Digitalisation holds promise for the transformation of modern chemical processes into intelligent manufacturing systems that are responsive to dynamic market demands and sustainability goals. To achieve interconnection of physical installations with their digital twins, we developed a semantic framework which enables systematic knowledge management and effective utilisation of plant data, serving as a backbone of intelligent manufacturing. Our approach leverages ontologies and agents built on a knowledge graph infrastructure to host the deployment of both first-principles and artificial intelligence (AI) models of chemical processes. Additional ontologies were designed for connecting models with industrial plants. We present an end-to-end demonstration of the knowledge graph-based digital twin system and illustrate this technology with a use case of anomaly detection in an industrial pilot plant for nanomaterials synthesis. The implementation includes data acquisition, secure communication protocols, cloud-hosted data storage, dedicated AI workflows, and a user interface with version control for both ontologies and models. The results demonstrate the effectiveness of the semantic framework in managing chemical process knowledge, linking plants to first-principles and data-driven models, and enabling the execution of complex AI-driven workflows.

Metal-organic polyhedra (MOPs) offer a modular route to porous molecular materials, but their discovery is limited by the difficulty of translating digital designs into experimentally accessible compounds. Here, we demonstrate an integrated workflow for predicting and synthesising novel MOPs in The World Avatar. To our knowledge, this is the first reticular material discovery workflow executed and persisted entirely in a shared machine-readable knowledge representation, without human-specified initial reaction conditions. This domain’s small literature corpus hinders data-driven synthesis prediction, yet its high symmetry provides exploitable structural analogies. We therefore encode auditable chemical reasoning as explicit rules rather than learning it statistically. MOP candidates are linked to synthesis knowledge, so procedures for new targets are inferred by analogy to known materials and rendered as human-readable instructions and machine-executable protocols. Candidates are assessed through hierarchical geometry optimisation, electronic-structure evaluation, crystal structure prediction, and simulated powder X-ray diffraction. We evaluate the workflow retrospectively against reported zirconium MOP syntheses and prospectively by synthesising a previously unreported Zr-EDB-MOP. Infrared spectroscopy, high-resolution mass spectrometry, and powder X-ray diffraction support formation of the targeted cage in agreement with the predicted model. This establishes a proof of concept for knowledge-integrated discovery in sparse, highly structured materials domains.

The purpose of this paper is to introduce a Material Passport Ontology (MPO), which is designed to enable the interoperability of material passport data across stakeholders, including manufacturers, suppliers, collectors, and recyclers. The MPO provides a novel unifying ontological infrastructure for representing the properties of products and recycling processes. These properties are essential for estimating the potential recycled yield and enabling the stakeholders to make informed decisions about the feasibility of recycling. The ontology was co-created with industrial stakeholders, following a pragmatic ontology development method. The MPO is organised into facets describing the physical properties, composition and circularity, biological and other properties of products, components and materials. A set of constraints was developed to address the non-conformant syntactic or value range inconsistencies identified by the industrial stakeholders. These inconsistencies occurred in properties such as international product identification numbers and mass-related properties represented in a material passport knowledge graph. The information provided by the knowledge graph enables the assessment of the circularity of products, components and materials. The MPO was validated using reasoning tools and in collaboration with domain experts from industrial partners representing two use cases: the manufacture of components for motor vehicles and blades for wind turbines. These use cases demonstrate its effectiveness and applicability in identifying recyclable materials, maximising resource reuse, and enhancing sustainability practices, thereby facilitating the transition to a circular economy.

The environment in which people spend their lives influences their health behaviours and health outcomes. Traditional studies often use fixed descriptions and ad hoc analyses to characterise environmental exposures at static locations, relating a single environmental factor to one health aspect, even though both the environment and the exposures of people who move through it are dynamic. We introduce a digital twinning approach that semantically integrates ontological concepts and data across environmental features into machine-readable knowledge, enabling scalable and context-aware assessment of individual exposures. This work is built on The World Avatar project, which aims to create an all-encompassing digital twin based on a dynamic knowledge graph. The proposed approach deploys a computational agent to calculate exposures based on semantic representations of environmental features in time and space. The time-specific part of the exposure calculation considers both the historical context (i.e. whether features of the environment had been built) and current context (i.e., whether something was open or accessible) of the exposure. The scalability of the approach is illustrated through the construction of interoperable digital twins and the analysis of smartphone data describing movements in both the UK and Singapore. The resulting exposures capture historic year-specific changes in greenspace and time-specific accessibility based on opening hours of the food retail environment. This approach could facilitate large-scale digital studies, yielding critical insights that help create healthy cities.

