Materials Science Research Articles

Benzoic acids are widely utilized in synthetic chemistry, pharmaceuticals, and materials science, yet their potential as precursors for linear synthons is severely constrained by the challenge of cleaving their highly stable aromatic C─C bonds. Here, we report that a PDPP (4-(piperidin-1-yl)pyridine) and copper synergistically catalyzed nitrogenation of benzoic acids with a tailored nitrogenation reagent. This protocol efficiently enables the selective decarboxylation and benzene ring opening processes leading to acyclic dinitrile products. The designed new azide reagent plays an essential role in the PDPP-catalyzed initial step to trigger the selective carboxyl and aromatic C─C bond cleavage process. This synergistic strategy greatly expands the synthetic utility of benzoic acids, providing a direct and practical route to acyclic scaffolds. The aromatic ring-opening products could be further used in cyclization reactions to realize phenyl ring scaffold hopping.

Dibenzodiazepines and organoselenium compounds represent important structural motifs with broad applications in pharmaceuticals and materials science. Herein, we describe an electrochemical selenylative cyclization strategy for the efficient synthesis of selenylated dibenzodiazepines at room temperature. Utilizing stable diselenides as the selenium source and isocyanides as a C1 synthon, a series of organoselenyl dibenzodiazepine derivatives were smoothly synthesized with up to 81% yield, demonstrating excellent functional group compatibility and high atom efficiency.

Introduction: Advances in material science and cell technology offer new possibilities in myocardial repair. Epicardial biocompatible scaffolds loaded with human (h)iPSC-derived cardiac cells may be promising but require safety, feasibility and efficacy studies in a porcine ischemic cardiomyopathy model, representative of human disease. Methods: We induced myocardial infarction (MI) in domestic pigs by 90min LCx occlusion and evaluated functional and structural LV remodelling using MRI 4w later. We printed multi-layered, 4x6cm scaffolds with a hexagonal regenerative zone using 3D printing technology, and populated them with 140 million hiPSC-derived cells (90% cardiomyocytes/10% cardiac fibroblasts) loaded in fibrin hydrogel. We confirmed stable beating rates in vitro and sutured the patches at 4w on the epicardial surface of the infarct area. Cell-free patches served as control. All pigs received immunosuppression with tacrolimus, azathioprine, and methylprednisolone, started 2w before patch implantation and continued 4w thereafter, when a 2nd MRI was performed. We continuously monitored arrhythmias using implantable loop recorders (ILR) and performed histological analysis. Results: At 4w after MI, infarct size was 12±3% of LV mass, and was associated with reduced global systolic function (LVEF 51±2%, n=12). We implanted spontaneously contracting scaffolds (beating rates of 84±27/min, range 48-108) on the infarcted heart, and measured significantly improved LVEF 4w later in the cell-loaded group (56±1% vs 46±6% in CON, P=0.003, n=6 for both), which was attributable to a smaller indexed end-systolic volume (50±6 vs 71±14 mL/m 2 , P=0.01). Immunosuppression was well tolerated (normal LFTs and eGFR) and no major arrhythmias were recorded on ILR. Human Ku80-positive cardiac cells were readily detectable in the patch 1w after implantation, but human cell survival 4w later was limited. Cell-loaded patches significantly promoted neovascularisation in the infarct (P=0.011) and border zone (P=0.015) and reduced myocardial fibrosis (P=0.020) without a prohibitive inflammatory response (Fig 1). Conclusions: Implantation of hiPSC-derived cellular scaffolds of clinically relevant size in the infarcted porcine heart is safe and significantly improves LV functional repair. The contractile benefit is predominantly attributable to paracrine pro-angiogenic and anti-fibrotic mechanisms and supports further development of this innovative treatment for ischemic cardiomyopathy.

