Kourosh Darvish, Arjun Sohal, Abhijoy Mandal
et al.
Accelerated materials discovery is critical for addressing global challenges. However, developing new laboratory workflows relies heavily on real-world experimental trials, and this can hinder scalability because of the need for numerous physical make-and-test iterations. Here we present MATTERIX, a multiscale, graphics processing unit-accelerated robotic simulation framework designed to create high-fidelity digital twins of chemistry laboratories, thus accelerating workflow development. This multiscale digital twin simulates robotic physical manipulation, powder and liquid dynamics, device functionalities, heat transfer and basic chemical reaction kinetics. This is enabled by integrating realistic physics simulation and photorealistic rendering with a modular graphics processing unit-accelerated semantics engine, which models logical states and continuous behaviors to simulate chemistry workflows across different levels of abstraction. MATTERIX streamlines the creation of digital twin environments through open-source asset libraries and interfaces, while enabling flexible workflow design via hierarchical plan definition and a modular skill library that incorporates learning-based methods. Our approach demonstrates sim-to-real transfer in robotic chemistry setups, reducing reliance on costly real-world experiments and enabling the testing of hypothetical automated workflows in silico. The project website is available at https://accelerationconsortium.github.io/Matterix/ .
This study presents a simple method for producing chemically crosslinked porous materials from lignocellulosic fibers with different particle sizes and lignin contents. Porous materials were prepared from organosolv pulp (OP), kneaded organosolv pulp (KOP), lignin-rich microfibrillated cellulose (LMFC), and enzyme cellulose nanofiber (ECNF) and were crosslinked using epichlorohydrin, glutaraldehyde, and glycerol diglycidyl ether (GDE). Among the crosslinkers, GDE provided the best dimensional stability and elastic recovery after repeated compression–recovery cycles in water. Notably, KOP-based porous materials outperformed those derived from LMFC and ECNF, despite being produced via a simple kneading process without energy-intensive fibrillation. KOP-derived materials exhibited excellent dimensional stability and high water absorption exceeding 5890%, demonstrating strong potential for bio-based absorbent applications such as hygiene and packaging.
In recent years, mechanochemistry has become an innovative and sustainable alternative to traditional solvent-based synthesis. Mechanochemistry rapidly expanded across a wide range of chemistry fields, including diverse organic compounds and active pharmaceutical ingredients, coordination compounds, organometallic complexes, main group frameworks, and technologically relevant materials. This Review aims to highlight recent advancements and accomplishments in mechanochemistry, underscoring its potential as a viable and eco-friendly alternative to conventional solution-based methods in the field of synthetic chemistry.
Over the years, scientists have identified various synthetic “handles” while developing wet chemical protocols for achieving a high level of shape and compositional complexity in colloidal nanomaterials. Halide ions have emerged as one such handle which serve as important surface active species that regulate nanocrystal (NC) growth and concomitant physicochemical properties. Halide ions affect the NC growth kinetics through several means, including selective binding on crystal facets, complexation with the precursors, and oxidative etching. On the other hand, their presence on the surfaces of semiconducting NCs stimulates interesting changes in the intrinsic electronic structure and interparticle communication in the NC solids eventually assembled from them. Then again, halide ions also induce optoelectronic tunability in NCs where they form part of the core, through sheer composition variation. In this review, we describe these roles of halide ions in the growth of nanostructures and the physical changes introduced by them and thereafter demonstrate the commonality of these effects across different classes of nanomaterials.