Methylcyclohexane/toluene (MCH/TOL) is a promising liquid organic hydrogen carrier (LOHC) due to their low toxicity and compatibility with existing fossil fuel infrastructure. Developing high‐performance and economical catalysts for H 2 release from MCH is crucial for practical applications. Previously reported Pt single‐atom catalysts (SACs) with loading up to 0.15 wt% are either nearly inactive or exhibit limited cycloalkane dehydrogenation conversions under realistic space velocities. Here, we successfully synthesized mesoporous hollow ceria‐supported Pt SACs (Pt 1 / mh ‐CeO 2 ) with tunable Pt loadings of 0.1–0.75 wt%, which operate efficiently under carrier‐gas‐free industrial conditions. At 0.25 wt% Pt loading, an exceptionally high hydrogen evolution rate of 4705 mmol·g Pt −1 ·min −1 can be obtained near the kinetic region (25% MCH conversion). Under a more realistic condition with 94% conversion, the same catalyst still affords high apparent hydrogen evolution rate of 3106 mmol·g Pt −1 ·min −1 with good stability, markedly surpassing previously reported Pt catalysts. The significantly boosted performance of Pt SACs by mesoporous hollow ceria is attributed to (i) accessible Pt single‐sites, (ii) enhanced mass transfer for large C 6 ‐ring molecules with kinetic sizes smaller than the pore dimensions, and (iii) promoted MCH adsorption/activation on isolated Pt sites anchored on ceria with rich oxygen vacancies, as evidenced by vapor diffusion experiment and dynamics simulations.

Reticular materials have come to the fore of chemistry with exceptional potential in applications ranging from CO2 capture and chemical separations to catalysis and drug delivery. However, due to the vast combinatorial space of molecular building blocks that can form these materials, designing high-performing reticular materials for applications remains a considerable challenge. Here, we present a computational approach that combines a library of molecular fragments suitable for constructing organic building units, template-based reassembly, and evolutionary optimization to accelerate the discovery of reticular materials. Applied to metal-organic polyhedra (MOPs), this approach produces a design space of nearly 800,000 MOP configurations. A genetic algorithm (GA) based on the molecular fragments is shown to be effective at rapidly identifying optimal MOPs within this space, demonstrated through optimizing cavity properties for host-guest applications and CO2 interaction energies estimated by machine-learning-accelerated simulations. An important component of our approach is that it is fully ontologized and integrated within The World Avatar, forming part of a broader, interoperable knowledge model for the discovery of reticular materials.

High-fidelity quantum chemical (QM) data sets that jointly resolve reaction thermochemistry, kinetics, and solvation at scale remain scarce, especially for radical chemistry. We introduce QuantumPioneer, an open-access reaction-centered QM database and workflow for small organic molecules, focused on peroxyl-mediated hydrogen atom transfer (HAT) and the corresponding homolytic bond dissociation reactions. QuantumPioneer contains 348,258 species (2-21 heavy atoms), 167,237 validated HAT transition states (TS) with corresponding reaction energies and homolytic bond dissociation energies (BDEs), and over 100 million COSMO-RS solvation free energies(ΔGsolv*) and enthalpies (ΔHsolv*) across 295 solvents. The workflow uses ωB97X-D/def2-SVP geometries, DLPNO-CCSD(T)-F12d/cc-pVTZ-F12 single-point energies, empirical thermochemical corrections, transition-state theory, and COSMO-RS BP-TZVPD-FINE solvation in a single high-throughput pipeline. Our benchmarks show reliable accuracy, with mean absolute errors (MAEs) compared to experimental and high-level QM reference data of 0.82 kcal/mol for gas-phase enthalpies of formation, 1.60 kcal/mol for C-H BDEs, 1.45 kcal/mol for HAT barriers, and 0.57 kcal/mol for ΔGsolv* values. We demonstrate two predictive applications. First, we show that combining BDE and HAT-barrier models identifies experimentally observed oxidative degradation sites in drug-like molecules with a 91% top-5 hit rate and 82% site-level recall. Second, we show that a QM-parametrized Abraham model enables rapid solvation energy estimates at near-COSMO-RS accuracy within its training domain, reproducing computed ΔGsolv* and ΔHsolv* values with MAEs of 0.16 and 0.18 kcal/mol, respectively, though performance on experimental ΔGsolv*values for unseen solutes was worse, with an MAE of 1.32 kcal/mol. This work provides a scalable template for other reaction families, unifying equilibrium species, validated TS, thermochemistry, kinetics, and solvation into one workflow.

