Browse Articles

Archean rifts and triple-junctions revealed by gravity modeling of the southern Superior Craton

Abstract The nature of Archean tectonics and the associated geodynamic regimes are much debated in modern geoscience, despite decades of research. In this study, we present a geophysical model to show that, by the Neoarchean, convective forces from rising mantle plumes or early forms of plate subduction caused widespread extension, creating linear zones of crustal growth. These regimes can be identified as Archean rifts in the ancient rock record by the topography of the Moho, i.e., a shallowing of the boundary between the crust and the lithospheric mantle. Gravity data collected over the Abitibi greenstone belt, a particularly well-preserved portion of Neoarchean crust located in Canada’s Superior Province, was modeled to produce a topographic map of the Moho. The model shows corridors of shallow Moho surrounding islands of thick, intrusion-filled crust and is interpreted to be a snap-shot of microplate growth and breakup between 2.75 to 2.69 Ga. The connectivity of the interpreted relict rifts is possible evidence for the existence of Neoarchean plate boundaries and triple junctions and supports a model of at least local mobile-lid tectonics during this stage of Earth’s history.

Electric Field‐Insensitive Solvation Chemistry Stabilizes High‐Voltage Lithium Metal Batteries

AbstractEther based electrolytes are promising for the realization of lithium (Li) metal batteries (LMBs). However, the oxidation chemistry of these electrolytes stemming from the disruption to solvent coordination by interfacial electric‐field remains unresolved and challenging. Herein, we demonstrated that reinforcing the interactions between solvent and diluent to decouple solvation dynamics from interfacial electric fields could maintain Li⁺‐solvent coordination integrity and drastically diminish uncoordinated solvents, thereby increasing electrolyte anodic stability. To realize this concept, we identify a class of solvophilic diluents (SPDs, whose interaction energies with solvent>3.3 kcal mol−1) to strongly anchor diethylene glycol dimethyl ether (DEGDME) solvent at electrified interfaces via mutually reinforcing H(DEGDME)···F/O(SPD) and H(SPD)···O(DEGDME) interactions and effectively decrease dipole moment of DEGDME (< 2.47 D), creating an electric field‐insensitive solvation environment wherein the O─H/C─H bond polarization of DEGDME is dramatically impeded. Remarkably, our designed SPD‐assisted electrolyte mediated by electric field‐insensitive methyl nonafluorobutyl ether exhibits outstanding anodic stability, endowing 4.7 V‐class 30 µm Li||2.1 mAh cm−2 LiNi0.8Co0.1Mn0.1O2 cell with 80% capacity retention after 168 cycles‐an improvement over the 90 cycles achieved with a Li friendly electrolyte. This work establishes a mechanistic framework for manipulating interfacial solvation dynamics to unlock high‐voltage LMBs.

Development of a new method to quantify filler dispersion in bituminous mastics by transmission microscopy and image analysis

Abstract This study introduces a novel method to analyze how fillers disperse in bituminous binders. A custom-designed laboratory setup enables precise temperature control (140 °C) and mixing speeds (200, 800, 1500 rpm) for small-scale mixtures (5% filler) over 5, 15 and 30 min. To assess dispersion, microscopy-based visualization approach using optical microscopy (10× objective) and image analysis (ImageJ). Each particle is segmented and its projected area converted into an equivalent diameter, yielding a surface-fraction distribution (in logarithmic size bins). We evaluate the D50 (Area50) position and the 50–100% range to detect coarse aggregates or over-shearing. The interest of the method is illustrated by comparing how limestone (CaCO2) and quartz (SiO2) fillers disperse in two different bitumens: naphthenic and paraffinic. Results indicate that quartz disperses more readily, quickly approaching a near-homogeneous state, whereas limestone requires higher shear and longer mixing times to approach the reference filler. Paraffinic bitumen shows a higher initial fine‑particle fraction; nonetheless, the naphthenic binder de‑agglomerates faster and ultimately matches, or slightly surpasses, the paraffinic dispersion. Mixing at 1500 rpm can fracture particles, skewing the size profile. The protocol delivers ± 1% repeatability across replicates, providing a robust tool for optimising mastic formulation and enhancing pavement durability.

