The chemical industry is increasingly prioritizing sustainability, with a focus on reducing its carbon footprint to achieve net zero. By 2026, the Together for Sustainability consortium will require reporting the biogenic carbon content (BCC) in chemical products, posing a challenge as the BCC depends on feedstocks, value chain configuration, and process-specific variables. While carbon-14 isotope analysis can measure the BCC, it is impractical for continuous industrial monitoring. This work presents CarAT (Carbon Atom Tracker), an automated methodology for calculating the BCC across industrial value chains, enabling iterative and accurate sustainability reporting. The approach leverages existing Enterprise Resource Planning data in three stages: preparing value chain data, performing atom mapping in chemical reactions using chemistry language models, and applying a linear program to calculate the BCC given known inlet compositions. The methodology is validated on a 27-node industrial toluene diisocyanate value chain. Three scenarios are analyzed: a base case with all fossil feedstocks, a case incorporating a renewable feedstock, and a butanediol value chain with a recycle stream. The results are visualized using Sankey diagrams, showing the flow of carbon attributes across the value chain. The key contribution is a scalable, automated framework for BCC calculation that can update as industrial conditions change. CarAT enables chemical manufacturers to comply with upcoming sustainability mandates while supporting carbon neutrality goals by facilitating the systematic substitution of fossil carbon with biogenic alternatives. By providing transparent, auditable tracking of carbon sources throughout production networks, this framework empowers the broader chemical industry to make data-driven decisions for achieving net-zero targets and accelerating the transition to sustainable manufacturing.

Methane pyrolysis in molten halide salts offers a promising route to low-CO2 hydrogen, with inherent carbon separation. While NaBr-KBr has been widely studied, the kinetic behavior of lithium-based eutectic salts remains underexplored. Here, we evaluate methane pyrolysis in NaBr-KBr, LiCl-NaCl, LiBr-NaBr, and LiBr-LiCl, with and without suspended Co/Al2O3 catalysts. In the catalyst-free regime, CH4 conversion at 1000 degrees C reached 5.4% in LiCl-NaCl, and 5.8% in NaBr-KBr. Activation energies for lithium-based melts ranged from 261 to 274 kJ mol- 1, compared to 245 kJ mol- 1 for NaBr-KBr. Catalyst addition nearly tripled conversions, with LiCl-NaCl achieving a conversion of 15.2%. Activation energies dropped to 139-162 kJ mol- 1. LiCl-NaCl retained the highest performance despite its higher barrier. These results provide a direct kinetic comparison of lithium-based molten salts for methane pyrolysis, confirming the effectiveness of Co/Al2O3 suspensions in enhancing methane conversion across all investigated systems and experimentally validating cobalt as a robustly active decomposition phase in chemically distinct halide matrices.

The industrialization of catalytic processes hinges on the availability of reliable kinetic models for design, optimization, and control. Traditional mechanistic models demand extensive domain expertise, while many data-driven approaches often lack interpretability and fail to enforce physical consistency. To overcome these limitations, we propose the Physics-Informed Automated Discovery of Kinetics (PI-ADoK) framework. By integrating physical constraints directly into a symbolic regression approach, PI-ADoK narrows the search space and substantially reduces the number of experiments required for model convergence. Additionally, the framework incorporates a robust uncertainty quantification strategy via the Metropolis-Hastings algorithm, which propagates parameter uncertainty to yield credible prediction intervals. Benchmarking our method against conventional approaches across several catalytic case studies demonstrates that PI-ADoK not only enhances model fidelity but also lowers the experimental burden, highlighting its potential for efficient and reliable kinetic model discovery in chemical reaction engineering.

Optimising continuous phototrophic cultivation remains a major challenge for scalable, energy-efficient cyanobacterial bioprocesses. Here, we combine controlled photophysiology, long-term continuous experimentation, multi-parameter analysis, and batch-derived Monod kinetic modelling to define a precise operational window for Synechocystis sp. PCC 6803 under flat-plate photobioreactor (FP-PBR) illumination. Using a fully calibrated FP-PBR platform, we first quantified intrinsic growth limits (µ max = 0.081-0.118 day-1) across low, moderate, and high irradiance regimes, establishing the illumination-driven growth ceilings that constrain downstream continuous operation. Guided by these kinetic boundaries, continuous cultivation demonstrated that productive steady-state growth emerges only within a narrow regime governed by light intensity (500-700 µmol photons m-2 s-1), temperature (32-34 °C), and dilution rate (0.12-0.14 day-1). Single-parameter and 3D interaction analyses revealed strong coupling between photonic supply, thermal sensitivity, and hydraulic residence time, while multi-factor modelling captured these nonlinear constraints and accurately predicted washout boundaries. Translating these insights into sustainability metrics, the optimised regime supports 0.07-0.125 g L-1 day-1 of biomass productivity, equivalent to 8.4-15.0 g biomass day-1 and 176-315 kJ day-1 of chemical energy in a 120 L mini-pilot system. Stoichiometric analysis indicates this corresponds to 15.6-27.6 g CO2 day-1 sequestered, demonstrating measurable environmental benefit even at a small scale. Together, these results provide a mechanistically grounded, kinetically constrained framework for designing inherently efficient, low-waste, and model-predictive cyanobacterial photobioprocesses aligned with green chemistry and future carbon-neutral manufacturing.

