This protocol is a comprehensive account of the intricate processes involved in the rational design, synthesis, and characterization of anisotropic metallic carbon materials. The materials were derived through the hydrolytic oxidation of graphene sheets, followed by self-assembly and mild annealing. The resulting products are highly percolated carbon networks that preserve the essential basal area of the source graphene. Structured into various sections, this document aims to furnish detailed insights crucial for supporting further investigations into these carbon materials. In particular, it highlights the key distinctions from conventional graphite/graphene oxidation protocols, offering a deeper understanding and ensuring the reproducibility of our seminal findings. We believe this differentiation is crucial to preventing the generalization of these materials from the outset, a limitation widely reported in the graphene oxide family and a major source of their inconsistencies, particularly in commercial products.

The availability and accessibility of economical renewable energy remains a key driving factor towards encouraging the uptake of clean energy. By incorporating economical hard carbon (HC), recycled from waste into anodes for lithium-ion batteries (LIBs), and treating the resulting HC-anode with a focused laser beam, the functionalised HC-anode exhibits enhanced electrochemical performance with a specific capacity of 516 mAh g- 1 at 0.1 A g- 1. It achieves over 100% capacity retention for 700 cycles at 1 A g- 1, and demonstrates super-long durability for 4000 cycles at 2 A g- 1. The improvements are attributed to laser-tunable expanded interlayer spacing and fluorescing defects in the engineered HC-anode. DFT calculations further established that these fluorescent defects correspond to carbon vacancies (cyan fluorescence), and their complexes with H heteroatoms (green fluorescence). These defects lead to the improved electrochemical performance via enhancing Li+ adsorption energies. Given such correlation, fluorescence studies are proposed as an interesting mechanism for guiding the development of carbon materials for energy applications, which serves as a highly efficient tool for assessing the electrochemical performance, eliminating the need for costly battery fabrication and testing processes. The performance achieved and its correlation to the observable fluorescence will not only contribute towards the effort of making cheaper batteries with better performance, but also serves as a rapid and scalable probe for preliminary evaluation of battery performance.

This dataset provides a comprehensive account of the intricate processes involved in the rational design, synthesis, and characterization of anisotropic metallic carbon materials. The materials were derived through the hydrolytic oxidation of graphene sheets, followed by meticulous self-assembly and mild annealing. The resulting products are highly percolated carbon networks, preserving the essential basal area of the source graphene. Structured into various sections, this dataset aims to furnish detailed insights crucial for supporting extensive investigations into these carbon materials. Section S1 delves into simulations that elucidate the reactivity of hydroxyl radicals in the hydrolytic oxidation process, pinpointing optimal conditions for their selective use in edge-hydrolysis of graphene. Additionally, it explores the molecular dynamics of edge-hydrolyzed graphene sheets, unraveling their self-assembly behavior and the formation of highly ordered films. Section S2 meticulously describes the source materials and optimal protocols, aligning with insights gained from simulations. In Section S3, the dataset explores the impact of synthesis protocols on the processability of hydrolyzed graphene and anticipates potential applications. Sections S4 to S7 present detailed characterization protocols, meticulously divided into morphology, composition, mechanical properties, and thermal/electronic transport, ensuring the inclusion of all essential details for reproducibility in core characterizations. Finally, Section S8 presents a table summarizing the general properties of the final annealed metallic carbon film (G0). This dataset thus serves as a valuable resource, providing a robust foundation for in-depth studies and fostering a comprehensive understanding of the multifaceted aspects of anisotropic metallic carbon materials.

Discovering new superionic materials is essential for advancing solid-state batteries, which offer improved energy density and safety compared to traditional lithium-ion batteries with liquid electrolytes. Conventional computational methods for identifying such materials are resource-intensive and not easily scalable. Recently, universal interatomic potential models have been developed using equivariant graph neural networks. These models are trained on extensive datasets of first-principles force and energy calculations. One can achieve significant computational advantages by leveraging them as the foundation for traditional methods of assessing the ionic conductivity, such as molecular dynamics or nudged elastic band techniques. However, the generalization error from model inference on diverse atomic structures arising in such calculations can compromise the reliability of the results. In this work, we propose an approach for the quick and reliable screening of ionic conductors through the analysis of a universal interatomic potential. Our method incorporates a set of heuristic structure descriptors that effectively employ the rich knowledge of the underlying model while requiring minimal generalization capabilities. Using our descriptors, we rank lithium-containing materials in the Materials Project database according to their expected ionic conductivity. Eight out of the ten highest-ranked materials are confirmed to be superionic at room temperature in first-principles calculations. Notably, our method achieves a speed-up factor of approximately 50 compared to molecular dynamics driven by a machine-learning potential, and it is at least 3000 times faster compared to first-principles molecular dynamics.

