Doping strategy in metavalently bonded materials for advancing thermoelectric performance.

Metavalent bonding is a unique bonding mechanism responsible for exceptional properties of materials used in thermoelectric, phase-change, and optoelectronic devices. For thermoelectrics, the desired performance of metavalently bonded materials can be tuned by doping foreign atoms. Incorporating dopants to form solid solutions or second phases is a crucial route to tailor the charge and phonon transport. Yet, it is difficult to predict if dopants will form a secondary phase or a solid solution, which hinders the tailoring of microstructures and material properties. Here, we propose that the solid solution is more easily formed between metavalently bonded solids, while precipitates prefer to exist in systems mixed by metavalently bonded and other bonding mechanisms. We demonstrate this in a metavalently bonded GeTe compound alloyed with different sulfides. We find that S can dissolve in the GeTe matrix when alloyed with metavalently bonded PbS. In contrast, S-rich second phases are omnipresent via alloying with covalently bonded GeS and SnS. Benefiting from the reduced phonon propagation and the optimized electrical transport properties upon doping PbS in GeTe, a high figure-of-merit ZT of 2.2 at 773 K in (Ge0.84Sb0.06Te0.9)(PbSe)0.05(PbS)0.05 is realized. This strategy can be applied to other metavalently bonded materials to design properties beyond thermoelectrics.

Interpretable machine learning model for predicting acute kidney injury in critically ill patients.

BACKGROUND: This study aimed to create a method for promptly predicting acute kidney injury (AKI) in intensive care patients by applying interpretable, explainable artificial intelligence techniques. METHODS: Population data regarding intensive care patients were derived from the Medical Information Mart for Intensive Care IV database from 2008 to 2019. Machine learning (ML) techniques with six methods were created to construct the predicted models for AKI. The performance of each ML model was evaluated by comparing the areas under the curve (AUC). Local Interpretable Model-Agnostic Explanations (LIME) method and Shapley Additive exPlanation values were used to decipher the best model. RESULTS: According to inclusion and exclusion criteria, 53,150 severely sick individuals were included in the present study, of which 42,520 (80%) were assigned to the training group, and 10,630 (20%) were allocated to the validation group. Compared to the other five ML models, the eXtreme Gradient Boosting (XGBoost) model greatly predicted AKI following ICU admission, with an AUC of 0.816. The top four contributing variables of the XGBoost model were SOFA score, weight, mechanical ventilation, and the Simplified Acute Physiology Score II. An AKI and Non-AKI cases were predicted separately using the LIME algorithm. CONCLUSION: Overall, the constructed clinical feature-based ML models are excellent in predicting AKI in intensive care patients. It would be constructive for physicians to provide early support and timely intervention measures to intensive care patients at risk of AKI.

Effects of different assembly strategies on gene annotation in activated sludge.

Activated sludge comprises diverse bacteria, fungi, and other microorganisms, featuring a rich repertoire of genes involved in antibiotic resistance, pollutant degradation, and elemental cycling. In this regard, hybrid assembly technology can revolutionize metagenomics by detecting greater gene diversity in environmental samples. Nonetheless, the optimal utilization and comparability of genomic information between hybrid assembly and short- or long-read technology remain unclear. To address this gap, we compared the performance of the hybrid assembly, short- and long-read technologies, abundance and diversity of annotated genes, and taxonomic diversity by analysing 46, 161, and 45 activated sludge metagenomic datasets, respectively. The results revealed that hybrid assembly technology exhibited the best performance, generating the most contiguous and longest contigs but with a lower proportion of high-quality metagenome-assembled genomes than short-read technology. Compared with short- or long-read technologies, hybrid assembly technology can detect a greater diversity of microbiota and antibiotic resistance genes, as well as a wider range of potential hosts. However, this approach may yield lower gene abundance and pathogen detection. Our study revealed the specific advantages and disadvantages of hybrid assembly and short- and long-read applications in wastewater treatment plants, and our approach could serve as a blueprint to be extended to terrestrial environments.

Combined effects of azoxystrobin and oxytetracycline on rhizosphere microbiota of Arabidopsis thaliana.

The rhizosphere is one of the key determinants of plant health and productivity. Mixtures of pesticides are commonly used in intensified agriculture. However, the combined mechanisms underlying their impacts on soil microbiota remain unknown. The present study revealed that the rhizosphere microbiota was more sensitive to azoxystrobin and oxytetracycline, two commonly used pesticides, than was the microbiota present in bulk soil. Moreover, the rhizosphere microbiota enhanced network complexity and stability and increased carbohydrate metabolism and xenobiotic biodegradation as well as the expression of metabolic genes involved in defence against pesticide stress. Co-exposure to azoxystrobin and oxytetracycline had antagonistic effects on Arabidopsis thaliana growth and soil microbial variation by recruiting organic-degrading bacteria and regulating ABC transporters to reduce pesticide uptake. Our study explored the composition and function of soil microorganisms through amplicon sequencing and metagenomic approaches, providing comprehensive insights into the synergistic effect of plants and rhizosphere microbiota on pesticides and contributing to our understanding of the ecological risks associated with pesticide use.

Synergistically Optimized Thermoelectric and Mechanical Properties of Mg3.2Bi1.5Sb0.5-SiC Composites.