The defossilisation of the global electricity system is critical for mitigating climate change. Wind and solar PV play critical roles in this shift; however, their intermittency presents a significant challenge. Intercontinental electricity transmission offers a potential solution to mitigate this intermittency. This study investigates the energetic feasibility of a hypothetical global electricity grid relying solely on wind and solar PV energy. An optimisation problem was solved to determine the deployment of wind and solar PV capacities that minimise excess electricity generation. The simulations use a much higher spatial resolution of renewable potentials than in previous studies of global grids. They suggest that a global grid could reduce excess electricity generation by up to 92% compared to an equivalent no transmission scenario and increase the correlation coefficient between the time-varying global generation and demand to 0.65. Analysis of global power flows estimated that approximately 3.6% of global demand would be lost during transmission. The study contextualised the power lost through transmission and curtailment by comparing it to the losses that would occur if other energy vectors (e.g., hydrogen) were used or if the curtailed power were redirected for other purposes. The efficiency of the global grid was found to be significantly higher than that of hydrogen — 93.1% compared to approximately 30%. Additionally, if the excess electricity were used for hydrogen production, direct air capture (DAC), or desalination, it could address approximately 21.1% of the anticipated global hydrogen demand in 2050, 3.3% of the global CO2 removal required by 2030 to meet Net Zero targets, or meet 33% of the estimated global freshwater demand in 2050 through desalination.

Abstract This work investigates the effects of hydrogen blending on soot formation and flame structure in a laboratory-scale rich–quench–lean (RQL) burner using ethylene–hydrogen mixtures (0–50 vol. % hydrogen) at constant carbon mass flow rate. Laser-induced incandescence (LII) and single-ring and multicyclic polycyclic aromatic hydrocarbons (PAH) planar laser-induced fluorescence (PLIF) were employed to quantify soot and PAH distributions, respectively, while OH* chemiluminescence was used to detect the flame structures and reaction zone location. Hydrogen addition progressively reduced soot by ca. 9%, 36%, and 68% at 10, 30, and 50 vol. % blending, respectively, with multicyclic PAHs decreasing more than single-ring aromatics. The single-ring aromatics were confined to the early parts of the fuel jet, while the multicyclic PAHs spread more downstream. Increasing the percentage of air flowing through the dilution jets results in significant shortening of the flame and reduction in soot, irrespective of the hydrogen content, and results in a difference between single-ring aromatics and multicyclic PAH distributions, possibly due to the reduction of the residence time in rich mixtures. An additional case with helium instead of hydrogen helped to isolate chemical effects from aerodynamic effects. The results suggest that the chemical effects of hydrogen addition dominate over thermal effects. This study shows that hydrogen addition can control particulate emissions. The dataset enables validation of turbulent combustion models for soot.

Methylcyclohexane (MCH) stands out as a leading liquid organic hydrogen carrier (LOHC) due to its favorable hydrogen storage capacity and transportability. Despite its potential, advancing catalysts that combine high efficiency, cost-effectiveness, and durability for MCH dehydrogenation to produce hydrogen remains a critical challenge hindering large-scale industrial deployment. Herein, we report the synthesis of highly dispersed and stable bimetallic Pt-MoOx nanoparticles immobilized on gamma-Al2O3. The introduction of MoOx species significantly improves the stability of Pt and results in a high toluene (TOL) selectivity of 99.8 % with MCH conversion of 99.5% and a high hydrogen evolution rate of 470.5 mmolgPt-1min-1 at 340 degrees C. Moreover, the optimal catalyst exhibits a remarkable long-term stability, with no evident loss of activity in 140-h dehydrogenation reaction at a weight hourly space velocity of 11.7 h-1. Through detailed in-situ structure analyses, it was revealed that the introduction of subnanometer MoOx species facilitates the generation of ultrafine Pt nanoparticles with improved resistance to sintering, resulting in enhanced catalytic activity and durability of the noble metal. Furthermore, in-situ spectroscopic characterization demonstrates the positively charged Pt delta+ species promote the rapid desorption of TOL products. The excellent catalytic performance including high conversion and selectivity and superior stability offers great opportunities for their practical applications in LOHC technologies. (c) 2026, Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

