each cycle lasting 2 min. Methane conversion reached 98% above 800 degrees C and 0.1 MPa but to 75% at 2 MPa. Concurrently, the H2 and CO selectivity also decreased. At all of the operating conditions, H2 was the dominant product during the reduction cycle and CO predominated during the oxidation cycle. Over the course of a 6 h experiment, the H2 and CO yield remained constant. Additionally, the residence time distribution analysis suggests the reactor operated in plug flow with Peclet numbers exceeding 70.

The demand for polymeric nanoparticles is increasing as more and more studies showcase the efficacy of nano-sized drug delivery systems. However, current technologies struggle to produce these nanoparticles on a scale sufficiently large for clinical trials or industry. We have developed a continuous nanoprecipitation process for poly(D,L-lactide) in a rotor-stator spinning disk reactor that produces uniform (dispersity < 0.2) nanoparticles (56 nm to 132 nm) at higher rates (up to 864 g d(-1)) than many other available technologies. The particle size can be predicted according to the diffusion limited coalescence model (R-2 = 0.89) in the slow mixing regime. Molecular weight (between 1 kDa to 54 kDa) and total flowrate (between 48 mL min(-1 )to 120 mL min(-1)) had no effect on particle size. We also fit a statistical power-law model (R-2 = 0.88) that predicts particle size in relation to the experimental parameters (concentration of polymer in the aqueous phase, rotational speed, flowrate ratio between the organic and aqueous phase). Polymer concentration in the organic phase and disk rotation are the dominant factors influencing size while the flowrate ratio has one-third their impact.

Carbon capture, utilization and storage (CCUS), along with lignocellulosic biomass valorization (e.g., lignin, cellulose), are promising decarbonization strategies for hard-to-abate industries. Green solvents, such as deep eutectic solvents and ionic liquids, enable efficient CO₂ capture and selective lignin extraction, enhancing lignin depolymerization into high-value products. However, current molecular design tools are slow and computationally expensive, limiting green material innovation. This study introduces a novel data-driven framework for green material discovery using generative AI, including transformers, generative adversarial networks, and variational autoencoders. The generation process was guided by rule-based and physics- and chemistry-informed models for automatic labeling, with feedback loops to reduce invalid SMILES strings. The approach achieved 70% molecular validity and 94% novelty in generating new solvents for CO₂ capture and lignin applications. Model training averaged under one hour, and molecule generation took only seconds, significantly faster than traditional methods. Ensemble machine learning models assessed the environmental sustainability of candidates, and retrosynthesis analysis identified feasible, green synthesis pathways. This flexible, scalable methodology extends beyond solvent discovery to broader applications in process design and optimization, enabling the rapid generation of novel and cost-effective process configurations.

The recovery of rare earth elements (REEs) from monazite ore was studied using ultrasound-assisted leaching (UL) and compared with silent leaching (SL). The UL experiments were conducted using a 40-mm diameter Monel alloy horn at 20 kHz, with power densities of 142 - 232 W/L. SL with H2SO4 yielded <3% recovery for all REEs, whereas UL enhanced dissolution within 10 min, (8.5% Ce, 7.0% Nd, 8.8% La) via cavitation-induced particle breakdown, confirmed by Scanning Electron Microscopy (SEM). Using 62% H2SO4, optimization achieved maximum recoveries: 20.5% Ce, 17.2% Nd, 22.0% La, 16.3% Pr, and 17.9% Sm. Recovery increased with ultrasound power density up to 206 W/L (26.2% La) and decreased at higher values due to cavitation shielding and nuclei removal. NaOH pretreatment via dry ball milling removed similar to 95% of phosphate with minimal REE loss. Among acids tested under SL (6 mol L-1, 60 degrees C, 30 min), HNO3 achieved > 95% recovery from phosphate-free monazite, compared to <10% for crude ore. Applying ultrasound to phosphate-free monazite accelerated leaching, yielding 55-92% recovery of Ce-Sm in 10 min and 72-94% after 30 min. Enhanced kinetics arising from improved ore disintegration, phosphate removal, and optimized acid conditions provide a rapid, scalable REEs recovery strategy.