This review aims to examine the impact of three-dimensional (3D) printing technologies on enhancing psychiatric pharmacotherapy through facilitating personalized and patient-centered drug delivery. This research specifically addresses problems such as poor medication compliance, polypharmacy, and palatability issues, especially in pediatric and elderly populations. A thorough review of the literature was conducted, focusing on novel advances in 3D printing techniques, including fused deposition modeling (FDM), semisolid extrusion (SSE), stereolithography, inkjet printing, binder jetting, and selective laser sintering (SLS). Selected research highlighted the application of such technologies in developing customized oral drug dosage forms. Emphasis was placed on the exploitation of polymers like Eudragit® E PO, flavor-masking excipients, and their combination with biosensor and artificial intelligence (AI) systems. Case studies were assessed to ascertain their relevance and innovation in the development of psychiatric medications. 3D printing allows the manufacture of tailored psychiatric drugs with greater dosing versatility, taste masking, and the ability to merge several active drug ingredients into a single pharmaceutical form. Patient-friendly dosage forms such as chew gummies and chocolate tablets demonstrated enhanced acceptability. Also, forthcoming technologies such as 4D printing and AI-driven biosensors yield intelligent, interactive drug release systems that are specific to individual physiological or behavioral inputs. 3D printing represents a paradigm-shifting advance in psychiatric care, offering solutions to long-standing treatment compliance and fixed-dose challenges. Although regulatory and scalability challenges persist, the intersection of pharmaceutical engineering, material science, and artificial intelligence creates an encouraging platform for the future of precision mental care therapies.

Purpose This study aims to investigate the tensile and flexural strength of composite parts produced by joining different materials in a butt joint using additive manufacturing. Design/methodology/approach International Organization for Standardization standards were used for the specimen designs, and Fused Deposition Modeling was used for sample production. Pure polylactic acid (PLA), pure polyethylene terephthalate glycol (PETG) and PLA–wood composite were used as research materials, and six combinations were created. Pure material combinations were used for references. Tensile and three-point bending tests were performed to detect mechanical strengths. Findings As a result of the study, it was seen that pure PLA was the strongest combination in both tensile and three-point bending tests. The weakest adhesion occurred between PETG and PLA–wood composite, almost a tenth of pure PLA in both tensile and three-point bending strengths. The strongest adhesion occurred between PLA and PLA–wood composite. Originality/value This study contributes significantly to the literature by examining the direct transition of different materials in Functional Graded Materials using additive manufacturing. In particular, the application of both tensile and three-point bending tests provided valuable information regarding adhesion strength. In addition, this study contributed to the literature via wood-reinforced PLA biocomposite and used high printing speed (250 mm/s) for the production of functionally graded materials.

The tripodal receptor GUA-IND was synthesized using guanidinium hydrazide as a core, and its three arms were prepared with three indole moieties. This Schiff base receptor formed an organohydrogel (GI-G) when triggered by the presence of SO42-/HSO4- anions selectively, thus demonstrating the rarely observed anion-induced supramolecular gelation behaviour. The supramolecular gel (GI-G) was found to be stimuli-responsive towards picric acid (PA) among different nitro-aromatic compounds, enabling completely naked eye sensing of picric acid, circumventing the need for any instruments. Gel nanocomposites (GI-Cu-G and GI-Ag-G), formed from GI-Gvia in situ reduction of precious metal salts like Cu2+ and Ag+ into their nanoparticles inside the gel matrix without using any external reducing agents, demonstrated antioxidant and antimicrobial activities. Additionally, the receptor facilitated anion exchange in the solid state with diverse anions, emphasizing its versatility and innovative design. This research paves the way for the development of advanced materials with tailored functionalities.