Efficient chemical kinetic model inference and application in combustion are challenging due to large ODE systems and widely separated time scales. Machine learning techniques have been proposed to streamline these models, though strong nonlinearity and numerical stiffness combined with noisy data sources make their application challenging. Here, we introduce ChemKANs, a novel neural network framework with applications both in model inference and simulation acceleration for combustion chemistry. ChemKAN's novel structure augments the generic Kolmogorov Arnold Network Ordinary Differential Equations (KAN-ODEs) with knowledge of the information flow through the relevant kinetic and thermodynamic laws. This chemistry-specific structure combined with the expressivity and rapid neural scaling of the underlying KAN-ODE algorithm instills in ChemKANs a strong inductive bias, streamlined training, and higher accuracy predictions compared to standard benchmarks, while facilitating parameter sparsity through shared information across all inputs and outputs. In a model inference investigation, we benchmark the robustness of ChemKANs to sparse data containing up to 15% added noise, and superfluously large network parameterizations. We find that ChemKANs exhibit no overfitting or model degradation in any of these training cases, demonstrating significant resilience to common deep learning failure modes. Next, we find that a remarkably parameter-lean ChemKAN (344 parameters) can accurately represent hydrogen combustion chemistry, providing a 2x acceleration over the detailed chemistry in a solver that is generalizable to larger-scale turbulent flow simulations. These demonstrations indicate the potential for ChemKANs as robust, expressive, and efficient tools for model inference and simulation acceleration for combustion physics and chemical kinetics.
Retrosynthesis strategically plans the synthesis of a chemical target compound from simpler, readily available precursor compounds. This process is critical for synthesizing novel inorganic materials, yet traditional methods in inorganic chemistry continue to rely on trial-and-error experimentation. Emerging machine-learning approaches struggle to generalize to entirely new reactions due to their reliance on known precursors, as they frame retrosynthesis as a multi-label classification task. To address these limitations, we propose Retro-Rank-In, a novel framework that reformulates the retrosynthesis problem by embedding target and precursor materials into a shared latent space and learning a pairwise ranker on a bipartite graph of inorganic compounds. We evaluate Retro-Rank-In's generalizability on challenging retrosynthesis dataset splits designed to mitigate data duplicates and overlaps. For instance, for Cr2AlB2, it correctly predicts the verified precursor pair CrB + Al despite never seeing them in training, a capability absent in prior work. Extensive experiments show that Retro-Rank-In sets a new state-of-the-art, particularly in out-of-distribution generalization and candidate set ranking, offering a powerful tool for accelerating inorganic material synthesis.
Owusu-Gyimah Victor, Phanuel Yao Klogo, Francis Gbefo
A study in southwestern Ghana compared the effects of organic and inorganic additives on P availability and related factors in two acidic soils, Ankasa and Abenia. Different amounts of P as KH2PO4 were applied: 0.067 g kg-1 for Abenia and 0.041 g kg-1 for Ankasa. Soil samples were treated with cow dung, Chromolaena odorata, and poultry droppings for six weeks to increase standard P requirement and neutralize exchangeable Aluminum. Data analysis was performed using GenStat (version 14). An analysis of variance (ANOVA) was conducted for the soil amendments, followed by Tukey’s comparison test at a 5% significance level to identify significant differences among the soil amendments. The result showed that higher rates of organic amendments significantly increased pH, available P (Bray 1 and NaHCO3-P), NaOH-P, and reduced exchangeable Al concentration. Poultry droppings and cow dung impact notably improved soil quality. At the same time, CaCO3 had similar effects on soil pH. However, it did not significantly affect P availability or NaOH-extractable P. CaSO4 and CaCO3 had minimal impact on phosphorus distribution, suggesting that altering pH or exchangeable Al does not necessarily change P fractions. Poultry droppings, rich in P, could be a potential alternative to lime in enhancing P availability and reducing soil acidity.
Agata Kryczyk-Poprawa, Wojciech Baran, Katarzyna Sułkowska-Ziaja
et al.