This work covers the production of cellulose esters with varying degrees of substitution (DS) using ultrasound (US) power input, leveraging free fatty acids as esterification agent (EA) as a bio-based alternative to traditional chlorides, anhydrides and vinyl esters. The best conditions without US were achieved with oleic acid, with an EA/cellulose molar ratio of 6 and a temperature of 80 °C for 24 h, producing esters with a DS of 1.44. Applying US at 20 kHz and 4.39 W at room temperature, required <30 min to produce cellulose esters with a DS of 0.38. Then, the effects of the US input power, reaction volume and properties of cellulose solutions on the cavitation activity were investigated by simulations in COMSOL. The density, viscosity and speed of sound in the cellulose esters solutions were measured and defined in the simulations as 936.2 kg m-3, 23.3·10–3 Pa.s, and 1495.8 m s-1 for 25 g L-1. Simulations with conditions resulting in the highest DS with US were characterized by the smallest acoustic cavitation volume and the lowest u: 9.60·10–8 m3 and 40.06 m s-1. US-assisted esterification produced thermoplastic esters with an energy input of 18 W g-1 of cellulose against 93 W g-1 required by conventional esterification.

Numerical simulations are a tool for sonoreactors design and reaction parameters choice. We modeled a sonoreactor with six lateral flat transducers along the walls and a concentric high intensity focused ultrasound (HIFU) transducer at the bottom. We examined the effects of the gap between the reflector and transducers (h), cone radii in the lower part and ultrasound frequency (f) on the cavitation activity of cellulose esters solutions. Then, we investigated the effects of the properties of cellulose solutions on the cavitation activity. The simulation accounts for the attenuation due to the cavitation bubbles and considers the propagation of sound waves from the HIFU as linear. We measured the speed of sound in a cellulose esters solutions and included it in our simulations: 1545 m s-1 for 6.25 g L-1. h, f, the density (ρ) and the viscosity (μ) of the cellulose solutions have the most significant effects - accounting for 34 % to 61 % of the variance - on the total acoustic pressure (pT) and active cavitation surface area (V). pT and V increase as f and ρ increase, and as h and μ decrease. At 78 kHz and h = 0.075 m, with μ = 5.3 × 10-3 Pa.s and ρ = 941.8 kg m-3, the simulation resulted in the highest pT and largest V: 1.96 × 106 Pa and 3.99 × 10-2 m2. These data provide a basis to optimize sonoreactor design and operating conditions for enhanced cavitation performance in cellulose processing.

Piezo-photocatalysis has emerged as a promising hybrid advanced oxidation process to eliminate resistant organic pollutants in wastewater via harnessing light irradiation and mechanical vibration. This process offers a synergistic enhancement of photocatalytic efficiency by coupling piezo-electricity with photocatalytic activity, addressing limitations of conventional photocatalysis, including rapid electron-hole pairs recombination and deactivation in the absence of illumination. We concisely review recent advancements in Ti-based piezo-electric semiconductors for wastewater treatment, focusing on research from 2020 to 2025. The studies included in this review are based on materials categorized into two main groups: integrated Ti-based piezo-photocatalysts and hybrid piezo-electric materials-incorporated TiO2 photocatalysts. We critically discuss the impact of piezo-potential and corresponding internal electric fields on photoinduced charge separation and reactive oxygen species generation. Methodologies for assessing the piezo-electric and photocatalytic properties are explored. This review highlights piezo-photocatalysis’s potential applications and challenges, offering insights into future developments in advanced oxidation processes for wastewater treatment.

