Browse Articles

Copper immobilized MIL-125-NH2 as an efficient nanocatalyst for click reaction and synthesis of benzo[b]pyrans

Robust chirality-induced spin selectivity in topologically chiral molecular knots

Cost-utility and budget impact analysis of desmopressin for treating monosymptomatic nocturnal enuresis in Thai children

Spectrally encoded parallel LiDAR driven by super-bunching light

Trends in problematic gambling among school-aged adolescents based on three Spanish nationwide population-based studies (2018–2023): a gender-based analysis of risk and protective factors

Tuning valley polarization of moiré trapped biexcitons by fine structure occupation in WS2/WSe2 heterostructures

Abstract Two-dimensional transition metal dichalcogenides (TMDs) heterostructures formed moiré superlattices have emerged as a new platform for exploring correlated excitonic states and valleytronic phenomena. Despite the significant progress in moiré-trapped single excitons, the valley polarization and fine structure of moiré-trapped biexcitons remain poorly understood, with the existing studies reporting only limited or zero polarization and lacking insight into the underlying mechanisms. Here, we study the moiré-trapped interlayer biexcitons in WS 2 /WSe 2 heterostructures through power- and temperature-dependent photoluminescence (PL) spectroscopy. We find that the valley polarization of these biexcitons can be effectively tuned, reaching ~ $$55\%$$ 55 % at 120 K. This behavior is attributed to the different occupation of intravalley and intervalley biexcitons within the fine structure, with the intravalley biexcitons playing a dominant role. The power-dependent energy splitting and temperature-dependent polarization trends further confirm the existence of biexciton fine structure and its influence on valley polarization. Furthermore, the experiment revealed a fine structure splitting of 2.77 meV, consistent with theoretical calculations. Our study provides new insight into the rational control of excitonic states in moiré superlattices and establishes a basis for developing advanced valleytronic devices, such as polarization-sensitive photodetectors and quantum light sources.

A hybrid multi-scale indirect vision detector algorithm for heritage building fire detection

Human cortical dynamics reflect graded contributions of local geometry and network topography

Triple-negative complementary metamaterial for ultrasound transmission through elastic barrier

Continental outflow shapes the circum-Antarctic pattern of summertime atmospheric mercury depletion zones

Pyridoxamine inhibits dopamine-induced α-synuclein oligomerization through scavenging neurotoxic dopamine quinone

Inferring fine-grained migration patterns across the United States

Abstract Fine-grained migration data illuminate demographic, environmental, and health phenomena. However, United States migration data have serious drawbacks: public data lack spatial granularity, and higher-resolution proprietary data suffer from multiple biases. To address this, we develop a method that fuses high-resolution proprietary data with coarse Census data to create MIGRATE: annual migration matrices capturing flows between 47.4 billion US Census Block Group pairs—approximately four thousand times the spatial resolution of current public data. Our estimates are highly correlated with external ground-truth datasets and improve accuracy relative to raw proprietary data. We use MIGRATE to analyze national and local migration patterns. Nationally, we document demographic and temporal variation in homophily, upward mobility, and moving distance—for example, rising moves into top-income-quartile block groups and racial disparities in upward mobility. Locally, MIGRATE reveals patterns such as wildfire-driven out-migration that are invisible in coarser previous data. We release MIGRATE as a resource for migration researchers.

An improved steel defect detection model using multi-scale feature fusion based on YOLO-MFD

Non-parasite genome encoded virus-like RNAs reprogram the pathogenicity of human blood flukes

Monitoring of ultra-high performance concrete manufacturing for reproducible quality and waste reduction

Abstract Ultra-high performance concrete (UHPC) combines exceptional strength and durability, yet its industrial production is hampered by batch-to-batch variability that generates costly off-specification waste. Leveraging a 150-batch design-of-experiments dataset based on systematic variations of a single reference UHPC mix, this study takes a holistic view of the UHPC manufacturing chain and quantifies how fluctuations in raw material quality, storage conditions, dosing errors, mixer energy demand, and curing regimes affect the 28-day compressive strength. Ten diverse machine learning algorithms are benchmarked; the best-performing model explains $$\ge$$ 75 % of the strength variance with a prediction error $$\le$$ 10 % under leave-one-out cross-validation. SHapley Additive exPlanations reveal that long-term curing temperature and humidity dominate strength development, followed by ingredient moisture and silica fume impurity. These insights are operationalized in an at-line, operator-in-the-loop recommendation system that explores the curing envelope and proposes end-of-mix, batch-specific adjustments before curing starts. In five validation cases, curing adjustments rescued 5/5 underperforming batches, eliminating 75 L of off-specification UHPC and—considering cement only with 600 kg/ $$\mathrm {m^{3}}$$ and 15 L per batch of UHPC made with white Portland cement—avoided $$\approx$$ 41 kg CO 2 e (cement-only; 0.913 kg CO 2 e/kg, A1–A3). The framework therefore not only elucidates the main sources of UHPC quality inconsistency but also provides a practical, data-driven tool to rescue off-specification products, minimize waste, and cut associated $$\mathrm {CO_2}$$ emissions.

Hidden colour signals as key drivers in the evolution of anti-predator coloration and defensive behaviours in snakes

Effects of artificial tears on ocular surface symptoms and visual task performance in digital device users

Small PdCx interstitial compound for efficient acidic CO2 electroreduction to formic acid

A sensor-fused BIM-based ıntelligent control system for energy-efficient ındoor environmental regulation using deep actor-critic reinforcement learning (DACRL)

Rationally designed Fe-cyclopentadienone with unique orientations for efficient asymmetric hydrogenation of acylsilanes

Abstract Fe-cyclopentadienone complexes have been widely utilized in various hydrogenation and dehydrogenation catalytic processes, yet their applications have largely been restricted to non-asymmetric versions. This limitation is primarily due to the considerable challenge of constructing an efficient chiral environment around the active iron center. In this study, we present a structurally distinctive chiral Fe-cyclopentadienone complex with excellent enantiocontrol capabilities. This new iron complex features bulky side arms oriented downward toward the cyclopentadienone plane, which create an ideal chiral environment in front of the catalytically active iron center. It demonstrates excellent performance in the catalytic asymmetric hydrogenation of acylsilanes, exhibiting both high reactivity and selectivity. The broad substrate scope, encompassing aryl-, alkenyl-, and alkyl-acylsilanes, along with successful gram-scale synthesis, underscores its potential applications in pharmaceutical synthesis. Experimental and DFT studies reveal the structural stability and rigidity of the catalyst during catalytic intervals. Additionally, weak interactions between the catalyst and the silyl group in the substrate play a critical role in achieving efficient enantioselectivity. More importantly, this type of chiral iron complex also shows excellent catalytic reactivity and selectivity for asymmetric transfer hydrogenation, utilizing i -PrOH as the hydrogen source.