Enhanced deep Southern Ocean stratification during the lukewarm interglacials

Covalent Organic Framework Nanohydrogel‐Based Oxidase‐Mimicking Nanozyme for Photocatalytic Antibacterial Therapy

AbstractWater‐soluble nanozymes have the potential to overcome the limitations of low catalytic efficiency of most heterogeneous nanozymes in aqueous solutions and further expand their applications in the biomedical field, but with significant synthetic challenges. Here we report an oxidase‐mimicking water‐soluble nanozyme based on zinc porphyrin‐based covalent organic framework nanohydrogel (Zn‐COF‐NHG) for photocatalytic antibacterial. The in situ atom transfer radical polymerization (ATRP) of poly(N‐isopropylacrylamide) (PNIPAM) on scaffold of Zn‐COF results in the exfoliation of crystalline COF nanosheets and assembly into nanohydrogels in aqueous solution. The obtained Zn‐COF‐NHG can effectively mimic photoresponsive oxidase‐like activity for the chromogenic catalysis of 3,3′,5,5′‐tetramethylbenzidine (TMB) by facilitating homogeneous behavior to enhance catalytic efficiency, while also exhibiting intelligent temperature‐response regulation of catalytic oxidation activity. Moreover, the high photodynamic production of reactive oxygen species (ROS) and the reinforcement of binding to the exterior of bacteria through noncovalent interactions concurrently boost its bactericidal activity against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) by amplifying oxidative stress. In vivo study on S. aureus‐infected murine model further substantiates the superior wound disinfection and healing effect of Zn‐COF‐NHG. Our work paves a way for the utilization of COF nanohydrogel as a potent antibacterial nanozyme agent and provides a novel platform for the development of biomedical applications.

Advanced prediction and optimization of VCR engine characteristics using RSM with DFA for sustainable biofuel derived from waste lemon Peel

Abstract The rising demand for alternative fuels stems from fossil fuel depletion, rising crude oil prices, and environmental concerns. Diesel engines, valued for efficiency and durability, contribute to resource depletion and pollution. Biofuels offer a sustainable alternative, with waste lemon peels presenting a viable feedstock for biofuel production. Using a steam distillation process, lemon peel waste oil (LPWO) is extracted from waste lemon peels and test fuel blends of LPWO and conventional diesel have been created in ratios of 5%, 10%, 15%, and 20%. According to the ASTM standards, the properties of LPWO and its blends, along with diesel, have been assessed. The characteristics of LPWO were determined by FTIR, GC-MS, and TG/dTG analysis. The performance, combustion, and emission parameters have been evaluated for neat LPWO and LPWO blends in a variable compression ratio (VCR) engine by varying BP between 0 kW and 5.2 kW and compression ratio from 16:1 to 18:1. From experimental analysis, optimum results are observed while using the blend 5% LPWO, BP 5.2 kW and CR 18:1. LPWO5 showed an increase in BTE and EGT by 2.168% and 3.09% while minimizing BSFC by 6.54%, also improved HRR and in-cylinder pressure; a decrease of CO, NOx, and smoke emissions by 59.42%, 30.99%, and 7.89% whereas 9.14% and 0.201% increase in HC and CO2 when compared to diesel fuel. To model and optimize the engine responses, a multiple regression model was developed using response surface methodology (RSM) with a desirability function approach (DFA). The optimal operating conditions predicted were 6.51% LPWO blend, 1.42 kW load, and CR 18:1, which closely aligned with experimental findings. The RSM-CCD design coupled with the DFA model yielded a combined desirability value of 0.8997. The VCR engine results were validated with the RSM predictions and DFA optimization, showing an error margin of less than 5%. These outcomes indicate that the LPWO5 blend holds strong potential as a viable alternative fuel for VCR engine applications.

Extremely poleward shift of Antarctic Circumpolar Current by eccentricity during the Last Interglacial

Innovative real-time pressure monitoring system utilizing Raspberry Pi and IMU for industrial application

Abstract This paper presents an innovative IoT-enabled solution for the real-time digitization of traditional chart recorders using a Raspberry Pi and the MPU6050 accelerometer. The proposed system harnesses modern IoT communication protocols to enable accurate pressure monitoring, remote data access, and real-time analysis, addressing the limitations of conventional paper-based systems. A key contribution of this work is the development of the first mathematical model for translating mechanical needle displacement in chart recorders into electrical signals, offering a robust theoretical foundation for precise signal conversion. Experimental results validate the system’s ability to accurately capture rapid pressure changes, demonstrating its suitability for demanding industrial applications, particularly in the oil and gas sector. The system’s performance was evaluated in various scenarios, showcasing its resilience to environmental noise, effective real-time data transmission (with latency as low as 130 ms), and significant noise reduction (up to 95%) through advanced filtering techniques. Furthermore, the system demonstrated a high level of accuracy in pressure measurements, with a maximum error of just 0.3 KPSI after filtering, confirming its reliability for precision monitoring. In addition to its technical capabilities, the proposed system supports paperless operation, significantly reducing operational costs and enhancing environmental sustainability. By eliminating the need for consumables such as paper and ink, the system offers a cost-effective and scalable solution. These results underscore the transformative potential of the system in modernizing industrial pressure monitoring, offering a scalable, precise, and environmentally sustainable alternative to traditional chart recorders. This work also lays the groundwork for future advancements in IoT-based sensing, predictive maintenance, and automation technologies in industrial settings.