A Ni(OH) 2 /BiVO 4 heterostructure enables photo-driven Ni(OH) 2 -NiOOH formation and effective oxidation of 5-hydroxymethylfurfura to 2,5-furandicarboxylic acid with high operational stability and a low interfacial activation energy.

Polyaromatic hydrocarbons (PAH) are present in several industrially relevant streams, including light cycle oil, coal- and bio-derived oils, high-temperature gasification tars, and asphaltenic oils, posing processing challenges due to coke formation and low conversion to valuable products with conventional technologies. This study focuses on oxidative cracking of model compound phenanthrene in supercritical water (SCW) at low oxidant concentration as a route to produce chemicals of industrial interest from PAHs. While some studies have dealt with PAH SCW oxidation, these were carried out in large oxygen excess, aiming to eliminate PAHs through complete oxidation. This work shows that phenanthrene underwent fast oxidation promoted by reactive oxygen species (ROS) from H2O2 decomposition, but its conversion leveled off once ROS were consumed. However, the oxygenated species formed continued reacting in SCW over longer timescales. A reaction pathway is proposed based on the evolution of the main intermediate compounds with time and temperature. Anthraquinone was the main product at early reaction stages, with 0 min selectivity above 65% at all temperatures. It further reacted to form xanthone and fluorenone as main intermediates, reaching selectivity of up to 35% and 32% respectively. At later reaction stages, higher selectivity of up to 31% and 34% towards dibenzofuran or fluorene, respectively, indicates in-situ deoxygenation of intermediate products. This pathway showed differences with those measured under large oxygen excess, as oxidation starts in central positions and further reactions lead to a range of products with progressively less oxygen as well as a hydrogen-rich gas, while coke yields remain low.

In response to accelerating global climate change and the urgent demand for sustainable energy, research efforts are increasingly concentrated on developing photo-microbial electrosynthesis (PMES) technologies that convert CO2 into valuable products, such as acetate, butyrate, and ethanol. However, many conventional semiconductor materials remain inadequate for real-world implementation due to their low specific surface area and insufficient CO2 adsorption capability, both of which are essential for efficient conversion. To overcome these limitations and boost the overall performance of PMES, researchers have explored the feasibility of using graphitic carbon nitride (g-C3N4), a two-dimensional polymeric semiconductor material, as a photocatalyst due to its exceptional chemical and physical stability, environmental friendliness, and pollution-free benefits. Therefore, this comprehensive review addresses the research gap by examining various PMES configurations with enhanced photocatalytic reactions using g-C3N4 and its metal-based composites to improve the bioelectrochemical conversion of CO2 to volatile fatty acids, which have not been critically reviewed. The review also elucidates the catalytic performance of g-C3N4 as anode and cathode in terms of efficiency and stability, recent advancements and potential optimization strategies influencing CO2 reduction in PMES. Finally, emerging operational techniques using g-C3N4 in PMES are thoroughly discussed, with strategies to promote CO2 bioconversion for field-scale applications.