Messenger RNA (mRNA) therapeutics are promising tools for vaccines, protein replacement, and cancer immunotherapy, but effective delivery systems are crucial to protect mRNA and ensure cellular uptake. Cationic polymers have gained attention for their tunable architectures and ability to form nanoscale complexes with nucleic acids, called polyplexes. This study investigates the use of bottlebrush polymers (BBs), a class of branched polymers, with different structures for mRNA delivery. A library of poly(2-(dimethylaminoethyl)methacrylate) (PDMAEMA) BBs is synthesized with varying backbone lengths and charge density to evaluate their impact on polyplex formation and delivery efficiency. After characterization to assess size distribution and encapsulation efficiency, in vitro assays are performed to examine cytotoxicity, protein expression, and cellular uptake. In vivo administration further investigates their performance. Quaternized PDMAEMA BBs, positively charged at any pH, demonstrate superior encapsulation efficiency at lower charge ratios, achieving optimal particle size and reduced cytotoxicity. Notably, the presence of permanent positive charges significantly diminishes lung accumulation, a challenge in systemically administered cationic formulations. Additionally, longer backbone improves encapsulation efficiency and cellular uptake rate. This comprehensive evaluation underscores the potential of quaternized BBs as a promising platform for mRNA delivery and provides key insights for designing next-generation bottlebrush polymers with improved transfection efficiency and in vivo applicability. STATEMENT OF SIGNIFICANCE: Messenger RNA medicines need carriers that protect the fragile genetic strands and reach the right tissues without harming cells. Lipid nanoparticles do the job today but require high doses of positively charged lipids that can trigger toxicity and tend to accumulate in the lungs. We present the first systematic in-vitro and in-vivo evaluation of "bottlebrush" polymers, which are dense, comb-shaped chains tailored for mRNA delivery. By adjusting the backbone length and introducing permanent, evenly spaced charges, we create nanocomplexes that fully encapsulate mRNA at one-tenth of the charge used previously, remain stable in blood, minimise lung uptake, and sustain protein production in liver cells with low toxicity. These structure-function rules open a versatile, scalable platform for next-generation vaccines and gene replacement therapies.

Zeolites are versatile crystalline materials for catalysis, water purification, membrane-based separation, and dehydration. However, traditional zeolite synthesis is time consuming (up to several days) and requires toxic templates to dictate crystallographic structures. Herein, we compare ultrasonic (US)-assisted hydrothermal synthesis, direct US synthesis (i.e. without hydrothermal treatment) and mechanical stirring-assisted hydrothermal treatment to synthesize Linde Type-A (LTA) zeolite (Zeolite A) in a template-free medium. The direct US process reduces the total synthesis time from 11 h to 6 h and increases product yield by 60 %, eliminating the need for a further hydrothermal step. The direct US synthesis produces pure LTA crystals with a cubic morphology ranging from 1 mu m to 14 mu m, whereas mechanical stirring-assisted synthesis produces amorphous-crystalline agglomerates with average particle size of similar to 25 mu m. Product yield and particle size gains reached a plateau beyond US amplitudes of 35 % (61 W/L). To correlate the synthesis parameters to the LTA parameters (crystallinity, yield, and particle size) we develop and report a machine learning (ML) prediction model. Catboost, a tree-based ML model trained on 36 experimental data points achieved the highest accuracy among 5 models evaluated in predicting yield (R-2 = 0.93), particle size (R-2 = 0.91), and crystallinity (R-2 = 0.88). The energy delivered to the system (US amplitude) was determined by the model to be the most significant parameter for all outputs. An ensemble model improved the yield predictions (R-2 = 0.98).

Several processes and strategies have been developed to promote the utilization of lignin and to facilitate its market adoption across a broad spectrum of applications within the expanding lignin bioeconomy. However, the inherent variability in lignin properties, resulting from diverse feedstock sources and varied recovery and downstream processing methods, remains a significant challenge. This highlights the critical need to investigate lignin's miscibility and reactivity with polymers and solvents, as most lignin valorization pathways involve mixing, blending, or solubilization. Accurate estimation of Hansen solubility parameters (HSP) is crucial for solvent selection in several fields such as polymer science, coatings, adhesives, lignin-based biorefineries and solvent-based carbon capture. Traditional methods for predicting HSP are time-consuming and involve complex experiments, especially in applications dealing with carbon dioxide and lignin solubility. This paper introduces a novel ensemble modeling methodology based on machine learning (ML) techniques for accurate HSP prediction using Simplified Molecular Input Line Entry System (SMILES) codes as entries. The methodology integrates different ML approaches, including deep and shallow learning, to enhance prediction accuracy. Decision fusion of individual ML models is achieved through a hybrid approach combining non-learnable and learnable methods, resulting in reduced errors and enhanced accuracy. The results highlight the effectiveness of the ensemble-based methodology, which achieved 99% accuracy in predicting dispersion solubility parameters, outperforming other individual ML techniques. The proposed generic methodology, from data preprocessing to decision fusion through diverse ML algorithms, can be applied to various chemical analytics beyond HSP prediction.

