Browse Articles

Simulation-based reservoir analysis assisted by chemical tracers transport for the development of enhanced oil recovery strategies

Beyond van der Waals: Modular Identification of 2D Materials in Strongly Bonded Layered Crystals

Abstract Top‐down exfoliation of layered solids has revolutionized materials innovation by enabling unprecedented access to atomically thin architectures. To expand the frontier of two‐dimensional (2D) materials, it is imperative to systematically identify non‐van der Waals (non‐vdW) layered systems with strong interlayer bonding. While conventional atomic distance‐based algorithms effectively identify vdW materials, they are considerably limited when applied to chemically bonded non‐vdW systems due to their inherent bonding complexity. Here, we introduce a structural modularity paradigm that resolves dimensional identification challenges by removing specific elements from strongly bonded crystals to construct artificial substructures from residual atoms. Coupled with the appropriate layer‐recognition algorithms for vdW lattices, this approach reveals 8,889 hidden 2D modules across 30,147 ternary compounds, of which 80.6% are undetectable by traditional layer‐detection approaches. These findings show that non‐vdW layered solids significantly outnumber vdW counterparts (1,383), highlighting a vast chemical space for 2D materials exploration. High‐throughput thermodynamic screening of 15,312 ternary oxides identifies 1,083 chemically stable 2D modules exfoliable via a proton exchange‐assisted method. Validation against 15 experimentally reported 2D oxide nanosheets, along with the first synthesis of ultrathin H x RuO 3 nanosheets from non‐vdW Na 2 RuO 3 crystal, confirms the reliability and predictive strength of this method for both known and previously unexplored systems.

Sodium new houttuyfonate affects tumor angiogenesis by suppressing FGF1 expression and the p38/MAPK signaling pathway in pancreatic cancer

Clinical feasibility and cost efficiency of perinatal mesenchymal stem cell production under GMP conditions

Tailoring Na⁺ Chelation Dynamics for Expedient Sulfur Redox Kinetics in Low‐Temperature Sodium–Sulfur Batteries

Abstract Sodium–sulfur (Na–S) batteries have attracted considerable attention due to their high theoretical energy density and the abundant natural availability of sodium and sulfur. However, sluggish kinetics of sulfur conversion, slow Na⁺ transport, and interfacial instability at low temperatures pose significant challenges for their operation and limit their practical application. Herein, three solvents with well‐designed molecular configurations are examined. We systematically investigate the impact of chelation effect on the desolvation behavior, sulfur conversion process, ion dynamics, and Na⁺ plating/stripping behavior. Compared with conventional linear ether solvents, the incorporation of methyl groups not only weaken the chelation capability and tailors the inner solvation sheath, but also reduces the energy barrier for Na⁺ transport, thus promoting enhanced sulfur conversion kinetics under low temperature conditions. This work elucidates the relationship among solvent molecules, Na⁺ desolvation behavior, and sulfur reaction kinetics, and offers a strategy for rational design of electrolytes for low‐temperature metal–sulfur batteries.

Energy-efficient scheduling of AGV-assisted robotic flexible flowshops under learning and processing time uncertainty

Structural optimization of framed automotive body-in-white using back propagation neural network optimized based on the sparrow search algorithm

Tungstotellurate(VI)‐Based Nanocomposite Inhibits Breast Cancer Proliferation and Metastasis by Enhancing Cuproptosis and Apoptosis

Abstract Cuproptosis, characterized by the accumulation of copper (Cu) ions and mitochondrial respiration modulation, holds great potential in cancer therapy. As a newly defined mode of cell death regulation, the mechanisms of copper efflux and high intracellular levels of reducing substances may impair cuproptosis efficacy. To address this, a nanocomposite designated POM/Cu‐SS@HA was constructed, comprising sandwich‐type polyoxometalate (POM) Na[(CH 3 ) 2 NH 2 ] 13 H[Sc 3 (H 2 O) 2 Te 2 W 24 O 90 ]·92H 2 O ( Sc 3 Te 2 W 24 ), disulfide‐bridged copper‐based complexes (Cu‐SS), and hyaluronic acid (HA) for targeted delivery. This system induces cuproptosis through copper accumulation, leading to lipoylated protein aggregation and iron–sulfur cluster protein depletion, while concurrently facilitating photothermal therapy (PTT) and apoptosis. The POM ( Sc 3 Te 2 W 24 ) cluster exhibited potent antitumor effects via p53‐dependent apoptotic pathway reactivation. In vitro studies demonstrated over 85.6% inhibition rate against 4T1 breast cancer cells when combining POM/Cu‐SS@HA with 808 nm NIR irradiation. In vivo results showed 92.8% tumor growth suppression compared to the controls. Transcriptome analysis further identified altered expression profiles in glutathione (GSH) metabolism and mitochondrial function‐related genes, confirming the dual induction of cuproptosis and apoptosis. These findings establish a synergistic nanotherapeutic strategy with enhanced efficacy for breast cancer treatment.