Heterogeneous catalytic methane pyrolysis can produce hydrogen without generating CO2, but catalyst coking and deactivation hinder continuous operation. We present a bubbling fluidized-bed reactor system utilizing low-surface-area carbon spheres bearing 5 wt% Fe, Co, or Ni, engineered to shed pyrolytic carbon by interparticle abrasion. Catalysts were made by wet impregnation or by ion-exchange followed by carbonization. Characterization used N-2 adsorption surface area, X-ray diffraction, Raman spectroscopy, scanning and transmission electron microscopy, and oxidative thermogravimetric analysis. Conversion was measured by mass spectrometry at 700-850 degrees C after 24 and 48 h of continuous operation. Reactor design was performed targeting a bubbling operating regime. Stable conversions were sustained for the catalysts prepared by wet impregnation, with activity ordering Co > Ni > Fe during the first run. Microscopy directly revealed pyrolytic carbon detachment and, on cobalt catalysts, an abundant, distinctive coiled carbon nanotube morphology among the multiwalled carbon nanotubes and cotton-like morphologies present in all pyrolytic samples. After each run, dry sieving isolated a carbon product and recovered catalyst spheres for reuse. Second runs showed similar to 40% lower rates, attributed to non-optimized separation and mechanical damage rather than intrinsic deactivation. Thermogravimetric analysis of separated carbon showed less than 0.1 wt% residue and no detectable metals, whereas the spheres retained most metal: roughly 70-90% for wet impregnation and 90-95% for ion-exchange, consistent with partial encapsulation in ion-exchanged materials and higher surface accessibility in impregnated materials. These results demonstrate robust abrasion-assisted self-cleaning that preserves fluidized-bed operability and outline a path to continuous methane pyrolysis with downstream particulate recovery.

Machine learning approaches for conceptualizing and designing in silico compounds have attracted significant attention. However, the applicability of these compounds is often challenged by synthetic viability and cost-effectiveness. Researchers introduced proxy-scores, known as synthethic accessiblity scoring, to quantify the ease of synthesis for virtual molecules. Despite their utility, existing synthetic accessibility tools have notable limitations: they overlook compound purchasability, lack physical interpretability, and often rely on imperfect computer-aided synthesis planning algorithms. We introduce MolPrice, an accurate and fast model for molecular price prediction. Utilizing self-supervised contrastive learning, MolPrice autonomously generates price labels for synthetically complex molecules, enabling the model to generalize to molecules beyond the training distribution. Our results show that MolPrice reliably assigns higher prices to synthetically complex molecules than to readily purchasable ones, effectively distinguishing different levels of synthetic accessibility. Furthermore, MolPrice achieves competitive performance on literature benchmarks for synthetic accessibility. To demonstrate its practical utility, we conduct a virtual screening case study, illustrating how MolPrice successfully identifies purchasable molecules from a large candidate library. MolPrice bridges the gap between generative molecular design and real-world feasibility by integrating cost-awareness into synthetic accessibility assessment, making it a powerful model to accelerate molecular discovery.

Methane pyrolysis can produce hydrogen without direct carbon dioxide emissions, but large co-product streams of solid carbon must be valorized for the process to be economically viable at scale. Because carbon properties vary strongly with processing route, fragmented characterizations hinder informed selection. This study presents a comprehensive, side-by-side comparison of pyrolytic carbons across various methane-pyrolysis methods by combining new measurements (molten salts, coke-seed growth, and transition-metal catalysis) with existing literature datasets and reference carbons. An integrated program of structural, spectroscopic, microscopic, thermal stability, and bulk property measurements establishes route-specific fingerprints and quantifies how method choice governs properties, including ordering, morphology, oxidation stability, density, and electrical conductivity. Cross-technique correlations link indicators of graphitic order and bonding environment to stability, yielding a systematic structure–stability perspective that turns disparate datasets into a comparative decision resource. The resulting analysis highlights how different methane pyrolysis routes tend to produce carbons with varying property profiles aligned to different technological needs, supporting early-stage decision-making in process development by providing comparable structure–property benchmarks for screening and prioritization.

Here, we report the development and application of a hierarchical computer simulation of the molten salt methane pyrolysis process for low CO2-emission H2 production. First, a hierarchical model was developed in Aspen Plus that uses a reactor network to consider gas-phase reaction and liquid film reaction near the gas-liquid interface of the bubbles. Equations were used to correlate reactor geometry and transfer phenomena, and rate expressions were developed to model gas-phase kinetics inside the bubbles, mass transfer into the surrounding film, as well as liquid film reaction. The model was validated using our previous data for molten salt (NaBr+KBr) pyrolysis experiments at varying conditions, such as temperature (850-1000 degrees C), methane partial pressure, reactor height, and bubble sizes. The flowsheet was then modified to model an industrial-scale reactor for an annual output of 18.6 kt a-1 H2 (equivalent to 83.5 MW HHV). This was done by modelling fluid mechanics and transfer phenomena, such as bubble sizes, bubble break-up and coalescence. Furthermore, additional unit vessels, such as a gas separation, heat exchangers, and salt recovery were added, and a heat integration optimization was performed. We are discussing the hydrogen costs for a wider natural gas feedstock and CAPEX range, which is $2.26-$3.85 kg-1 H2 when not considering revenues from carbon selling. When selling the by-product for $200 t-1, this range lowered to $1.57-3.17 kg-1 H2. This work uniquely integrates film-level transport modelling, reactor hydrodynamics, and full process flow sheeting within a single simulation framework validated by experimental data and extended to techno-economic assessment and could serve as a protocol on how to transfer lab data to model industrial-scale processes.