Achieving efficient industrial decarbonization and net-zero emissions requires the widespread adoption of costeffective and eco-friendly carbon capture technologies. This study presents a hybrid simulation-based optimization methodology integrating physics-based models with data-driven learning, under expert supervision, to optimize carbon capture process design. A causal incremental reinforcement learning (RL) agent is developed to search for optimal design variables within a predefined space. To enhance decision-making, formal concept analysis, Granger causality, and causal Bayesian networks are used for informed causality analysis. The RL agent is guided by this analysis to improve key performance indicators (KPIs), i.e. capture cost and total relative emissions. The proposed method is applied to two case studies: monoethanolamine (MEA)-based and dimethyl ethers of polyethylene glycol (DEPG)-based carbon capture processes. For the MEA process, the methodology achieves a capture cost of 41.3 USD/ton CO2 representing an 8.14% reduction from the 44.96 USD/ton baseline and total relative emissions of 0.28 kg CO2/kg CO2 captured, with optimal design variables including 3 bar absorption pressure, 25.5 degrees C flue gas temperature, 31 degrees C lean MEA temperature, and a boil-up ratio of 0.087. In the DEPG case, optimal values are 7.5 bar pressure, 4.5 solvent-to-feed molar ratio and 9 absorption stages, yielding a capture cost of 44.5 USD/ton CO2 and emissions of 0.00071 kg CO2/kg CO2 captured. All extracted causal graphs, dependencies, and design rules are stored in a unified, updatable knowledgebase to enable transfer learning and generalization to other industrial processes.

Process intensification (PI) has emerged as a transformative approach to enhancing efficiency, sustainability, and economics across chemical and manufacturing industries. However, within its dedicated communities, there is recognition of a persistent gap in transitioning these innovations from laboratory-scale success to widespread industrial adoption. Scaling up PI technologies is far more complex than simply replicating laboratory conditions on a larger scale. Challenges such as the integration with existing units and processes, proving economic viability, and navigating regulatory requirements often impede the practical implementation of PI innovations. This paper aims to identify the key enablers for scaling up PI technologies by presenting a roadmap to bridge the gap between concept and commercialization. While robust engineering design frameworks and advanced modeling tools are crucial, interdisciplinary collaborations and lab-to-market partnerships (or integrated scaling collaborations) are equally critical to drive the successful adoption of PI at the industrial scale.

Floating photocatalysts are an innovative class of immobilized semiconductors that can address the limitations of traditional photocatalysis by directly harvesting light, enhancing oxygen transfer from the air, and enabling easy photocatalyst recovery. In the present study, we developed a novel ultrasound-assisted solvothermal method to synthesize sulfur-doped TiO2 nanotubes (S-TNT). The floating photocatalysts were prepared by immobilizing S-TNT onto buoyant polyurethane foam (PUF) with an open-cell structure using a wet chemical deposition method. The synthesis yielded double-walled TNT doped with 3 nm sulfur nanodots. The ultrasound-assisted solvothermal synthesis method using thiourea as a sulfur precursor resulted in the incorporation of anionic sulfur (S2-) and cationic sulfur (S4+, S6+) into TNT. Among different sulfur dopants (0.5-2 wt%), 1 wt% (S-TNT-1) was optimal to achieve superior optical properties, enhanced photo-induced charge generation, and effective electron-hole pair separation. Compared to bare TNT, the specific surface area of S-TNT-1 increased from 110 to 267 m(2)/g, while the bandgap energy decreased from 3 to 2.48 eV. The photocurrent density of S-TNT-1 (0.6 mA/cm(2)) was threefold higher than that of TNT. Evaluating operating parameters indicated that >96 % of 10 mg/L Bisphenol A (BPA) was degraded with 0.2 g S-TNT-1@PUF at pH = 8 after 75 min of simulated sunlight irradiation. This degradation followed first-order kinetics, with an apparent rate constant of 0.05 min(-1), in which (OH)-O-center dot was the reactive species that contributed most (31 %).