Understanding machine learning weather prediction by designing a cost-efficient model with knowledge-oriented modules

Abstract Deep learning-based models are gaining prevalence in global weather forecasting, surpassing the performance of existing numerical models. However, training these models with high-resolution global weather data requires massive computational resources, making it difficult to conduct extensive experiments to understand the model processes. In addition, the reason for region- or variable-dependent accuracy in the machine learning models, along with the extra predictability provided by each component, remains unknown. Therefore, we propose a novel data-driven model named KARINA, which combines Geocyclic Padding and SENet modules with the ConvNeXt backbone to enhance weather forecasting while minimizing training resources. Despite its much lower training cost, KARINA achieved competitive performance compared to the recently developed data-driven models such as Pangu-Weather and GraphCast, while surpassing the numerical weather prediction of ECMWF IFS at a lead time of up to 10 days. The efficient training process and KARINA’s modular structure allow us to demonstrate the effectiveness of Geocyclic Padding and SENet through comprehensive trials. Geocyclic Padding significantly improves the modeling of horizontal advection, while SENet particularly captures the dynamics of atmospheric convection. These findings suggest that incorporating knowledge-oriented techniques can lead to reliable performance. This paper presents a framework for gaining a deeper understanding of the model mechanism and proposes ways to improve machine learning weather prediction models.

Structural basis of dimerization and cascade formation by Cas5

A Probe Design Strategy Based on Cholesterol‐Induced Differential Cellular Uptake for Cancer Cells Specific Penetration and Imaging

Abstract Cancer metastasis and postoperative recurrence continue to pose significant challenges in the treatment of advanced‐stage cancer, highlighting the urgent necessity for early diagnosis and precise intraoperative identification of lesions. In this study, we synthesized a series of molecules derived from the traditional nucleic acid probe thiazole orange (TO), by incorporating alkyl chains of varying lengths and hydrophilic functional groups. Among them, the specific lipophilicity of TO‐Ac (a derivative of TO) enables its penetration into cancer cells, where it binds to nucleic acids and generates intense fluorescence, while being effectively excluded by normal cell membranes. The differential cholesterol content in cell plasma membranes was confirmed to influence the selective uptake of molecules in cancer cells. Accordingly, we established a quantitative design criterion (LogP = 1.48–1.62) that predicts cancer cell specificity based on oil‐water partition coefficients. Finally, TO‐Ac was successfully applied for detection of early‐stage colon cancer, enabling complete tumor resection via fluorescence imaging while identifying lesions that would otherwise remain undetected. This passive targeting strategy fundamentally challenges the conventional paradigm of ligand‐modified probes, offering a transformative approach for the development of universal tumor identification systems.

Prediction of remaining useful life for electronic equipment based on online PINN

Low bias negative differential resistance in WSe2/MoS2 Planar Superlattice Diodes

Design and optimization of a polarization-insensitive Ti/TiO2 metamaterial absorber using particle swarm optimization for broadband solar–thermal applications