With the rapid expansion of the nuclear industry, the safe and efficient management of nuclear waste has emerged as a pressing global imperative that is crucial for protecting both the environment and future generations. The release of hazardous radionuclides such as uranium (U), americium (Am), technetium (Tc), rhenium (Re), iodine (I), selenium (Se), thorium (Th), cesium (Cs), and strontium (Sr) into the environment may pose serious threats to human health and can significantly disrupt the ecological balance. Addressing these issues requires the development of advanced materials capable of selectively adsorbing these hazardous radionuclides. This review highlights the potential of modular advanced functional porous materials (AFPMs), specifically those based on metal-organic frameworks (MOFs), covalent organic frameworks (COFs), and porous organic polymers (POPs), as the next-generation adsorbents for radionuclide remediation. We provide comprehensive outlines of the modular porous materials along with an in-depth analysis of their adsorption efficiency, selectivity, stability, and reusability, offering insights into their sorption mechanisms and structural advantages. Furthermore, we discuss the latest advancements in the synthesis, functionalization, and application of these materials in nuclear waste treatment. Additionally, we evaluate the chemical toxicity, radiation hazards, and detection strategies for key radionuclides. With their exceptional tunability and superior performance, these advanced porous materials hold significant promises for advancing sustainable nuclear waste management strategies, positioning them as the pivotal sorbent materials in both environmental and industrial applications. This comprehensive review underscores the transformative potential of tailor-made porous materials in mitigating the risks associated with radioactive contamination, marking a significant step toward achieving a cleaner and safer nuclear future.

Abstract Saturated nitrogen heterocycles are highly prevalent in medicinal chemistry, agrochemistry, and material science. Despite extensive research to access substituted piperidines and related compounds, there remains a strong demand for new stereoselective approaches. We present a Brønsted acid‐promoted, light‐induced formal ring‐insertion strategy for the efficient synthesis of piperidone and pyrrolidone derivatives. This method enables the efficient construction of thermodynamically disfavored cis ‐disubstituted piperidone and pyrrolidone derivatives. A key aspect of this strategy is the use of an acyl imidazole as a uniquely capable chromophore activator, in cooperation with the Brønsted acid, which controls both reactivity and stereoselectivity without the need for photosensitizers. The unique properties of the in situ generated carbonyl triplet diradical enable a selective 1,5‐hydrogen atom transfer process, followed by C─N bond cleavage and a subsequent Mannich reaction, offering broad functional group tolerance. The synthetic utility of this methodology was exemplified by the preparation of key intermediates for the synthesis of neurokinin 1 antagonist drug candidates.

Smoothed dissipative particle dynamics (SDPD) is a widely used particle-based method for modeling soft matter systems at mesoscopic and macroscopic scales, offering thermodynamic consistency and direct control over the fluid's transport properties. Here, we present an SDPD model that incorporates the transport of reactants on scales smaller than the discretizing particles, including the evolution of compositional fields. The proposed methodology is well-suited for modeling complex systems governed by advection-diffusion-reaction (ADR) dynamics. Implemented in Large-scale Atomic/Molecular Massively Parallel Simulator, the model is validated using a range of benchmark problems spanning diffusion-dominated, reaction-dominated, and coupled ADR regimes. Our simulation results demonstrate that the implemented SDPD model effectively captures complex behaviors, such as Turing pattern formation. The proposed model holds promise for applications across various fields, including biology, chemistry, materials science, and environmental engineering.

When two molecular species with mutual affinity are mixed together, various self-assembled phases can arise at low temperature, depending on the shape of like and unlike interactions. Among them, stripes-where layers of one type are regularly alternated with layers of another type-hold a prominent place in materials science, occurring, for example, in the structure of superconductive doped antiferromagnets. Stripe patterns are relevant for the design of functional materials, with applications in optoelectronics, sensing, and biomedicine. In a purely classical setting, an open question pertains to the features that spherically symmetric particle interactions must have to foster stripe order. Here, we address this challenge for a lattice-gas mixture of two particle species, whose equilibrium properties are exactly determined by Monte Carlo simulations with Wang-Landau sampling, in both planar and spherical geometry and for equal chemical potentials of the species. Somewhat surprisingly, stripes can emerge from largely different off-core interactions, featuring various combinations of repulsive-like interactions with a predominantly attractive unlike interaction. In addition to stripes, our survey also unveils crystals and crystal-like structures, cluster crystals, and networks, which considerably broaden the catalog of possible patterns. Overall, our study demonstrates that stripes are more widespread than generally thought, as they can be generated by several distinct mechanisms, thereby explaining why stripe patterns are observed in systems as diverse as cuprate materials, biomaterials, and nanoparticle films.