Metal-organic polyhedra (MOPs) have exceptional potential for host-guest chemistry, but their discovery is hindered by the large combinatorial space of their building units. Computational screening offers a powerful workflow for efficiently screening large sets of MOPs; however, reliable results depend on accurate modelling of the geometries and properties of the MOPs. In this paper, we extend our previous workflow, which assembled computation-ready MOPs using purely geometric operations, by incorporating post-assembly computational modelling. We benchmark a range of methods, including machine learning interatomic potentials (MLIPs) and tight-binding DFT, against 85 experimentally resolved MOP structures. The results show that geometry optimisation significantly refines the initial assembled MOP structures in terms of cavity and pore properties when compared against experimental structures, with MLIPs found to achieve good accuracy and excellent convergence rates. We applied the most reliable method to the entire dataset, integrating the resulting data within The World Avatar through the OntoMOPs ontology and enabling natural language querying. Lastly, we demonstrate the utility of this refined dataset by screening for MOPs with the potential to act as hosts for a urea guest molecule.

This paper investigates how digital chemistry technologies such as machine learning, knowledge engineering, and laboratory automation are revolutionizing materials discovery for pressing global challenges in energy and healthcare. We introduce a comprehensive technology framework that integrates advanced databases, artificial intelligence models, semantic ontologies, and robotic systems to address fundamental challenges in chemical research. The World Avatar platform serves as a central case study, demonstrating its unique ability to connect computational design with experimental execution through dynamic and interoperable workflows. Practical applications in reticular chemistry and automated laboratory systems showcase the platform’s capacity to enable autonomous discovery processes. Together, these technological advances are driving chemical research toward more scalable, reproducible, and intelligent materials development approaches.

The discovery of functional reticular materials is dependent on optimising the organic building unit to achieve precise chemical and structural properties. However, experimentally characterised chemical building units (CBUs) are only a small fraction of the possible design space, and as such, computer-aided design based on datasets of known reticular materials have fundamental limitations in chemical diversity. Here, we present a general workflow for extracting, analysing, and recombining molecular fragments from large, chemically diverse datasets to systematically expand the accessible organic CBU design space. Using the ChEMBL36 database as a case study, we generate a library of 12 387 unique fragments through filtering, fragmentation, and ontology-driven classification. These fragments are recombined to enumerate a dataset of 44.8 million CBUs. To demonstrate integrating the workflow into designing functional reticular materials, we combine the library with a genetic algorithm to identify candidate metal-organic polyhedra (MOPs) carriers for the antibiotic ciprofloxacin, with the highest-ranked candidates predicted to have low toxicity and stable host-guest interactions according to tight-binding density functional theory calculations. The concepts and relationships between the molecular fragments, CBUs, templates, and MOPs are captured and connected through a knowledge graph and ontology integrated in The World Avatar.

This work presents a generalisable process that transforms unstructured synthesis descriptions of metal-organic polyhedra (MOPs) - a class of organometallic nanocages - into machine-readable, structured representations, integrating them into The World Avatar (TWA), a universal knowledge representation encompassing physical, abstract, and conceptual entities. TWA makes use of knowledge graphs and semantic agents. While previous work established rational design principles for MOPs in the context of TWA, experimental verification remains a bottleneck due to the lack of accessible and structured synthesis data. However, synthesis information in the literature is often sparse, ambiguous, and embedded with implicit knowledge, making direct translation into structured formats a significant challenge. To achieve this, a synthesis ontology was developed to standardise the representation of chemical synthesis procedures by building on existing standardisation efforts. We then designed an LLM-based pipeline with advanced prompt engineering strategies to automate data extraction and created workflows for seamless integration into a knowledge representation within TWA. Using this approach, we extracted and uploaded nearly 300 synthesis procedures, automatically linking reactants, chemical building units, and MOPs to related entities across interconnected knowledge graphs. Over 90% of publications were processed successfully through the fully automated pipeline without manual intervention. The demonstrated use cases show that this framework supports chemists in designing and executing experiments and enables data-driven retrosynthetic analysis, laying the groundwork for autonomous, knowledge-guided discovery in reticular chemistry.

The upgrading of energy-inefficient buildings is a critical part of the energy transition. Holistic analyses that foster informed and equitable policy interventions require interoperable data. We apply a principled approach that leverages The World Avatar to create a virtual knowledge graph underpinned by machine-understandable data representations. This approach provides a common terminology to integrate heterogeneous data sources to support multi-scale analysis of building energy retrofit options. We consider a case study in the UK based on the holistic analysis of household-level energy performance data, public health statistics and socio-economic metrics across geographic hierarchies. The analysis identifies regions with critical retrofit necessities, revealing disparities between these imperatives and extant policy levers. Granular retrofit targets are proposed to optimise resource allocation to the most vulnerable areas. Bespoke retrofit strategies are developed for 14.4 million households in the UK, providing actionable insights to support the targeted application of ‘fabric-first’ or ‘system-led’ retrofit pathways.