Abstract In this paper, a polarization-insensitive and ultra-broadband metamaterial absorber composed of titanium (Ti) and titanium dioxide (TiO 2 ) resonators is proposed. The multilayer architecture integrates three square resonators and one disk resonator, which are optimized to maximize absorption across the 0.25–4 μm spectral range. Particle swarm optimization (PSO) yields an average absorption of 98.99% and a bandwidth of 3533 nm (467–4000 nm). The structure maintains absorption above 80% for transverse magnetic (TM) polarization and above 90% for transverse electric (TE) polarization for wavelengths above 600 nm, even at incidence angles up to 60°. Under the Air Mass 1.5 Global spectrum (AM 1.5G) solar spectrum, the device achieves a solar absorption efficiency of 98.17% and a thermal emission efficiency of 99.26% at 1800 K. This performance is a result of the combined effects of surface plasmon polaritons, magnetic plasmons, and localized surface plasmon resonances. The absorber further demonstrates robustness against structural variations, maintaining over 90% absorption across the wavelength range of 250–4919 nm (BW = 4669 nm). The designed absorber exhibits a compact electrical size, with a total thickness of approximately 0.2λ and periodicity of 0.1λ, calculated at the longest operating wavelength (4 μm), confirming its subwavelength characteristics and suitability for planar integration. In comparison to existing Ti-based absorbers, this design provides both the broadest operational bandwidth and the highest efficiency, indicating strong potential for solar energy harvesting applications.

Next-generation Candida albicans vaccine VXV-01 containing recombinant Als3p and Hyr1p antigens for invasive Candida infections

Study on the anti-tumor effect of Wall-broken ganoderma lucidum spore powder on papillary thyroid cancer

Engineering Nanoscale Interfacial Solvation Inner‐Outer Configuration via Multi‐Group Synergy for Practical Zinc Batteries

Abstract Rational design of interfacial solvation structures in electric double layer (EDL) remains a critical challenge for aqueous zinc metal batteries (AZMBs). Herein, an efficient multi‐group synergy strategy has been proposed to precisely regulate the interfacial solvation structure at nanoscale, which affords long‐lifespan AZMBs under high depth of discharge (DOD) and low temperature. Through combined in situ experiments and theoretical simulations, we demonstrate trace multifunctional group biomolecular additive cannot alter electrolyte solvation structure, but contributes to formation of the positively charged Zn 2+ solvation shell in outer Helmholtz layer (OHL) and additive‐involved and H 2 O/anion‐less solvation shell in inner Helmholtz layer (IHL). This synergistic configuration enables an organic‐inorganic hybrid interface that simultaneously suppresses hydrogen evolution, accelerates desolvation, offers pH buffering capacity, and regulates Zn 2 ⁺ deposition orientation. Zn anodes deliver high coulombic efficiency of 99.65%, long‐lifespan over 6500 h, and stable operation under low temperature of −20 °C and high DOD of 85.4%. Furthermore, under practical condition of high mass loading (27 mg cm −2 ) and limited N/P ratio of 3.5, Zn||VO 2 battery delivers a superhigh surface capacity of 8.1 mAh·cm −2 and remains stable over 800 cycles, and pouch batteries can stably operate for almost 500 cycles.

Generalized Galton’s boards explain social phenomena via statistical physics

222Rn, 226Ra, and 238U in soil samples at a depth of 50 cm in KUFA City of AL-NAJAF Governorate, IRAQ

Statins attenuate PD-L1 sorting to small extracellular vesicles dependent on ubiquitin-like 3 modification

Abstract Small extracellular vesicles (sEVs) mediate cell-to-cell communication by carrying RNAs and proteins. Ubiquitin-like 3 (UBL3) functions as a posttranslational modification factor, regulating protein sorting to sEVs. Programmed cell death ligand 1 (PD-L1) binds to programmed cell death 1 (PD-1) on immune cells, suppressing their function. Although immune checkpoint inhibitors, anti-PD-L1 and anti-PD-1 antibodies, have improved cancer treatment, efficacy remains limited (~ 25%). Per recent studies, PD-L1-containing sEVs are elevated in cancer patients, contributing to impaired immunotherapy responses. Herein, we discovered that PD-L1 is modified by UBL3 and that its sorting to sEVs is regulated by UBL3. Furthermore, we found that statins, commonly prescribed for hypercholesterolemia, inhibit UBL3 modification, thereby reducing PD-L1 sorting to sEVs. Among patients with a high tumor proportion score, serum levels of PD-L1-containing sEVs were significantly lower in those using statins. Consistently, bioinformatic analysis revealed that UBL3 and PD-L1 expression levels affect lung cancer survival. Integrating statins into existing combination therapies may therefore offer a promising strategy to enhance immunotherapy efficacy.