Kaolinite is a single 2D layer of kaolin or metakaolin (MK), common clays that can be characterized as layered 3D materials. We show that because of its chemical composition, kaolinite can be converted into an amorphous 3D material by chemical means. This dimensional transformation is possible due to the large surface to volume ratio and chemical reactivity of kaolinite. We investigate the formation and influence of quasi- or nanocrystalline phases in MK-based alkali-activated materials (AAM) that are related to the Si/Al ratio. We analyze the formation of an AAM from a MK precursor, which is a 3D bonded network that preserves the layered structure at the nanometer scale. We also exfoliate the remaining layered phase to examine the effects of the alkali-activation in the final sheet structures embedded within the amorphous network. The final material can be used as a cement with no carbon dioxide produced by the transformation reaction.

State-of-the-art delivery systems currently rely on chemical synthesis routes for drug encapsulation. However, these methods have inherent drawbacks concerning toxicity, selectivity, and the potential for burst release. To overcome these limitations, the enzymatic synthesis of polymer-drug conjugates emerges as a promising alternative to promote ecofriendliness and safety in production and grants precise control over the resulting structures. In this study, we delved into the reactivity of Pseudozyma (Candida) antarctica lipase B for polyesters’ capping with small molecules. For that purpose, Quantum Mechanics/Molecular Mechanics simulations were used to predict the conjugation reactions. These predictions were translated to the laboratory, where the enzymatic reactions were replicated, subsequently benchmarking them against metal-catalysed reactions. The outcome of our experiments was the successful generation of end-capped oligo/polycaprolactone, with their molecular weights spanning from 540 to 2600 g/mol with an enzymatic approach, and slightly higher when applying conventional catalysis, and with isolation yields up to 68.4%. This work underscores the potential of enzyme-driven strategies in fabricating precisely engineered drug delivery

The CRISPR/Cas9 system has emerged as a promising platform for gene editing; however, the lack of an efficient and safe delivery system to introduce it into cells continues to hinder clinical translation. Here, we report a rationally designed gene-editing nanoparticle (NP) formulation for brain applications: an sgRNA:Cas9 ribonucleoprotein complex is immobilized on the NP surface by oligonucleotides that are complementary to the sgRNA. Irradiation of the formulation with a near-infrared (NIR) laser generates heat in the NP, leading to the release of the ribonucleoprotein complex. The gene-editing potential of the formulation was demonstrated in vitro at the single-cell level. The safety and gene editing of the formulation were also demonstrated in the brains of reporter mice, specifically in the subventricular zone after intracerebral administration and in the olfactory bulb after intranasal administration. The formulation presented here offers a new strategy for the spatially controlled delivery of the CRISPR system to the brain.

We have investigated the lithium capacity of the 2H phase of niobium sulfide (NbS2) using density functional theory calculations and experiments. Theoretically, this material is found to allow the intercalation of a double layer of Li in between each NbS2 layer when in equilibrium with metal Li. The resulting specific capacity (340.8 mAh/g for the pristine material, 681.6 mAh/g for oxidized material) can reach more than double the specific capacity of graphite anodes. The presence of various defects leads to an even higher capacity with a partially reversible conversion of the material, indicating that the performance of the anodes is robust with respect to the presence of defects. Experiments in battery prototypes with NbS2-based anodes find a first specific capacity of about 1,130 mAh/g, exceeding the theoretical predictions.

Enzyme-substrate interactions are essential to both biological processes and industrial applications. Advanced machine learning techniques have significantly accelerated biocatalysis research, revolutionizing the prediction of biocatalytic activities and facilitating the discovery of novel biocatalysts. However, the limited availability of data for specific enzyme functions, such as conversion efficiency and stereoselectivity, presents challenges for prediction accuracy. In this study, we developed BioStructNet, a structure-based deep learning network that integrates both protein and ligand structural data to capture the complexity of enzyme-substrate interactions. Benchmarking studies with different algorithms showed the enhanced predictive accuracy of BioStructNet. To further optimize the prediction accuracy for the small data set, we implemented transfer learning in the framework, training a source model on a large data set and fine-tuning it on a small, function-specific data set, using the CalB data set as a case study. The model performance was validated by comparing the attention heat maps generated by the BioStructNet interaction module with the enzyme-substrate interactions revealed from molecular dynamics simulations of enzyme-substrate complexes. BioStructNet would accelerate the discovery of functional enzymes for industrial use, particularly in cases where the training data sets for machine learning are small.