Compared with traditional petroleum-based foam materials, cellulosic foam materials have significant advantages in terms of economy and environmental protection. However, the traditional cellulose-based foams have some problems such as high energy consumption in the preparation process. In this study, the recycled pulp foam (RPF) with ultra-light density (12.69 kg/m(3) similar to 13.83 kg/m(3)) and high porosity (99.07 % similar to 99.15 %) was prepared by mechanical stirring using waste corrugated cardboard as the main raw material and further hydrophobically modified using rosin. The prepared r-RPF foam has excellent hydrophobicity with the water contact angle remaining 132.9 degrees after 20 s. In addition, the rosin coating enhances the mechanical properties of r-RPF, and the stress of r-RPF at 50 % strain is increased by 97 %. It should be noted that after rosin treatment, the compressive strength and elasticity of r-RPF were improved. The r-RPF was stable in water and had a high oil adsorption capacity (7.8 to 28.13 g/g), which had the potential to treat current oily wastewater.

We present a novel diagnostic platform using differential dynamic microscopy (DDM) to quantify viral load in salivary samples. This method leverages gold nanoparticle-based sensors that form heteroaggregates with virus-like particles (VLPs), designed to mimic viruses. The sensors were sequentially functionalized with biotin, streptavidin, and biotinylated angiotensin-converting enzyme 2, while VLPs were functionalized with streptavidin and the spike S1 receptor-binding domain of SARS-CoV-2 as the model virus. Viral load is quantified by tracking changes in the sensor nanoparticle dynamics during their interactions with VLPs. Initially optimized in buffer and subsequently adapted for salivary samples, the assay leverages dark-field DDM to remove possible interference from unbound VLPs. This approach enables the quantification of VLPs that were otherwise undetectable by dark-field DDM alone, by exploiting the slower diffusion of nanosensor-VLP heteroaggregates, achieving a detection limit of 9 × 103 VLPs/mL (20 pg/mL), within clinically relevant viral loads. The platform requires minimal sample preparation, a 5 min incubation, and no fluorescent labeling or washing steps. With only a conventional microscope and camera, this rapid assay provides quantitative results in 10-15 min. This proof-of-concept offers an accessible tool for rapid and precise viral load quantification in laboratory settings with potential for point-of-care applications through setup miniaturization. With its simplicity, speed, and sensitivity, this platform represents a promising advancement in infectious disease diagnostics.

This research evaluates Cu as a low-cost promoter for a Co-based Fischer-Tropsch synthesis (FTS) catalyst. Co selectively produces alkenes and long hydrocarbon chains, while Cu's affinity for H-2 adsorption ensures Co remains in its metallic state. Moreover, Cu promotes Co to synthesize longer hydrocarbon chains, specifically in the C-8-C-16 range. The synergistic relationship between Cu and Co reduces the formation of undesirable products active at low and medium temperatures (<300 degrees C). We synthesized seven catalysts with varying Cu NPs loadings from 0 to 0.15 g g(-1) of Cousing the ultrasonic impregnation method, controlling the size of the catalyst in the nanorange (<20 nm). Among them, Co15-Cu0.15 is the best-performing catalyst with a CO conversion of 66% and selectivity for C5+ paraffin of 29% while having the lowest selectivity for CH4 at 16%. Transmission electron microscopy (TEM) images showed uniformly dispersed CoCu NPs on the Al2O3 support before the reaction. Scanning transmission electron microscopy (STEM), X-ray diffraction (XRD), and temperature-programmed reduction (TPR) were also used to characterize the catalysts. Furthermore, we developed a kinetic model to evaluate the influence of Cu loading on the product distribution. Co15-Cu0.15 was determined to yield the most C5+ hydrocarbons and decrease the yield of C3+, complementing our experimental results.