Abstract Unsupervised pre-training on vast amounts of graph data is critical in real-world applications wherein labeled data is limited, such as molecule properties prediction or materials science. Existing approaches pre-train models for specific graph domains, neglecting the inherent connections within networks. This limits their ability to transfer knowledge to various supervised tasks. In this work, we propose a novel pre-training strategy on graphs that focuses on modeling their multi-resolution structural information, allowing us to capture global information of the whole graph while preserving local structures around its nodes. We extend the work of Graph \textbf{Wave}let \textbf{P}ositional \textbf{E}ncoding (WavePE) from \citet{10.1063/5.0152833} by pretraining a \textbf{H}igh-\textbf{O}rder \textbf{P}ermutation-\textbf{E}quivariant Autoencoder (HOPE-WavePE) to reconstruct node connectivities from their multi-resolution wavelet signals. Since our approach relies solely on the graph structure, it is domain-agnostic and adaptable to datasets from various domains, therefore paving the way for developing general graph structure encoders and graph foundation models. We theoretically demonstrate that for $k$ given resolutions, the width required for the autoencoder to learn arbitrarily long-range information is $O\left(n^{1/k}r^{1+1/k}\epsilon^{-1/k}\right)$ where $n,r$ denote the number of nodes and the rank of normalized Laplacian, respectively, and $\epsilon$ is the error tolerance defined by the Frobenius norm. We also evaluate HOPE-WavePE on graph-level prediction tasks of different areas and show its superiority compared to other methods. Our source code is publicly available at \url{https://github.com/HySonLab/WaveletPE}.

Synchrotron radiation micro-computed tomography (SR-µCT) is a vital technique for the quantitative characterization of three-dimensional internal structures across diverse fields, including energy, integrated circuits, materials science, biomedicine, archaeology etc. While SR-µCT provides high spatial resolution and high image contrast, it typically offers only moderate temporal resolution, with acquisition times ranging from minutes to hours. Recently, dynamic SR-µCT has attracted significant interest for its capacity to capture real-time three-dimensional structural evolution. Here, we demonstrate a dynamic SR-µCT system operating at 26.7 Hz, developed at the BL09B test beamline of the Shanghai Synchrotron Radiation Facility using a filtered white beam. The key components of this system include an air-cooling millisecond fast shutter, an air-bearing rotation stage, a high-efficiency detector integrated with a Photron FASTCAM SA-Z camera and a custom-designed optical system, and a synchronization clock to ensure precise temporal alignment of all devices. Experimental results confirm the feasibility of this approach for in vivo four-dimensional studies, making it particularly promising for applications in biomedical research and related disciplines.

Molecular nanocarbons are critical bridges between small polycyclic aromatic hydrocarbons and extended graphene lattices, driving advances across materials science, optoelectronics, and quantum technologies. However, the atomically precise synthesis of such systems, particularly those featuring K-region and cove-type topologies, remains an enduring challenge. Here, we present a de novo modular strategy that overcomes this constraint by directing K-region growth via annulation of preorganized aryl acetylnaphthalene precursors with acetylenedicarboxylate. The strategy mirrors the hierarchical construction of pyrene by formally inserting naphthalene fragments into spatially defined molecular scaffolds. This method integrates Suzuki-Miyaura coupling with a Zn(OTf)2-catalyzed cascade comprising Friedel-Crafts alkylation, dehydro-Diels-Alder cycloaddition, and dehydrogenative aromatization. The platform affords structurally diverse pyrene-based molecular nanocarbons with programmable control over topology and dimensionality, spanning linear, contorted, and three-dimensional π-architectures. These results establish a generalizable blueprint for bottom-up synthesis of complex carbon-rich architectures with atomic precision.

