The Innovation最新文献

Research in high-income countries has established the health benefits of physical activity (PA), but evidence from low- and middle-income countries, including China, where PA patterns vary from those in high-income countries, remains limited. Moreover, previous research, mainly focused on specific diseases, failing to fully capture the health impacts of PA. We investigated the associations of PA with 425 distinct diseases and 53 causes of death using data from 511,088 participants aged 30-79 years in the China Kadoorie Biobank. Baseline PA was assessed using a questionnaire between 2004 and 2008, and usual PA levels were estimated using the resurvey data in 2013-2014. Cox regression was employed to estimate the associations between PA and outcomes, adjusting for potential confounders. During a median follow-up time of 12 years, 722,183 incident events and 39,320 deaths were recorded across 18 chapters of the International Classification of Diseases, 10th Revision (ICD-10). Total PA was significantly and inversely associated with incidence risks of 14 ICD-10 chapters, specifically 65 diseases and 19 causes of death, with the highest quintile group of PA showing a 14% lower disease incidence and 40% lower all-cause mortality compared with the lowest group. Of these diseases, 54 were not highlighted in World Health Organization PA guidelines. Dose-response analyses revealed L-shaped associations for most PA types, except moderate-to-vigorous intensity PA, which showed a U-shaped relationship. In this population, physical inactivity accounted for 12.8% of PA-related deaths. The findings underscore the broad health benefits of PA across a variety of body systems and the significant disease burden due to inactivity in China, highlighting the urgent need for PA promotion.

Ionomers play a vital role in the preparation of electrodes for CO₂ electroreduction, and controlling the ionomer configuration on the catalyst surface offers an effective strategy for adjusting the surface microenvironment of the electrode, thereby influencing the distribution of CO₂ electroreduction products. In this study, we demonstrate that Nafion, a commonly used ionomer, exhibits distinct aggregation behaviors in solvents with different dielectric constant (ε) values. These differences in aggregation result in varied Nafion arrangements on the catalyst surface, which in turn affect the binding of ∗CO and ∗H intermediates, enabling control over product distribution. For example, over a Cu nanosheet catalyst at 800 mA cm^-2, the Faradaic efficiency for multicarbon products increases from 67.5% to 90.5% simply by changing the dispersion solvent from low-ε dimethyl sulfoxide to moderate-ε isopropanol. This work introduces a novel approach for fine-tuning CO₂ electroreduction product distribution through manipulation of the dispersion solvent without requiring modifications to the catalyst or ionomer.

Ultralow-power non-volatile memristors are key elements in electronics. Generally, power reduction of memristors compromises data retention, a challenge known as the "power-retention dilemma," due to the stochastic formation of conductive dendrites in resistive-switching materials. Here, we report the results of conductive dendrite engineering in single-crystalline two-dimensional (2D) dielectrics in which directional control of filamentary distribution is possible. We find that the single-vacancy density (n_SV) of single-crystalline hexagonal boron nitride (h-BN) plays an essential role in regulating conductive dendrite growth, supported by scanning joule expansion microscopy (SJEM). With optimized n_SV, random dendrite growth is largely limited, and electrons hop between the neighboring Ag nanoclusters in vertical channels. The corresponding model was established to probe the relationship between n_SV and memristor operating voltage. The conductive channel confinement in the vertical orientation contributes to long-retention non-volatile memristors with ultralow switch voltages (set: 26 mV; reset: -135 mV), excellent power efficiency (4 fW standby and a switching energy of 72 pJ) while keeping a high on/off resistance ratio of 10⁸. Even at a record-low compliance current of 10 nA, memristors retains very robust non-volatile, multiple resistive states with an operating voltage less than 120 mV (the per-transition power low as 900 pW).

The aggregation process plays a significant role in regulating the aggregate structures from molecules toward macroscopic photophysical properties. Pyrene (Py), as the simplest dimer candidate, serves as a suitable model for studying the aggregation. Herein, a series of Py-based aggregation-induced emission (AIE) materials have been investigated by clarifying the comprehensive roles of oxygen, substituents, molecular motion, and packing during aggregation, initially realizing the aim of controlling aggregate structures. With a largely planar and conjugated conformation, Py shows anomalous AIE characteristics due to the oxygen quenching at the molecular level but turn-on fluorescence in the aggregate state because of the oxygen isolation. Although introducing substituents induces molecular motion and similarly weakened luminescence in the molecular state, the impact of substituents on the aggregate-state photophysical properties enormously differs, exhibiting from weak blue, strong cyan, and strong green to weak yellow emissions, due to variable aggregate structures. Interestingly, the natural alicycle-substituted Py-dehydroabietylamine (Py-DAA) exhibits both mechanochromism and acidichromism, which can be synergistically applied in dynamic encryption-decryption. This work not only elucidates the unique AIE property of Py for the first time but also clarifies the bridging role of aggregation between single-molecular and aggregate states, achieving preliminary control over the aggregate structures.

Ice, a ubiquitous substance in nature, exhibits diverse forms under varying temperature and pressure conditions. However, our understanding of ice polymorphs remains incomplete. The directional nature of hydrogen bonding and the complexity of the networks they form pose significant challenges to computational studies of ice structures. In this work, we present an extensive exploration of ice polymorphs under pressure conditions ranging from 1 bar to 10 GPa. We employ an advanced crystal-structure-prediction scheme that integrates an evolutionary algorithm, an active-learning deep neural network potential, and molecular dynamics simulations with ab initio accuracy. Among the 131,481 predicted structures, we successfully identify all experimentally known ice phases within the target pressure range, including the particularly challenging ice IV and V. These phases feature highly intricate H-bond networks, which have hindered previous efforts to fully explore ice structures. Additionally, we identify 34 new ice polymorphs that are potential candidates for experimental discovery. Notably, we predict the existence of a new stable ice phase, ice L, within the temperature range of 253-291 K and pressure range of 0.38-0.57 GPa, exhibiting a unique topology unseen in any known crystals. Our findings highlight the potential for experimental discovery of new ice phases. Furthermore, our approach can be applied to other complex systems, particularly those with network structures.