Fundamental Research最新文献

Understanding thermal transport at the submicron scale is crucial for engineering applications, especially in the thermal management of electronics and tailoring the thermal conductivity of thermoelectric materials. At the submicron scale, the macroscopic heat diffusion equation is no longer valid and the phonon Boltzmann transport equation (BTE) becomes the governing equation for thermal transport. However, previous thermal simulations based on the phonon BTE have two main limitations: relying on empirical parameters and prohibitive computational costs. Therefore, the phonon BTE is commonly used for qualitatively studying the non-Fourier thermal transport phenomena of toy problems. In this work, we demonstrate an ultra-efficient and parameter-free computational method of the phonon BTE to achieve quantitatively accurate thermal simulation for realistic materials and devices. By properly integrating the phonon properties from first-principles calculations, our method does not rely on empirical material properties input. It can be generally applicable for different materials and the predicted results can match well with experimental results. Moreover, by developing a suitable ensemble of advanced numerical algorithms, our method exhibits superior numerical efficiency. The full-scale (from ballistic to diffusive) thermal simulation of a 3-dimensional fin field-effect transistor with 13 million degrees of freedom, which is prohibitive for existing phonon BTE solvers even on supercomputers, can now be completed within two hours on a single personal computer. Our method makes it possible to achieve the predictive design of realistic nanostructures for the desired thermal conductivity. It also enables accurately resolving the temperature profiles at the transistor level, which helps in better understanding the self-heating effect of electronics.

Combining photodynamic therapy (PDT) with chemodynamic therapy (CDT) has been proven to be a promising strategy to improve the treatment efficiency of cancer, because of the synergistic therapeutic effect arising between the two modalities. Herein, we report an inorganic nanoagent based on ternary NiCoTi-layered double hydroxide (NiCoTi-LDH) nanosheets to realize highly efficient photodynamic/chemodynamic synergistic therapy. The NiCoTi-LDH nanosheets exhibit oxygen vacancy-promoted electron-hole separation and photogenerated hole-induced O₂-independent reactive oxygen species (ROS) generation under acidic circumstances, realizing in situ pH-responsive PDT. Moreover, due to the effective conversion between Co³⁺ and Co²⁺ caused by photogenerated electrons, the NiCoTi-LDH nanosheets catalyze the release of hydroxyl radicals (·OH) from H₂O₂ through Fenton reactions, resulting in CDT. Laser irradiation enhances the catalyzed ability of the NiCoTi-LDH nanosheets to promote the ROS generation, resulting in a better performance than TiO₂ nanoparticles at pH 6.5. In vitro and in vivo experimental results show conclusively that NiCoTi-LDH nanosheets plus irradiation lead to efficient cell apoptosis and significant inhibition of tumor growth. This study reports a new pH-responsive inorganic nanoagent with oxygen vacancy-promoted photodynamic/chemodynamic synergistic performance, offering a potentially appealing clinical strategy for selective tumor elimination.

Nitrate products are widely used in manufacturing as crucial raw materials and fertilizers. The traditional nitrate synthesis process involves high energy consumption and emission, thereby opposing the goals of zero-carbon emission and green chemistry. Thus, a sustainable roadmap for nitrate synthesis that uses green energy input, clean N sources, and direct catalytic processes is urgently required (e.g., developing a novel photosynthesis system). Here, we synthesized TiO₂-supported atomically dispersed Cu species for N₂ photofixation to nitrate in a flow reactor. The optimized photocatalyst yielded a high nitrate photosynthesis rate of 0.93 μmol h⁻¹ and selectivity of ∼90%, which is superior to most of the values reported thus far. Further, experimental results and in-situ investigations revealed that the atomically dispersed Cu sites in the as-designed sample significantly enhanced the separation and transfer efficiency of photogenerated carriers, adsorption and activation of reactants, and the formation of chemisorbed NO_x intermediates, thereby realizing the excellent photofixation of N₂ to nitrate.

The COVID-19 pandemic has posed severe threats to global sustainable development. However, a comprehensive quantitative assessment of the impacts of COVID-19 on Sustainable Development Goals (SDGs) is still lacking. This research quantified the post-COVID-19 SDG progress from 2020 to 2024 using projected GDP growth and population and machine learning models including support vector machine, random forest, and extreme gradient boosting. The results show that the overall SDG performance declined by 7.7% in 2020 at the global scale, with 12 socioeconomic SDG performance decreasing by 3.0%–22.3% and 4 environmental SDG performance increasing by 1.6%–9.2%. By 2024, the progress of 12 SDGs will lag behind for one to eight years compared to their pre-COVID-19 trajectories, while extra time will be gained for 4 environment-related SDGs. Furthermore, the pandemic will cause more impacts on countries in emerging markets and developing economies than those on advanced economies, and the latter will recover more quickly to be closer to their pre-COVID-19 trajectories by 2024. Post-COVID-19 economic recovery should emphasize in areas that can help decouple economic growth from negative environmental impacts. The results can help government and non-state stakeholders identify critical areas for targeted policy to resume and speed up the progress to achieve SDGs by 2030.

With the soaring generation of hazardous waste (HW) during industrialization and urbanization, HW illegal dumping continues to be an intractable global issue. Particularly in developing regions with lax regulations, it has become a major source of soil and groundwater contamination. One dominant challenge for HW illegal dumping supervision is the invisibility of dumping sites, which makes HW illegal dumping difficult to be found, thereby causing a long-term adverse impact on the environment. How to utilize the limited historic supervision records to screen the potential dumping sites in the whole region is a key challenge to be addressed. In this study, a novel machine learning model based on the positive-unlabeled (PU) learning algorithm was proposed to resolve this problem through the ensemble method which could iteratively mine the features of limited historic cases. Validation of the random forest-based PU model showed that the predicted top 30% of high-risk areas could cover 68.1% of newly reported cases in the studied region, indicating the reliability of the model prediction. This novel framework will also be promising in other environmental management scenarios to deal with numerous unknown samples based on limited prior experience.