Fluorosurfactant-stabilized microdroplets hold significant promise for a wide range of applications, owing to their biological and chemical inertness. However, conventional synthetic routes for fluorosurfactants typically require multiple reaction steps and stringent conditions, such as high temperatures and anaerobic environments. This complexity poses a significant limitation to the development of fluorosurfactantsynthesis and its subsequent applications in droplet-based systems. In this work, a robust two-step synthesis of fluorosurfactants with tunable functionalities is presented. Microdroplets stabilized by these fluorosurfactants exhibit enhanced stability and biocompatibility. Notably, these fluorosurfactants facilitate the formation of nanodroplets that efficiently transport and concentrate fluorophores with high selectivity. Furthermore, it is demonstrated that colloidal self-assemblies with distinct structures can be engineered by modulating interactions between the fluorosurfactants and colloidal particles. The synthetic approach provides a strategy for the rapid production of functional fluorosurfactants under mild conditions, enabling droplet-based microfluidic techniques with applications in biology and material science.

Next-generation autonomous laboratories that combine machine learning (ML), robotic synthesis, and in situ characterization is rapidly emerging as one of the key directions in modern materials science. By integrating active and deep learning algorithms with multi-level datasets ranging from quantum mechanical simulations to high-throughput flow-based experiments, these platforms establish closed-loop frameworks that guide complex nanofabrication processes in real time. Such AI-driven systems lower material and energy consumption, enable the precise design of nanostructures with tailored optical, electronic, and mechanical properties, and create research opportunities that were previously difficult to realize. This review provides a comprehensive overview of self-driving laboratories (SDLs), including their architectures, algorithmic strategies, and practical demonstrations such as the optimization of perovskite nanostructures, the development of nanoparticles for targeted drug delivery, and the synthesis of quantum dots with controlled emission. It also discusses existing methodological limitations, the need for standardized data practices, and challenges related to the integration of in situ techniques, while highlighting the prospects for creating fully digital pipelines for material production, as evidenced by recent autonomous laboratory demonstrations that progressed from initial hypotheses to functional prototypes within remarkably short timeframes. Furthermore, the self-driving lab paradigm is beginning to transition from academic laboratories into industrial research and development. This emerging trend suggests that SDL techniques will soon accelerate discovery and development in sectors such as pharmaceutical research and chemical manufacturing.

Endohedral metallofullerenes (EMFs) are a unique class of hybrid molecules formed by encapsulating metal atoms within carbon cages (fullerenes), giving rise to distinctive properties that differ from empty fullerenes. Extensive research has focused on optimizing the synthesis, extraction, isolation, and characterization of EMFs, along with investigating their physicochemical properties and potential applications in areas such as electronics, photovoltaics, biomedicine, and materials science. Here, the use of a laser vaporization source combined with ion mobility and mass spectrometry is demonstrated to characterize and isolate EMF structures, enabling further investigation of their gas-phase chemical properties. This approach is illustrated through a comparative study of the reactivity of empty carbon cages and calcium EMFs in the nucleophilic addition of pyridine.

Endowing functional crystals with flexibility significantly broadens their potential applications in the field of flexible smart devices. Despite tremendous efforts to understand the foundational structural principles of flexibility, most discoveries of crystals with mechanical flexibility remain accidental. Currently, machine learning has become a transformative research paradigm in materials science. Here, we propose an innovative approach and develop a platform for predicting the mechanical properties of molecular crystals named CrystalGAT. CrystalGAT is a graph neural network model based on attention mechanisms, which constructs a robust optimal model through data augmentation strategies and achieves markedly superior prediction performance compared to established models. A high prediction accuracy of 90% is demonstrated on the validation set, alongside a promising generalization capability that extends to multicomponent systems. Most importantly, we have identified key segments influencing the mechanical properties of molecular crystals using CrystalGAT, successfully achieving a breakthrough in transforming brittle crystals into flexible photoresponsive crystals. Furthermore, it is possible to quickly screen for plastic multicomponent drug crystals, enhancing tableting performance. CrystalGAT provides an efficient method for flexible molecular crystal design, demonstrating its potential applications in material discovery and drug molecular modification. For user convenience, a dedicated website has been established: https://huggingface.co/spaces/ZZZCCCYYY/CrystalGAT.