This dataset provides a comprehensive account of the intricate processes involved in the rational design, synthesis, and characterization of anisotropic metallic carbon materials. The materials were derived through the hydrolytic oxidation of graphene sheets, followed by meticulous self-assembly and mild annealing. The resulting products are highly percolated carbon networks, preserving the essential basal area of the source graphene. Structured into various sections, this dataset aims to furnish detailed insights crucial for supporting extensive investigations into these carbon materials. Section S1 delves into simulations that elucidate the reactivity of hydroxyl radicals in the hydrolytic oxidation process, pinpointing optimal conditions for their selective use in edge-hydrolysis of graphene. Additionally, it explores the molecular dynamics of edge-hydrolyzed graphene sheets, unraveling their self-assembly behavior and the formation of highly ordered films. Section S2 meticulously describes the source materials and optimal protocols, aligning with insights gained from simulations. In Section S3, the dataset explores the impact of synthesis protocols on the processability of hydrolyzed graphene and anticipates potential applications. Sections S4 to S7 present detailed characterization protocols, meticulously divided into morphology, composition, mechanical properties, and thermal/electronic transport, ensuring the inclusion of all essential details for reproducibility in core characterizations. Finally, Section S8 presents a table summarizing the general properties of the final annealed metallic carbon film (G0). This dataset thus serves as a valuable resource, providing a robust foundation for in-depth studies and fostering a comprehensive understanding of the multifaceted aspects of anisotropic metallic carbon materials.

Two new monooxygenase biocatalysts, the Baeyer-Villiger monooxygenase BVMO145 and the flavin monooxygenase FMO401 from Almac library, have been found to catalyse the enantiodivergent oxidation of sulfides bearing N-heterocyclic substituents into sulfoxides under mild and green conditions. The biocatalyst BVMO145 provides (S)-sulfoxides while the flavin monooxygenase FMO401 affords (R)-sulfoxides with good conversions and high ee. Two new monooxygenase biocatalysts, BVMO145 and FMO401 from Almac library, catalyse the enantiodivergent oxidation of sulfides bearing N-heterocyclic substituents into sulfoxides under mild and green conditions.

Harnessing the phenomena of quantum coherence and destructive interference, we have successfully engineered and synthesized a three-dimensional (3D) graphene-based film exhibiting remarkable properties, including metallic thermal conductivity (kappa approximate to 150 Wm-1K-1) and electrical conductivity (sigma approximate to 320 kSm-1) at room temperature. Notably, these films demonstrate colossal transport anisotropies, reaching approximately 103 for thermal and 105 for electrical conductivity. This places them among the conducting materials with the highest anisotropies known to date, surpassing even the performance of one-dimensional (1D) carbon nanotubes and two-dimensional (2D) materials like h-BN and MoS2. These films are synthesized by self-assembly and crosslinking of edge-hydrolyzed graphene flakes. The electron transport between flakes is phonon mediated and at low temperatures the films present quantum critical behavior of a metal to Anderson insulator transition. We measure the electron transport properties in a Hall bar geometry and extract the critical exponents as a function of the sample mobility.

Benzylisoquinoline alkaloids are valuable active ingredients in medicines that are typically extracted from plants and subsequently derivatized. Buprenorphine is a member of this family of compounds and is an effective analgesic and is also used for the treatment of opioid use disorder. The commercial route of synthesis for buprenorphine starts from thebaine and uses toxic reagents and harsh reaction conditions for the N- and O-demethylation steps. Here, we propose an alternative approach for buprenorphine synthesis via enzymatic N- and O-demethylation reactions. Utilizing rational enzyme design and directed evolution, we identified and engineered two oxygenase enzymes. For the N-demethylation reaction, the best variant achieved a cumulative improvement in conversion of 567-fold, while for the O-demethylation, the best variant achieved 22-fold cumulative improvement in conversion. A separate variant was able to efficiently catalyze both the N-demethylation and the O-demethylation reactions.

van der Waals heterojunctions based on transition-metal dichalcogenides (TMDs) offer advanced strategies for manipulating light-emitting and light-harvesting behaviors. A crucial factor determining the light-material interaction is in the band alignment at the heterojunction interface, particularly the distinctions between type-I and type-II alignments. However, altering the band alignment from one type to another without changing the constituent materials is exceptionally difficult. Here, utilizing Bi2O2Se with a thickness-dependent band gap as a bottom layer, we present an innovative strategy for engineering interfacial band configurations in WS2/Bi2O2Se heterojunctions. In particular, we achieve tuning of the band alignment from type-I (Bi2O2Se straddling WS2) to type-II and finally to type-I (WS2 straddling Bi2O2Se) by increasing the thickness of the Bi2O2Se bottom layer from monolayer to multilayer. We verified this band architecture conversion using steady-state and transient spectroscopy as well as density functional theory calculations. Using this material combination, we further design a sophisticated band architecture incorporating both type-I (WS2 straddles Bi2O2Se, fluorescence-quenched) and type-I (Bi2SeO5 straddles WS2, fluorescence-recovered) alignments in one sample through focused laser beam (FLB). By programming the FLB trajectory, we achieve a predesigned localized fluorescence micropattern on WS2 without changing its intrinsic atomic structure. This effective band architecture design strategy represents a significant leap forward in harnessing the potential of TMD heterojunctions for multifunctional photonic applications.