To study the influence of an elbow inlet on the rotating stall characteristics of a waterjet propulsion pump (WJPP), a three-dimensional internal flow field in a WJPP under a straight-pipe inlet and elbow inlet is numerically simulated. By comparing the hydraulic performance of WJPP under the two inlet conditions, the internal relationship between the inlet mode and the flow pattern in the pump is clarified. Based on unsteady pressure fluctuation characteristics and wavelet analysis, the influence of the inlet mode on the rotating stall is revealed, and the stall transient propagation characteristics under critical stall conditions are analyzed. The disturbance effects of the inlet channel geometry disappear under low flow rate conditions, the main disturbance is induced by the high-speed countercurrent, and the flow pattern under the elbow inlet is better than that under the straight-pipe inlet. Under the straight-pipe inlet, the single-stall nucleus in the WJPP temporarily experiences a low-frequency and high-amplitude disturbance, which subsequently transforms into a mode of multi-stall nuclei with high-frequency circumferential disturbance. Under the elbow inlet, the rotating stall always maintains a mode of high-amplitude and low-frequency disturbance, which represents the transient characteristics of a single stall core propagating in the circumferential direction inside the channel. The results of this study have a reference value for structural design optimization in a WJPP.

The increasing emergence of the time-series single-cell RNA sequencing (scRNA-seq) data, inferring developmental trajectory by connecting transcriptome similar cell states (i.e., cell types or clusters) has become a major challenge. Most existing computational methods are designed for individual cells and do not take into account the available time series information. We present IDTI based on the Increment of Diversity for Trajectory Inference, which combines time series information and the minimum increment of diversity method to infer cell state trajectory of time-series scRNA-seq data. We apply IDTI to simulated and three real diverse tissue development datasets, and compare it with six other commonly used trajectory inference methods in terms of topology similarity and branching accuracy. The results have shown that the IDTI method accurately constructs the cell state trajectory without the requirement of starting cells. In the performance test, we further demonstrate that IDTI has the advantages of high accuracy and strong robustness.

Abnormalities in the transition between α-helices and β-sheets (α-β transition) may lead to devastating neurodegenerative diseases, such as Parkinson's syndrome and Alzheimer's disease. Ionic liquids (ILs) are potential drugs for targeted therapies against these diseases because of their excellent bioactivity and designability of ILs. However, the mechanism through which ILs regulate the α-β transition remains unclear. Herein, a combination of GPU-accelerated microsecond molecular dynamics simulations, correlation analysis, and machine learning was used to probe the dynamical α-β transition process induced by ILs of 1-alkyl-3-methylimidazolium chloride ([C_nmim]Cl) and its molecular mechanism. Interestingly, the cation of [C_nmim]⁺ in ILs can spontaneously insert into the peptides as free ions (n ≤ 10) and clusters (n ≥ 11). Such insertion can significantly inhibit the α-β, transition and the inhibiting ability for the clusters is more significant than that of free ions, where [C₁₀mim]⁺ and [C₁₂mim]⁺ can reduce the maximum β-sheet content of the peptide by 18.5% and 44.9%, respectively. Furthermore, the correlation analysis and machine learning method were used to develop a predictive model accounting for the influencing factors on the α-β transition, which could accurately predict the effect of ILs on the α-β transition. Overall, these quantitative results may not only deepen the understanding of the role of ILs in the α-β transition but also guide the development of the IL-based treatments for related diseases.

As the global demand for healthcare services continues to grow, improving the efficiency and effectiveness of the healthcare ecosystem has become a pressing concern. Information systems are transforming the healthcare delivery process, shifting the focus of healthcare services from passive disease treatment to proactive health prevention and the healthcare management model from hospital-centric to patient-centric. This study focuses on reviewing research in IS journals on the topic of e-health and is dedicated to constructing a theoretical model of intelligent health to provide a research basis for future discussions in this field. In addition, as the innovation of intelligent healthcare services has led to changes in its elements (e.g., an increase in the number of stakeholders), there is an urgent need to sort out and analyze the existing research.

In this work, two kinds of primary batteries, both of which included a Zn anode, C rod cathode, copper wire and electrolyte composed of Cd²⁺-contaminated water or soil, were constructed in the first attempt to both remove Cd²⁺ and generate electricity. Unlike traditional technologies such as electrokinetic remediation with high energy consumption, this technology could realize Cd²⁺ migration to aggregation and solidification and generate energy at the same time through simultaneous galvanic reactions. The passive surface of Zn and C was proven via electrochemical measurements to be porous to maintain the relatively active galvanic reactions for continuous Cd²⁺ precipitation. Cd²⁺ RE (removal efficiency) and electricity generation were investigated under different conditions, based on which two empirical models were established to predict them successfully. In soil, KCl was added to desorb Cd²⁺ from soil colloids to promote Cd²⁺ removal. These systems were also proven to remove Cd²⁺ efficiently when their effects on plants, zebrafish, and the soil bacterial community were tested. LEDs could be lit for days by utilizing the electricity produced herein. This work provides a novel, green, and low-cost route to remediate Cd²⁺ contamination and generate electricity simultaneously, which is of extensive practical significance in the environmental and energy fields.