Freshwater represents one of the most precious resources on the planet, so it is fundamental to preserve it. In this work, an innovative sunlight-driven device composed of bismuth oxybromide (BiOBr) grown on a material derived from natural sources, i.e., Lightweight Expanded Clay Aggregates (LECA), is developed to clean surface waters under natural solar light irradiation. For this purpose, the photodegradation of two non-steroidal anti-inflammatory drugs, ibuprofen, and diclofenac, is investigated under varying operative conditions. Laboratory- and real-scale experiments reveal that the fabricated floating BiOBr/LECA photocatalyst fully degrades diclofenac, whereas limited abatement of ibuprofen is observed. Based on the identification of specific transformation products (TPs) during the degradation, this behavior seems to be strongly related to the different structures of the two drugs. In fact, the main TP produced during diclofenac degradation derives from dechlorination and ring condensation: this type of photocatalytic degradation pathway is generally favored over the CC bonds's cleavage, which is a unique possibility for IBU abatement. Moreover, the potential partial adsorption of these species on the photocatalyst's active sites can cause their deactivation. Finally, reusability tests demonstrate the high stability of the floating composite. An innovative floating photocatalyst, based on BiOBr grown on LECA, is fabricated and tested in the degradation of two common non-steroidal anti-inflammatory drugs, diclofenac, and ibuprofen, under solar light irradiation in different conditions. The very promising results pave the way for BiOBr/LECA as an alternative to traditional materials for the effective removal of pharmaceutical drugs from water. image

Pectin is a valuable product that can be extracted from waste fruit peels. Here we propose the use of graphene oxide (GO)-based membranes for pectin concentration. The synthesized GO was functionalized with ethylenediamine (EDA) to molecularly design the GO framework. Kaolin hollow fibers with asymmetric pore distribution were used as a porous substrate for GO/EDA deposition. A GO/EDA layer with a thickness of 2.86 +/- 0.24 um was assembled on the substrate by the simple vacuum-assisted deposition method. After GO/EDA depositions, the water permeance of the pristine kaolin hollow fibers reduced from 8.46 +/- 0.17 to 0.52 +/- 0.03 L h - 1 center dot m- 2 center dot kPa- 1.A pectin aqueous extract from orange peels was filtered at cross-flow mode through the prepared membranes and the steady-state fluxes through pristine and GO/EDA-coated hollow fibers were 56 +/- 2 and 20 +/- 3 L h-1 m- 2, respectively. The GO/EDA-coated membrane presented greater pectin selectivity than the pristine hollow fiber. The GO/EDA-coated hollow fiber concentrated the galacturonic acid, phenolic, and methoxyl contents in 19.5, 17.4, and 29.2 %, respectively. Thus, filtration through the GO/EDA-based membrane is a suitable alternative for pectin concentration.

Most ultrasound-based processes root in empirical approaches. Because nearly all advances have been conducted in aqueous systems, there exists a paucity of information on sonoprocessing in other solvents, particularly ionic liquids (ILs). In this work, we modelled an ultrasonic horn-type sonoreactor and investigated the effects of ultrasound power, sonotrode immersion depth, and solvent’s thermodynamic properties on acoustic cavitation in nine imidazolium-based and three pyrrolidinium-based ILs. The model accounts for bubbles, acoustic impedance mismatch at interfaces, and treats the ILs as incompressible, Newtonian, and saturated with argon. Following a statistical analysis of the simulation results, we determined that viscosity and ultrasound input power are the most significant variables affecting the intensity of the acoustic pressure field (P), the volume of cavitation zones (V), and the magnitude of the maximum acoustic streaming surface velocity (u). V and u increase with the increase of ultrasound input power and the decrease in viscosity, whereas the magnitude of negative P decreases as ultrasound power and viscosity increase. Probe immersion depth positively correlates with V, but its impact on P and u is insignificant. 1-alkyl-3-methylimidazolium-based ILs yielded the largest V and the fastest acoustic jets – 0.77 cm3 and 24.4 m s−1 for 1-ethyl-3-methylimidazolium chloride at 60 W. 1-methyl-3-(3-sulfopropyl)-imidazolium-based ILs generated the smallest V and lowest u – 0.17 cm3 and 1.7 m s−1 for 1-methyl-3-(3-sulfopropyl)-imidazolium p-toluene sulfonate at 20 W. Sonochemiluminescence experiments validated the model.