The hydrodynamic characteristics of liquid-liquid flows in a micro-mixing plate with a ‘heart/spade’ geometry and a hydraulic diameter of ∼ 0.36 mm at the contraction point are studied experimentally in the Reynolds number range of 250 - 1,000 . Localised optical observations of the two-phase flow within the micro-mixing device units are performed using a high-speed camera in combination with an LED-induced fluorescence imaging technique. A qualitative interpretation of the instantaneous images enabled the development of a regime map with three stable flow patterns, each with a metastable transitional state. The formation of secondary flows and recirculation zones resulted in the breakup of interfaces and fragmentation of droplets. The droplet size distribution of an MTBE-water dispersion is studied across a broad range of the dispersed phase (water) ratios, ϕ _d = 0.091 - 0.714 . The resulting Sauter mean diameter of water droplets is used to evaluate the improvement in the specific surface area and was correlated as a function of the energy dissipation rate and the Reynolds and Weber numbers.

Microkinetic models are key for evaluating industrial processes' efficiency and chemicals' environmental impact. Manual construction of these models is difficult and time-consuming, prompting a shift to automated methods. This study introduces SiMBA (Simplest Mechanism Builder Algorithm), a novel approach for generating microkinetic models from kinetic data. SiMBA operates through four phases: mechanism generation, mechanism translation, parameter estimation, and model comparison. Our approach systematically proposes reaction mechanisms, using matrix representations and a parallelized backtracking algorithm to manage complexity. These mechanisms are then translated into microkinetic models represented by ordinary differential equations, and optimi/zed to fit available data. Models are compared using information criteria to balance accuracy and complexity, iterating until convergence to an optimal model is reached. Case studies on an aldol condensation reaction, and the dehydration of fructose demonstrate SiMBA's effectiveness in distilling complex kinetic behaviors into simple yet accurate models. While SiMBA predicts intermediates correctly for all case studies, it does not chemically identify intermediates, requiring expert input for complex systems. Despite this, SiMBA significantly enhances mechanistic exploration, offering a robust initial mechanism that accelerates the development and modeling of chemical processes. By automating microkinetic model generation from a data-first approach, SiMBA opens new avenues for future research in automated mechanism discovery.

The industrialization of catalytic processes requires reliable kinetic models for design, optimization, and control. While white box models are preferred for their interpretability, they demand considerable time and expertise for their construction. This research enhances the ADoK-S framework by embedding prior expert knowledge using mathematical constraints and integrating uncertainty quantification. The improved methodology consists of: (I) a genetic programming algorithm with constraints to produce physically coherent candidate models, (II) a sequential optimization algorithm for parameter estimation, (III) model selection based on the Akaike information criterion (AIC), and (IV) uncertainty quantification of the chosen model�s predictions. The refined approach not only requires less data for discovering kinetic models but also ensures physically sound proposals. With the inclusion of uncertainty quantification, the method bolsters prediction reliability, and aids in safer system developments � crucial for decision-making and risk management. These improvements enhance data efficiency, model reliability, and position automated knowledge discovery as a real alternative to traditional kinetic modeling techniques.

Ethylene produced from steam cracking includes an acetylene impurity of 0.5-3%, harming the downstream polymerization process. To achieve polymer-grade ethylene, acetylene must be removed by chemoselective hydrogenation to ethylene without overhydrogenation to ethane. The current state-of-the-art process uses supported Pd nanoparticles (NPs) and toxic CO injections to poison the active sites, which is expensive and shows poor ethylene selectivity. To tackle this issue, the use of single-atom catalysts can offer a way to simultaneously improve selectivity through preferential desorption of ethylene over its hydrogenation and minimize cost. In particular, single-atom cobalt catalysis can address both of these issues. However, to date, single-atom cobalt has not been tested for this reaction. Herein, we present a cost-effective monometallic, cobalt-anchored zeolite Y (Co1@Y) catalyst, synthesized via an in situ hydrothermal method, holding isolated active cobalt atoms that efficiently and selectively hydrogenate acetylene to ethylene. Characterization techniques proved the absence of NPs and the presence of single-atom cobalt sites. The catalyst achieved an ethylene selectivity of 90 +/- 2% at full acetylene conversion, with a stable performance for over 400 h. Co1@Y achieved TOFethylene greater than the previously reported zeolite-supported single-atom catalysts by similar to 5 times. Varying the dispersion of cobalt from an NP to a single atom modified the reaction mechanism from associative to dissociative, remarkably improving catalytic activity and selectivity. This strategy can be extended to other relatively inactive metals and other hydrogenation reactions.