Women in developing countries still face enormous challenges when accessing reproductive health care. Access to voluntary family planning empowers women allowing them to complete their education and join the paid workforce. This effectively helps to end poverty, hunger and promotes good health for all. According to the United Nations (UN) organization, in 2022, an estimated 257 million women still lacked access to safe and effective family planning methods globally. One of the main barriers is the associated cost of modern contraceptive methods. Funded by the Bill & Melinda Gates Foundation, Almac Group worked on the development of a novel biocatalytic route to etonogestrel and levonorgestrel, two modern contraceptive APIs, with the goal of substantially decreasing the cost of production and so enabling their use in developing nations. This present work combines the selection and engineering of a carbonyl reductase (CRED) enzyme from Almac's selectAZyme™ panel, with process development, to enable efficient and economically viable bioreduction of ethyl secodione to (13R,17S)-secol, the key chirality introducing intermediate en route to etonogestrel and levonorgestrel API. CRED library screening returned a good hit with an Almac CRED from Bacillus weidmannii, which allowed for highly stereoselective bioreduction at low enzyme loading of less than 1% w/w under screening assay conditions. However, the only co-solvent tolerated was DMSO up to ∼30% v/v, and it was impossible to achieve reaction completion with any enzyme loading at substrate titres of 20 g L-1 and above, due to the insolubility of the secodione. This triggered a rapid enzyme engineering program fully based on computational mutant selection. A small panel of 93 CRED mutants was rationally designed to increase the catalytic activity as well as thermal and solvent stability. The best mutant, Mutant-75, enabled a reaction at 45 °C to go to completion at 90 g L-1 substrate titre in a buffer/DMSO/heptane reaction medium fed over 6 h with substrate DMSO stock solution, with a low enzyme loading of 3.5% w/w wrt substrate. In screening assay conditions, Mutant-75 also showed a 2.2-fold activity increase. Our paper shows which computations and rational decisions enabled this outcome.

2D materials are solid microscopic flakes with a-few-Angstrom thickness possessing some of the largest surface-to-volume ratios known. Altering their conformation state from a flat flake to a scroll or fiber offers a synergistic association of properties arising from 2D and 1D nanomaterials. However, a combination of the long-range electrostatic and short-range solvation forces produces an interlayer repulsion that has to be overcome, making scrolling 2D materials without disrupting the pristine structure a challenging task. Herein, a facile method is presented to alter the 2D materials' inter-layer interactions by confining organic salts onto their basal area, forming 2D-confined electrolytes. The confined electrolytes produce local charge inhomogeneities, which can conjugate across the interlayer gap, binding the two surfaces. This allows the 2D-confined electrolytes to behave as polyelectrolytes within a higher dimensional order (2D -> 1D) and form robust nanofibers with distinct electronic properties. The method is not material-specific and the resulting fibers are tightly bound even though the crystal structure of the basal plane remains unaltered.

Enzymatic peptide synthesis is a powerful alternative to solid-phase methods, as enzymes can have high regio- and stereoselectivity and high yield and require mild reaction conditions. This is beneficial in formulation research due to the rise of nucleic acid therapies. Peptide nucleic acids (PNAs) have a high affinity toward DNA and RNA, and their solubility and cellular delivery can be improved via conjugation to peptides. Here, we designed and assessed the viability of the papain enzyme to conjugate four PNA-peptide models in water and an organic solvent using QM/MM metadynamics. We found that the reactions in water yield better results, where three conjugates could potentially be synthesized by the enzyme, with the first transition state as the rate-limiting step, with an associated energy of 14.53 kcal mol(-1), although with a slight endergonic profile. The results highlight the importance of considering the enzyme pockets and different substrate acceptivities and contribute to developing greener, direct, and precise synthetic routes for nucleic acid-based therapies. By exploring the enzyme's potential in conjunction with chemical synthesis, current protocols can be simplified for the synthesis of longer nucleic acids and peptide sequences (and, by extension, proteins) from smaller oligo or peptide blocks.

Alkali-metal beta/beta '' aluminas are among the fastest ionic conductors, yet little is understood about the role of defects in the ion transport mechanism. Here, we use density functional theory (DFT) to investigate the crystal structures of the beta and beta '' phases and their vacancy and interstitial defects. We find that charge transport is likely to be dominated by alkali-metal interstitials in beta aluminas and by vacancies in beta '' aluminas. Lower bounds for the activation energy for diffusion are found by determining the minimum-energy paths for defect migration. The resulting migration barriers are lower than the experimental activation energies for conduction in Na beta and beta '' aluminas, suggesting a latent potential for optimization. The lowest activation energy of about 20 meV is predicted for correlated vacancy migration in K beta '' alumina.