Diatoms are unicellular eukaryotic algae, renowned for their intricately patterned silica cell walls, which exhibit remarkable morphological precision and nanostructural complexity. Theypossess a unique and rich biochemical profile and are prolific producers of biologically active polysaccharides, broadly categorized as intracellular, extracellular (primarily sulfated), and cell wall-associated types. These polysaccharides play vital roles in biofilm formation, carbon cycling, nutrient storage, and ecosystem dynamics, while also holding substantial promises in commercial and biotechnological fields.This review provides an integrated overview of diatom polysaccharide chemotypes-storage β-glucans, cell-wall uronic- and sulfate-rich scaffolds, and extracellular exopolymers-and evaluates the conventional versus emerging extraction and purification techniques, discussing trade-offs in yield, selectivity, and polymer integrity. Thediverse structural characterization methods for elucidating monosaccharide linkages and functional modificationshave been reviewed. Thegenomic and metabolic insights into polysaccharide biosynthesis have been elaboratedalong with elucidation of the relationship between extracellular polymeric substances and bacterial community assembly. The multifacetedapplications of diatom-derived polysaccharides in carbon sequestration, biomedicine (e.g., anticoagulant, antioxidant, antiviral, anticancer, immunomodulatory agents), materials science, and environmental remediationhas been discussed along with the current challenges-species variability, efficient frustule disruption, and scalable processing. The-genomics-guided strain optimization and sustainable bioprocess design holdsimmense future potential for diatom derive polysaccharides.

Background A comprehension of the macroeconomic losses on a worldwide, regional, and national scale attributable to liver cancer is crucial for the optimal distribution of medical and research materials. The authors conducted an investigation into the macroeconomic impacts of the strain imposed by liver cancer in 2021 across 185 nations. Methods The data pertaining to disability-adjusted life years (DALY) for liver cancer and its associated risk factors were sourced from the 2021 records of the Global Burden of Disease investigation. Information pertaining to GDP, modified for purchasing power parity (PPP), originated from the World Bank; the integration of GDP and DALY data facilitated the estimation of macroeconomic losses through the application of a value of lost welfare (VLW) methodology. Every finding is articulated in 2021 international US dollars, calibrated for PPP. Outcomes In the year 2021, the VLW resulting from liver cancer worldwide amounted to $141.95 B, representing 0.15% of the worldwide GDP. The worldwide VLW/GDP ratio for alcohol-related liver cancer was 0.033% (VLW = $31.835 B) Hepatitis B-associated liver cancer prevalence was 0.041% (VLW = $38.667 B) Hepatitis C-associated liver cancer prevalence was 0.056% (VLW = $53.268 B) incidence of NASH-related liver cancer was 0.012% (VLW = $11.653 B) the incidence of liver cancer attributed to alternative factors was recorded at 0.007% (VLW = $6.728 B). The East Asia, Southeast Asia, and Oceania super-region recorded the greatest VLW/GDP for liver cancer overall was 0.19%, with VLW of $39.08 B, the high-income super-region accounted for the second (VLW/GDP = 0.16%; VLW = $88.00 B). Conclusion The global macroeconomic burden attributable to liver cancer is substantial, with far-reaching implications for productivity losses and healthcare expenditure. These evidence-based economic estimates provide a compelling rationale for strategic resource allocation towards liver cancer control programs.

Polyaniline (PANI), as a classical conducting polymer, has attracted significant attention in the field of energy storage due to its low cost, facile synthesis, environmental stability, and unique dual electronic/ionic conductivity. Particularly, one-dimensional (1D) nanostructures of PANI, such as nanowires and nanorods, exhibit superior electrochemical performance and cycling stability, attributed to their high surface area and efficient charge transport pathways. This review provides a comprehensive summary of recent advances in 1D PANI-based anode materials for lithium-ion, sodium-ion, and other types of rechargeable batteries. The specific capacity, rate performance, and long-term cycling behavior of these materials are discussed in detail. Moreover, strategies for performance enhancement through combination with carbon materials, metal oxides, and silicon, as well as chemical doping and structural modification, are systematically reviewed. Key challenges including electrochemical stability, structural durability, and large-scale fabrication are analyzed. Finally, the future directions in structural design, composite engineering, and commercialization of 1D PANI anode materials are outlined. This review aims to provide insight and guidance for the further development and practical application of PANI-based energy storage systems.