Interfacial design strategies for stable and high-performance perovskite/silicon tandem solar cells on industrial silicon cells

Abstract Reducing interfacial non-radiative recombination at the perovskite/electron transport layer interface remains a critical challenge for achieving high performance and stable perovskite/silicon tandem solar cells. This study analyzes energy losses and design bilayer passivation for enhancing the performance and durability of tandem solar cells. Our experimental results confirm that, the bilayer passivation strategy, precisely modulates perovskite energy level alignment, reduces defect density, and suppresses interfacial non-radiative recombination. Moreover, the ALD-AlOx forms a homogeneous film on the perovskite grain surface while creating island-like structures at grain boundaries, enabling nanoscale local contact areas for subsequent PDAI2 deposition. While serving as an ion diffusion barrier, this structure facilitates moderate n-type doping and enhances charge extraction and transport efficiency. Monolithic perovskite/silicon tandem solar cells incorporating AlOx/PDAI2 treatment achieve a power conversion efficiency of 31.6% (certified at 30.8%), utilizing industrial silicon bottom cells fabricated with Q CELLS’ Q.ANTUM technology. Furthermore, our device exhibits 95% efficiency retention after 1000 hours of maximum power point tracking at 25 oC.

The association between preschoolers’ retinal microcirculation and the indoor microbial environment: results of the ENVIRONAGE birth cohort

Abstract Early life environmental microbiota may influence normative development. Here, we explore the associations between the residential indoor microbial environment and the retinal microcirculation among preschoolers. We included 177 children aged 4–6 years from the Belgian ENVIRONAGE birth cohort. We measured retina microcirculation using fundus photography and quantified the retinal vessel tortuosity [tortuosity index (TI)] and diameters [central retinal vein equivalent (CRVE) and central retinal artery equivalent (CRAE)]. Residential indoor microbial characteristics (bacterial and fungal loads, richness, diversity, and taxa) were measured in settled dust using qPCR and amplicon sequencing. Adjusted associations were obtained using linear regression models and expressed as coefficients (β) with 95% confidence intervals (CI). We observed inverse associations between microbial loads and retinal microcirculation, significant for CRAE: β = -0.28; CI:-0.53;-0.04 (bacteria) and β = -0.27; CI:-0.50,-0.03 (fungi). Conversely, retinal microcirculation was directly associated with Gram-positive bacterial loads, significant for TI (β = 0.44; CI:0.06,0.81). These associations were stronger among boys. No consistent associations were observed for diversity. Conclusively, indoor microbial loads can affect the retinal microcirculation in preschool children. Retinal vascularization is a cardiovascular marker linked to immune factors and brain vascularization. Our findings support previously observed associations of the environmental microbiome with cognition, and open new hypotheses about potential cardiovascular effects.

The impact of musical expertise on disentangled and contextual neural encoding of music revealed by generative music models

Fulcrum Occupancy–Leverage Perturbation Strategy Enables Rapid Discovery of Potent CDK2–Cyclin A2 Interaction Inhibitors

AbstractTraditional strategies for developing small‐molecule inhibitors of protein–protein interactions (PPIs) are time‐consuming and often yield low success rates due to the flat and dynamic interfaces of PPIs. To enable the rapid design of highly potent PPI inhibitors, we proposed a novel strategy named “Fulcrum Occupancy–Leverage Perturbation (FOLP)”. In this strategy, high‐affinity fragments serve as the “Fulcrum” by binding to the orthosteric pocket, while suitable moieties extend into allosteric sites near the PPI interface as “Leverage” to modulate the protein–protein interaction. As a proof of concept, the potent CDK2–Cyclin A2 PPI inhibitor LC‐K2CAin‐3, which fits the “FOLP” paradigm, was discovered with an IC50 of 32.1 nM for inhibiting the interaction. Molecular dynamics simulations and cryptic pocket identification were employed, revealing the activation loop (A‐loop) of CDK2 was flexible and targetable. X‐ray crystallography and hydrogen deuterium exchange mass spectrometry (HDX‐MS) analysis showed that LC‐K2CAin‐3 indeed bound to and stabilized the A‐loop. LC‐K2CAin‐3 effectively inhibited the CDK2–Cyclin A2 interaction in CDK2 highly expressed melanoma cells, leading to cell cycle arrest and apoptosis and inhibition of CDK2 mediated signaling. In conclusion, the “FOLP” strategy offers a novel approach for PPI inhibitor discovery and could accelerate the development of PPI inhibitors.