Oxybenzone, a common sunscreen ingredient, has been widely detected in various environmental matrices, posing significant ecological and health risks. The present study demonstrates, for the first time, the capacity of <i>Pleurotus djamor</i> to degrade oxybenzone in in vitro cultures. After 14 days of mycelial incubation, oxybenzone concentrations in the medium decreased from 25 mg to 1.5394 ± 0.095 mg. The final amount of oxybenzone in the mycelium after lyophilization was 6.2067 ± 0.2459 mg. Furthermore, oxybenzone addition significantly reduced biomass growth from 2.510 ± 0.6230 g to 1.4697 ± 0.0465 g. The transformation products in the dry mycelium and medium were assessed and identified using UPLC-Q-tof based on monoisotopic molecular mass and fragmentation spectra. In processes initiated by <i>P. djamor</i>, mainly acylated derivatives of oxybenzone were formed. Additionally, compounds with thiol and amino groups were identified. Alterations in antioxidant profiles (L-tryptophan, 6-methyl-D,L-tryptophan, p-hydroxybenzoic acid, ergosterol, lovastatin, L-phenylalanine, and ergothioneine) in response to oxybenzone exposure were observed. Our findings reveal significant changes in the antioxidant levels and biomass growth inhibition, underscoring the potential toxicological risks associated with oxybenzone. The observed reduction in oxybenzone concentration highlights the potential of <i>P. djamor</i> as an effective and environmentally friendly strategy for mitigating this pollutant.
Christopher J. Bartel, Samantha L. Millican, Ann M. Deml
et al.
The Gibbs energy, G, determines the equilibrium conditions of chemical reactions and materials stability. Despite this fundamental and ubiquitous role, G has been tabulated for only a small fraction of known inorganic compounds, impeding a comprehensive perspective on the effects of temperature and composition on materials stability and synthesizability. Here, we use the SISSO (sure independence screening and sparsifying operator) approach to identify a simple and accurate descriptor to predict G for stoichiometric inorganic compounds with ~50 meV atom−1 (~1 kcal mol−1) resolution, and with minimal computational cost, for temperatures ranging from 300–1800 K. We then apply this descriptor to ~30,000 known materials curated from the Inorganic Crystal Structure Database (ICSD). Using the resulting predicted thermochemical data, we generate thousands of temperature-dependent phase diagrams to provide insights into the effects of temperature and composition on materials synthesizability and stability and to establish the temperature-dependent scale of metastability for inorganic compounds. Materials databases currently neglect the temperature effect on compound thermodynamics. Here the authors introduce a Gibbs energy descriptor enabling the high-throughput prediction of temperature-dependent thermodynamics across a wide range of compositions and temperatures for inorganic solids.
Inorganic/inorganic composites are found in multiple applications crucial for the energy transition, from nuclear reactor to energy storage devices. Their microstructures dictate a number of properties, such as mass transport or fracture resistance. There has been a multitude of process developed to control the microstructure of inorganic/inorganic composites, from powder mixing and the use of short or long fibre, to tape casting for laminates up to recently 3D printing. Here, we combined emulsions and slip casting into a simpler, broadly available, inexpensive processing platform that allow for in situ control of a composite's microstructure that also enables complex shaping. Emulsions are used to form droplets of controllable size of one inorganic phase into another, while slip casting enable 3D shaping of the final part. Our study shows that slip casting emulsions trigger a two-steps solvent removal that opens the possibility for conformal coating of porosity. By making magnetically responsive droplets, we form inorganic fibre inside an inorganic matrix during slip casting, demonstrating a simple fabrication for long-fibre reinforced composites. We exemplify the potential of this processing platform by making strong and lightweight alumina scaffolds reinforced by a confirmed zirconia coating and alumina with metallic iron fibres that displays work of fracture an order of magnitude higher than alumina.
Modelling the inorganic exciton contribution to 2D hybrid organic-inorganic perovskites is essential to understand their properties and screen for new materials. Here, we combine hybrid density functional theory calculations including spin orbit coupling (SOC) with the experimental relationship between the inorganic band gap and exciton binding energy to predict the inorganic exciton energy. For this purpose, we determine a universal exchange parameter for the HSE06 hybrid functional with SOC for lead-based 2D hybrid organic-inorganic perovskites. We further identify a relationship that connects PBE calculations to experiment-quality optical gaps and allows us to generalize the exchange mixing parameter other SOC approximations. Our approach opens the path to screen for 2D hybrid organic-inorganic perovskites with optimized spectra, e.g., for new solar cell or light emitting materials.