Decarbonizing the energy-intensive chemical industry has emerged as a pivotal challenge in recent years. This article underscores the urgent need for green and smart chemistry and explores decarbonization in chemical sector using a multi-scale smart systems engineering approach. By examining innovations across various scalesu2014from micro-level materials discovery to meso-level process optimization, and up to macro-level chemical industrial park design/redesignu2014this review illuminates how intelligence approaches can surrogate traditional mechanistic models and thus revolutionize efficiency, sustainability, and carbon neutrality of the chemical industry. Additionally, this review highlights the role of cross-scale modeling in addressing complex challenges in chemical processes through practical applications cases. Further key challenges are identified including data management, model interoperability, and industrial integration, alongside economic, social, and ethical considerations. Finally, it outlines future research directions, emphasizing interdisciplinary approaches to advance the industry toward a greener, more efficient, and carbon-neutral future, aligning with global sustainability objectives.

The adoption of heat pumps to displace the use of gas for domestic heating is a major component of the strategy to reduce emissions in the UK. This study examines the impact of adopting heat pumps on regional inequalities in the UK. An index is used to assess how variations in household fuel costs could affect regional disparities across different future price scenarios. The findings reveal that, at 2019 prices, most households would face higher heating costs with heat pumps. However, following the 2022 energy price shock, heat pump adoption would lead to lower heating costs for most households compared to gas heating. The effect is sensitive to the electricity-to-gas price ratio, with regions experiencing high fuel poverty being most vulnerable to negative impacts. By mapping these geospatial effects, the study enables the forecasting of future inequality trends, providing insights for informed policy development. The results suggest that, under appropriate price structures, heat pump adoption could contribute to both decarbonisation and reduced social inequality. An example mechanism for financial support to mitigate the impact of adopting heat pumps on inequality is demonstrated. This study highlights the novel capability of The World Avatar (TWA) approach to integrate cross-domain data sets, combining energy policy with social equity goals. By forecasting future inequality trends based on energy price scenarios, the study provides a route to valuable insights to support informed policy development, highlighting how the adoption of heat pumps can influence regional inequalities and emphasising the need for targeted interventions to support vulnerable regions.

The electrochemical reduction of CO₂ (CO₂RR) into valuable multi-carbon (C₂⁺) products offers a promising route for sustainable CO₂ conversion. However, many catalysts suffer from low Faradaic efficiency (FE) for C₂⁺ formation, limiting their practical application. In this study, we develop a scalable anodic oxidation method to fabricate Cu/Cu₂O heterostructured catalysts, where multifaceted Cu₂O nanoparticles form intimate interfaces with nanostructured Cu on a PTFE layer. This unique architecture significantly enhances CO₂RR selectivity, achieving a high C₂⁺ Faradaic efficiency of 58.1% and stable electrolysis for ~12 hours at 200 mA cm⁻² in a full-cell configuration with a NaOH electrolyte. Furthermore, we systematically investigate the effects of Cu film thickness and PTFE membrane pore size, along with the same key parameters for carbon paper as an alternative gas diffusion layer (GDL) in gas diffusion electrode (GDE) fabrication, to evaluate its stability and Faradaic efficiency compared to PTFE. Our findings provide valuable insights into how these factors influence product selectivity and catalyst stability. Structural and surface characterization (XRD, XPS, SEM) confirm the formation of stable Cu/Cu₂O interfaces, which enhance CO₂RR efficiency and catalyst durability.

Assembly modeling has been achieved in knowledge AI systems for the automated inference of new and rational metal-organic polyhedra (J. Am. Chem. Soc. 2022, 144, 26, 11713-11728). This work presents an algorithm and data structure that extends the process of assembly modeling to the automated generation of structural information about metal-organic polyhedra, enabling automation of computational approaches to analyse trends in cavity and pore sizing. Distinct from string-based tools for purely organic cages, the workflow positions inorganic, organic, and hybrid chemical building units directly in 3-D and outputs geometries suitable for higher-level geometry optimization calculations in one step. The structural geometries obtained from this work are semantically integrated as part of The World Avatar, a dynamic knowledge ecosystem.