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Abstract Precisely managing electron transfer pathways throughout the catalytic reaction is paramount for bolstering both the efficacy and endurance of catalysts, offering a pivotal solution to addressing concerns surrounding host structure destabilization and cycling life degradation. This paper describes the integration of B-Ni dual single-atoms within MnO2 channels to serve as an electronic reservoir to direct the electron transfer route during methane catalytic combustion. Comprehensive analysis discovers that B atoms weaken the interaction between O and Mn atoms by forming bonds with lattice oxygen atoms. Meanwhile, Ni atoms facilitate electron transfer to achieve the heightened activity of MnO2. The B-Ni dual-sites instead of Mn (IV) could accommodate excess electrons generated during the reaction to inhibit the formation of high spin Mn (III) species, thereby hindering the Jahn-Teller distortion and maintaining the catalyst stability. This work demonstrates an effective modification strategy to substantially prolong the service life of MnO2-based materials.

A recent paper found that the sex chromosomes of the crested ibis have more gametolog pairs than many other birds. This Comment discusses that this finding suggets that WZ recombination stopped independently in the sex chromosomes in different bird lineages.

Abstract The Sc2.0 global consortium to design and construct a synthetic genome based on the Saccharomyces cerevisiae genome commenced in 2006, comprising 16 synthetic chromosomes and a new-to-nature tRNA neochromosome. In this paper we describe assembly and debugging of the 902,994-bp synthetic Saccharomyces cerevisiae chromosome synXVI of the Sc2.0 project. Application of the CRISPR D-BUGS protocol identified defective loci, which were modified to improve sporulation and recover wild-type like growth when grown on glycerol as a sole carbon source when grown at 37˚C. LoxPsym sites inserted downstream of dubious open reading frames impacted the 5’ UTR of genes required for optimal growth and were identified as a systematic cause of defective growth. Based on lessons learned from analysis of Sc2.0 defects and synXVI, an in-silico redesign of the synXVI chromosome was performed, which can be used as a blueprint for future synthetic yeast genome designs. The in-silico redesign of synXVI includes reduced PCR tag frequency, modified chunk and megachunk termini, and adjustments to allocation of loxPsym sites and TAA stop codons to dubious ORFs. This redesign provides a roadmap into applications of Sc2.0 strategies in non-yeast organisms.

Abstract Inferring appropriate synthesis reaction (i.e., retrosynthesis) routes for newly designed molecules is vital. Recently, computational methods have produced promising single-step retrosynthesis predictions. However, template-based methods are limited by the known synthesis templates; template-free methods are weakly interpretable; and semi template-based methods are deficient with regard to utilizing the associations between chemical entities. To address these issues, this paper leverages the intra-associations between synthons, the inter-associations between synthons and leaving groups (LGs), and the intra-associations between LGs. It develops a multitask graph representation learning model for single-step retrosynthesis prediction (Retro-MTGR) to solve reaction centre deduction and LG identification simultaneously. A comparison with 16 state-of-the-art methods first demonstrates the superiority of Retro-MTGR. Then, its robustness and scalability and the contributions of its crucial components are validated. More importantly, it can determine whether a bond can be a reaction centre and what LGs are appropriate for a given synthon, respectively. The answers reflect underlying chemical synthesis rules, especially opposite electrical properties between chemical entities (e.g., reaction sites, synthons, and LGs). Finally, case studies demonstrate that the retrosynthesis routes inferred by Retro-MTGR are promising for single-step synthesis reactions. The code and data of this study are freely available at https://doi.org/10.5281/zenodo.14346324 .

Abstract Two-dimensional (2D) van der Waals heterostructures consist of different 2D crystals with diverse properties, constituting the cornerstone of the new generation of 2D electronic devices. Yet interfaces in heterostructures inevitably break bulk symmetry and structural continuity, resulting in delicate atomic rearrangements and novel electronic structures. In this paper, we predict that 2D interfaces undergo “spontaneous curvature”, which means when two flat 2D layers approach each other, they inevitably experience out-of-plane curvature. Based on deep-learning-assisted large-scale molecular dynamics simulations, we observe significant out-of-plane displacements up to 3.8 Å in graphene/BN bilayers induced by curvature, producing a stable hexagonal moiré pattern, which agrees well with experimentally observations. Additionally, the out-of-plane flexibility of 2D crystals enables the propagation of curvature throughout the system, thereby influencing the mechanical properties of the heterostructure. These findings offer fundamental insights into the atomic structure in 2D van der Waals heterostructures and pave the way for their applications in devices.

Abstract System-level wearable electronics require to be flexible to ensure conformal contact with the skin, but they also need to integrate rigid and bulky functional components to achieve system-level functionality. As one of integration methods, folding integration offers simplified processing and enhanced functionality through rigid-soft region separation, but so far, it has mainly been applied to modality of electrical sensing and stimulation. This paper introduces a vialess heterogeneous skin patch with multi modalities that separates the soft region and strain-robust region through folded structure. Our system includes electrical and optical modalities for hemodynamic and cardiovascular monitoring, and a force-electrically driven micropump for drug delivery. Each modality is demonstrated through on-demand drug delivery, flexible waveguide-based PPG monitoring, and ECG and body movement monitoring. Wireless data transmission and real-time measurement validate the feedback operation for multi-modalities. This engineered closed-loop platform offers the possibility for broad applications, including cardiovascular monitoring and chronic disease management.