Martin G. Zhen, Kathleen L. May, Robert A. Gossage
The synthesis and characterisation (UV-Vis, IR, HRESI-MS, <sup>1</sup>H and <sup>13</sup>C NMR spectroscopies, electrochemistry) is reported of the novel title material (<b>1</b>: alternatively named <i>rac</i>-1-(2′-benzothiazolyl)-2-ferrocenyl-2-propanol): a rare example of a ferrocenyl-benzothiazole hybrid species. Compound <b>1</b> is produced by the low temperature reaction of acetylferrocene (<b>3</b>) with a solution of the methyl anion derived via the deprotonation of 2-methyl-1,3-benzothiazole. The yield of <b>1</b> is moderate (34%) after purification and is an air and thermally stable solid under ambient conditions. Attempts to sublime <b>1</b>, however, result in decomposition with one of the products being identified (NMR) as <b>3</b>. The spectroscopic features of <b>1</b> are presented. Attempts to obtain suitable crystalline material of <b>1</b> for a single crystal X-ray diffraction study were unfortunately unsuccessful. Compound <b>1</b> also does not form stable coordination complexes with various metal salts (e.g., Ni[2+], Co[2+], etc.) under the conditions tested.
Glycosylation is considered as a critical quality attribute for monoclonal antibodies (mAbs) and needs routine monitoring during production. This study aims to compare the glycoform profiles of biosimilar and four originator mAbs using ultra-performance liquid chromatography (UPLC) coupled to electrospray ionization-quadrupole time of flight-mass spectrometry (ESI/Q-TOF MS). The resultant mass spectrum showed that seven different glycoform pairs, including G0F–GN/G0, G0F–GN/G0F, G0F/G0F, G0F/G1F, G1F/G1F, G1F/G2F, and G2F/G2F were identified via intact mass analysis for all tested mAb samples. The correct identification of each glycoform pair was achieved by comparing the observed mass with its theoretical mass using high-resolution mass spectrometry data (with mass accuracies of less than 100 ppm). The most abundant paired glycoforms detected at the intact protein level are G0F/G0F and G0F/G1F, with relative abundance ranges of 38.45 – 43.43% and 19.32 – 22.20%, respectively. The obtained data demonstrated that biosimilar and originators have the same types of glycoform pairs, and the relative abundances of each pair were comparable among biosimilar and four originator mAb samples. Additionally, the reduced mass analysis revealed that five different glycans (G0F–GN, G0, G0F, G1F, and G2F) were attached to the heavy chain of the mAb, and the relative abundance of G0F ranged from 75.21 to 77.90%. The detected mass accuracies for reduced mass analysis were below 25 ppm. The results of the intact and reduced mass analyses showed that the biosimilar is similar to its originator in terms of glycoform percentages and molecular masses.
Agnieszka Ziółkiewicz, Kamila Kasprzak-Drozd, Agnieszka Wójtowicz
et al.
The phenol content of sorghum is a unique feature among all cereal grains; hence this fact merits the special attention of scientists. It should be remembered that before polyphenols can be used in the body, they are modified within the digestive tract. In order to obtain more accurate data on the level and activity of tested ingredients after ingestion and digestion in the in vivo digestive tract, in vitro simulated digestion may be used. Thus, the aim of this study was to determine the content of polyphenols, flavonoids, and individual phenolic acids, as well as the antiradical properties, of sorghum and sorghum-enriched pasta before and after in vitro simulated gastrointestinal digestion. We observed that the total content of polyphenols decreased after gastric digestion of sorghum, and slightly increased after duodenal digestion. Moreover, the flavonoid content decreased after the first stage of digestion, while antioxidant properties increased after the first stage of digestion and slightly decreased after the second stage. The digestion of polyphenolics in sorghum is completely different to that in pasta—both in varieties with, and without, the addition of sorghum. For pasta, the content of total polyphenols and flavonoids, and free radical scavenging properties, decrease after each stage of digestion.