A monoclonal antibody selectively recognizing PfEMP1 proteins associated with cerebral malaria

Investigating whether deep learning models for co-folding learn the physics of protein-ligand interactions

Abstract Co-folding models represent a major innovation in deep-learning-based protein-ligand structure prediction. The recent publications of RoseTTAFold All-Atom, AlphaFold3, and others have shown high-quality results on predicting the structures of proteins interacting with small-molecules, nucleic-acids, and other proteins. Despite these advanced capabilities and broad potential, the current study presents critical findings that question the adherence of these models to fundamental physical principles. Through adversarial examples based on established physical, chemical, and biological principles, we demonstrate notable discrepancies in protein-ligand structural predictions when subjected to biologically and chemically plausible perturbations. These discrepancies reveal a significant divergence from expected physical behaviors, indicating potential overfitting to particular data features within its training corpus. Our findings underscore the models’ limitations in generalizing effectively across diverse protein-ligand structures and highlight the necessity of integrating robust physical and chemical priors in the development of such predictive tools. The results advocate a measured reliance on deep-learning-based models for critical applications in drug discovery and protein engineering, where a deep understanding of the underlying physical and chemical properties is crucial.

Phytochemical profiling, HPLC analysis, and antimicrobial potential of Curio radicans (L. f.) P.V. Heath

Learning plasma dynamics and robust rampdown trajectories with predict-first experiments at TCV

Abstract The rampdown phase of a tokamak pulse is difficult to simulate and often exacerbates multiple plasma instabilities. To reduce the risk of disrupting operations, we leverage advances in Scientific Machine Learning (SciML) to combine physics with data-driven models, developing a neural state-space model (NSSM) that predicts plasma dynamics during Tokamak à Configuration Variable (TCV) rampdowns. The NSSM efficiently learns dynamics from a modest dataset of 311 pulses with only five pulses in a reactor-relevant high-performance regime. The NSSM is parallelized across uncertainties, and reinforcement learning (RL) is applied to design trajectories that avoid instability limits. High-performance experiments at TCV show statistically significant improvements in relevant metrics. A predict-first experiment, increasing plasma current by 20% from baseline, demonstrates the NSSM’s ability to make small extrapolations. The developed approach paves the way for designing tokamak controls with robustness to considerable uncertainty and demonstrates the relevance of SciML for fusion experiments.

Thermodynamic Feedback Mechanisms for Mitigating Polarization in Lithium‐Ion Batteries

AbstractThe performance of lithium‐ion batteries (LIBs) is intrinsically determined by the interplay between the kinetic and thermodynamic processes, which jointly govern the polarization dynamics across multiple scales. Extensive efforts have been directed toward alleviating kinetic limitations, but the essential role of thermodynamic factors, particularly under extreme operating conditions, has been largely overlooked. This oversight has impeded the development of comprehensive design principle for optimizing LIBs. In this study, we systematically investigate the coupled effects between thermodynamics and kinetics using advanced multiphysics simulations. We identify the slope of the equilibrium potential profile as a pivotal thermodynamic parameter. Steeper slopes have been demonstrated to induce stronger negative feedback, thereby effectively mitigating polarization heterogeneity and realizing the consistent electrode utilization. Based on this insight, we propose a design strategy centered on steepening the equilibrium potential to enhance thermodynamic feedback. This approach achieves a remarkable 80% reduction in polarization heterogeneity, significantly improving operational stability. Our work establishes a theoretical framework for polarization dynamics and offers actionable thermodynamic design principles, paves the way for the development of LIBs capable of extreme operating conditions.

Exploring complex phenomena in fluid flow and plasma physics via the Schrödinger-type Maccari system

t2 occupancy as a descriptor for polysulfide conversion on spinel oxides

Photocatalytic Co‐Reduction of CO₂ and Nitrate over Porphyrin Metal–Organic Frameworks: Dual Atomic Active Site and Nanostructure Synergy Enhances C–N Coupling for Urea Production

AbstractThe photocatalytic co‐reduction of NO3− and CO2 (NitRR&CRR) offers a sustainable approach for urea synthesis and environment remediation. However, there is a lack of high‐performance photocatalysts. Herein, we report a nanoflower‐like bimetallic porphyrin metal–organic framework superstructure (Cu‐TCPP(Co)‐NF, TCPP = tetrakis(4‐carboxyphenyl)porphyrin) as an efficient NitRR&CRR photocatalyst for urea production. Systematical investigations have revealed that the Cu and Co centers serve as the active sites for driving NitRR and CRR, respectively. The high pore volumes of nanoflower architecture can improve the light adsorption, promote the reactant enrichment, and intermediate generation. Consequently, the C–N coupling process over Cu‐TCPP(Co)‐NF is facilitated with reduced energy barriers and inhibited side reactions, resulting in an unprecedent urea yield of 2459.8 µg h−1 gcat−1 in the absence of sacrificial agents. This study provides new insights into the design of advanced photocatalysts for urea synthesis.