Accumulation of per- and polyfluoroalkyl substances (PFAS) in soil, sediment, and water poses significant public health risks due to their persistence and potential toxicity. PFAS compound possesses strong C - F bonds that require very high energy to break, making current technology unsustainable and challenging for large-scale treatment. Recent mechanistic insights into microbial degradation of PFAS offer promising solutions for their sustainable degradation. Specifically, bioelectrochemical systems can effectively break the strong C - F bonds in PFAS using high-energy electrons generated from electroactive microbes at a conductive anode electrode, achieving an astonishing removal efficiency of up to 96 %. However, these systems are still experimental, requiring further optimization for successful large-scale applications. This concise yet detailed review aims to enhance understanding of the emergence of PFAS as a pervasive potent chemical, microbe-assisted degradation mechanisms, and microbial community analysis, guiding future research and policy development for improved public health and environmental management.

The rapid advancement of computational drug discovery has enabled the generation of vast virtual libraries of promising drug candidates. However, evaluating the synthetic accessibility (SA) of these compounds remains a critical bottleneck. While computer-aided synthesis planning (CASP) tools can provide synthesis routes to the candidate, their computational demands make them impractical for large-scale screening. Existing rapid SA scoring methods, struggle to generalize to out-of-distribution molecules and do not account for economic viability. To address these challenges, we present MolPrice, an accurate and reliable price prediction tool. By introducing a novel self-supervised learning approach, MolPrice achieves robust generalization to diverse molecular structures of various complexities. Our comprehensive analysis of model architectures and molecular representations reveals that substructure-based features strongly correlate with market prices, supporting the relationship between synthetic complexity and economic value. MolPrice performs well on the standard literature SA benchmark, showcasing its ability for SA estimation. MolPrice thus serves as both an accurate molecular price predictor and a rapid synthetic accessibility assessment tool, enhancing the efficiency of modern drug discovery pipelines.

The robustness of the flash thermal racemization of optically active 1-phenylethylamine over Pd/γ-Al2O3 was studied by applying split-plot design-of-experiments (DoE), where the effects of temperature, flow rate, and concentration (3-factors) on the e.e. and selectivity (2-responses) were examined and quantified. The same effects were also interrogated using multivariate ramps in transient flow to produce response surfaces for the reaction space. The same set of optimal conditions for the process was identified by both approaches, and the same relationships between the variables were observed: while the extent of racemization (e.e.) can be directly correlated to temperature, a more complex relationship between temperature and flow rate on the selectivity was uncovered.

An acetylene impurity of 0.5-3 % is present in ethylene produced from steam cracking, disrupting the polymerisation process by causing harm to the Ziegler-Natta catalyst. Achieving polymer-grade ethylene requires the removal of acetylene through chemoselective hydrogenation to ethylene, preventing over-hydrogenation to ethane. The current state-of-the-art process employs expensive, scarce Pd nanoparticles with low selectivity. Single-atom catalysts (SACs) enhance selectivity by preferentially desorbing ethylene. Single atoms of earth-abundant iron can improve selectivity and reduce costs. Here, we introduce zeolite Y-supported single-atom iron (Fe-1@Y) catalyst, prepared through in-situ hydrothermal method, which efficiently catalyses semi-hydrogenation of acetylene to ethylene. Characterisation techniques confirm absence of Fe nanoparticles and presence of single-atom Fe sites. Fe-1@Y achieves a remarkable ethylene selectivity of 93 % +/- 2 % at full acetylene conversion, following a dissociative mechanism and stable operation for over 600 h. Under industrial conditions with excess ethylene in the feed, ethylene selectivity of 91 % +/- 2 % was maintained at full acetylene conversion. The TOFC2H2 -> C2H4 of 711 mol(C2H2 -> C2H4)h(-1) mol(Fe) (-1) achieved by Fe-1@Y is similar to 14 times greater than previously reported zeolite-supported SACs. Changing iron dispersion from nanoparticles to single atoms significantly enhanced catalytic activity and selectivity, a strategy extendable to other moderately active metals and hydrogenation reactions.