Abstract Thin-film composite polyamide (TFC PA) membranes hold promise for energy-efficient liquid separation, but achieving high permeance and precise separation membrane via a facile approach that is compatible with present manufacturing line remains a great challenge. Herein, we demonstrate the use of lignin alkali (LA) derived from waste of paper pulp as an aqueous phase additive to regulate interfacial polymerization (IP) process for achieving high performance nanofiltration (NF) membrane. Various characterizations and molecular dynamics simulations revealed that LA can promote the diffusion and partition of aqueous phase monomer piperazine (PIP) molecules into organic phase and their uniform dispersion on substrate, accelerating the IP reaction and promoting greater interfacial instabilities, thus endowing formation of TFC NF membrane with an ultrathin, highly cross-linked, and crumpled PA layer. The optimal membrane exhibited a remarkable water permeance of 26.0 L m-2 h-1 bar-1 and Cl-/SO4 2- selectivity of 191.0, which is superior to the state-of-the-art PA NF membranes. This study provides a cost-effective scalable strategy for fabricating ultra-selective and highly permeable NF membrane for precise ion-ion separation and small organic compounds removal.

Abstract Unsymmetric disulfides are prevalent in natural products and are essential in medicinal chemistry and materials science, but their robust synthesis poses significant challenges. In this paper, we report an expeditous transition-metal-free methodology for synthesizing unsymmetric disulfides through the addition of perthiyl radicals to alkenes. This study marks the use of generating perthiyl radicals by reacting SO2 with unactivated alkyl (pseudo)halides (Cl/Br/I/OTs). Various primary, secondary and tertiary alkyl (pseudo)halides substituted with different functional groups successfully function as suitable reactants. The formation of perthiyl radicals and their involvement in the reaction process are verified through mechanistic studies and DFT calculations. Overall, this method leverages readily available alkyl electrophiles and alkenes alongside SO2 in a single reaction setup to efficiently form both carbon-sulfur and sulfur-sulfur bonds simultaneously.

Abstract Hydrogen is a promising clean energy source with geological reserves widely distributed globally, offering an annual flow exceeding 23 trillion grams. However, natural hydrogen extraction wells face unique safety challenges compared to conventional oil and gas wells. This paper reviews well safety concerns such as tubing/casing damage, cement/sealant failure, and excessive annular pressure buildup. Key issues include hydrogen embrittlement, microbiological corrosion, H2-cement reaction, and H2-rubber degradation, which can lead to mechanical failures. The review explores potential solutions like metal coatings, rubber fillers, and cement additives to mitigate these problems. It also emphasizes the need for further research to validate these solutions under real-world conditions. Addressing these challenges is crucial for the safe and efficient extraction of natural hydrogen.

Abstract With the development of nanotechnology, nano-functional units of different dimensions, morphologies, and sizes exhibit the potential for efficient microwave absorption (MA) performance. However, the multi-unit coupling enhancement mechanism triggered by the alignment and orientation of nano-functional units has been neglected, hindering the further development of microwave absorbing materials (MAMs). In this paper, two typical ZIF-derived nanomaterials are self-assembled into two-dimensional ordered polyhedral superstructures by the simple ice template method. The nano-functional units exhibit distinctive dielectric-sensitive behaviors after self-assembling into two-dimensional ordered arrays. The modified 2D ordered polyhedral superstructures not only inherit the atomic-level doping and well-designed shell structure, but also further amplify the loss properties to realize the multi-scale modulated MA response. Satisfactory MA performance in C, X and Ku bands is finally achieved. In particular, the ultra-broadband microwave absorption bandwidth (EAB) of 6.41 GHz is realized at 1.82 mm thickness. Our work demonstrates the two-dimensional ordered array-induced multiscale polarization behavior, providing a direction to fully utilize the potential of wave-absorbing functional units.

Abstract Ultraviolet (UV) detection is extensively used in a variety of applications. However, the storage and processing of information after detection require multiple components, resulting in increased energy consumption and data transmission latency. In this paper, a reconfigurable UV photodetector based on CeO2/SrTiO3 heterostructures is demonstrated with in-sensor computing capabilities achieved through interface engineering. We show that the non-volatile storage capability of the device could be significantly improved by the introduction of an oxygen reservoir. A photodetector array operated as a single-layer neural network was constructed, in which edge detection and pattern recognition were realized without the need for external memory and computing units. The location and classification of corona discharges in real-world environments were also simulated and achieved an accuracy of 100%. The approach proposed here offers promising avenues and material options for creating non-volatile smart photodetectors.

Abstract Reliable molecular property prediction is essential for various scientific endeavors and industrial applications, such as drug discovery. However, the data scarcity, combined with the highly non-linear causal relationships between physicochemical and biological properties and conventional molecular featurization schemes, complicates the development of robust molecular machine learning models. Self-supervised learning (SSL) has emerged as a popular solution, utilizing large-scale, unannotated molecular data to learn a foundational representation of chemical space that might be advantageous for downstream tasks. Yet, existing molecular SSL methods largely overlook chemical knowledge, including molecular structure similarity, scaffold composition, and the context-dependent aspects of molecular properties when operating over the chemical space. They also struggle to learn the subtle variations in structure-activity relationship. This paper introduces a multi-channel pre-training framework that learns robust and generalizable chemical knowledge. It leverages the structural hierarchy within the molecule, embeds them through distinct pre-training tasks across channels, and aggregates channel information in a task-specific manner during fine-tuning. Our approach demonstrates competitive performance across various molecular property benchmarks and offers strong advantages in particularly challenging yet ubiquitous scenarios like activity cliffs.