Motivated by the surging demand for low-temperature waste heat harvesting, materials with both prominent thermoelectric and good mechanical properties are preferred in practical applications. In this present work, the composite exploration of Te-doped Mg3.2Bi1.5Sb0.5-x vol % nanosized SiC (x = 0, 0.05, 0.1, 0.2, and 0.5) was carried out, where nanosized SiC is physically dispersed in the matrix in the form of a second phase. SiC second phase compositing further optimized the matrix carrier concentration, resulting in a higher power factor in the service temperature range (the highest value from 28.9 to 31.7 µW cm-1 K-2), and the (ZT)ave from 0.91 to 0.96 compared with the matrix sample. In addition, the SiC second phase effectively enhanced the mechanical properties of composite materials, including flexural strength, microhardness, and modulus. Because of the simultaneous optimization of thermoelectric and mechanical properties, the overall performance of Te-doped Mg3.2Bi1.5Sb0.5-0.05 vol % SiC composite is leveraged to meet special requirements of power generation. It is expected that the addition of SiC should be broadly applicable to address the physical performance in other thermoelectric systems.

Mechanism of Thermoelectric Performance Enhancement in CaMg2 Bi2 -Based Materials with Different Cation Site Doping.

Chemical bonds determine electron and phonon transport in solids. Tailoring chemical bonding in thermoelectric materials causes desirable or compromise thermoelectric transport properties. In this work, taking an example of CaMg2 Bi2 with covalent and ionic bonds, density functional theory calculations uncover that element Zn, respectively, replacing Ca and Mg sites cause the weakness of ionic and covalent bonding. Electrically, Zn doping at both Ca and Mg sites increases carrier concentration, while the former leads to higher carrier concentration than that of the latter because of its lower vacancy formation energy. Both doping types increase density-of-state effective mass but their mechanisms are different. The Zn doping Ca site induces resonance level in valence band and Zn doping Mg site promotes orbital alignment. Thermally, point defect and the change of phonon dispersion introduced by doping result in pronounced reduction of lattice thermal conductivity. Finally, combining with the further increase of carrier concentration caused by Na doping and the modulation of band structure and the decrease of lattice thermal conductivity caused by Ba doping, a high figure-of-merit ZT of 1.1 at 823 K in Zn doping Ca sample is realized, which is competitive in 1-2-2 Zintl phase thermoelectric systems.

Digital infrastructure construction, carbon total factor productivity, and carbon rebound effect.

Digital infrastructure construction (DIC) and low-carbon transformation are important engines and objective functions of the superior economic development, and the synergistic drive between the two is essential to achieving lasting economic development. Based on the panel data of 279 cities in China between 2007 and 2019, the econometric model system is used to explore the impact mechanism of DIC on carbon total factor productivity (CTFP), and the impact of DIC on carbon rebound effect (CRE) is further studied. Research findings that, first, the expansion of DIC has a nonlinear effect on CTFP, with a U-shaped link between the two; multiple robustness tests confirm that this is still true. Second, DIC and optimization of the energy consumption structure in a "U" curve relationship, and the major strategy for increasing CTFP is to reduce energy consumption, while industrial structure optimization and technical innovation have less of a intermediary effect. Third, further analysis reveals that there is a "U" shaped nonlinear connection between the DIC and the CRE, and energy savings and emission reductions in the later stages of DIC fall short of expectations. The current DIC is still dominated by episodic expansion. The findings of the study can better enhance CTFP, curb the CRE, put a limit on total carbon emissions and accelerate the decoupling of economic growth from carbon emissions.

Breaking the Minimum Limit of Thermal Conductivity of Mg3 Sb2 Thermoelectric Mediated by Chemical Alloying Induced Lattice Instability.

Thermal properties strongly affect the applications of functional materials, such as thermal management, thermal barrier coatings, and thermoelectrics. Thermoelectric (TE) materials must have a low lattice thermal conductivity to maintain a temperature gradient to generate the voltage. Traditional strategies for minimizing the lattice thermal conductivity mainly rely on introduced multiscale defects to suppress the propagation of phonons. Here, the origin of the anomalously low lattice thermal conductivity is uncovered in Cd-alloyed Mg3 Sb2 Zintl compounds through complementary bonding analysis. First, the weakened chemical bonds and the lattice instability induced by the antibonding states of 5p-4d levels between Sb and Cd triggered giant anharmonicity and consequently increased the phonon scattering. Moreover, the bond heterogeneity also augmented Umklapp phonon scatterings. Second, the weakened bonds and heavy element alloying softened the phonon mode and significantly decreased the group velocity. Thus, an ultralow lattice thermal conductivity of ≈0.33 W m-1 K-1 at 773 K is obtained, which is even lower than the predicated minimum value. Eventually, Na0.01 Mg1.7 Cd1.25 Sb2 displays a high ZT of ≈0.76 at 773 K, competitive with most of the reported values. Based on the complementary bonding analysis, the work provides new means to control thermal transport properties through balancing the lattice stability and instability.

Design of N-Type Textured Bi2 Te3 with Robust Mechanical Properties for Thermoelectric Micro-Refrigeration Application.

Thermoelectric refrigeration is one of the mature techniques used for cooling applications, with the great advantage of miniaturization over traditional compression refrigeration. Due to the anisotropic thermoelectric properties of n-type bismuth telluride (Bi2 Te3 ) alloys, these two common methods, including the liquid phase hot deformation (LPHD) and traditional hot forging (HF) methods, are of considerable importance for texture engineering to enhance performance. However, their effects on thermoelectric and mechanical properties are still controversial and not clear yet. Moreover, there has been little documentation of mechanical properties related to micro-refrigeration applications. In this work, the above-mentioned methods are separately employed to control the macroscopic grain orientation for bulk n-type Bi2 Te3 samples. The HF method enabled the stabilization of both composition and carrier concentration, therefore yielding a higher quality factor to compare with that of LPHD samples, supported by DFT calculations. In addition to superior thermoelectric performance, the HF sample also exhibited robust mechanical properties due to the presence of nano-scale distortion and dense dislocation, which is the prerequisite for realizing ultra-precision machining. This work helps to pave the way for the utilization of n-type Bi2 Te3 for commercial micro-refrigeration applications.

Can local environmental constraints improve enterprise's green innovation quality? Evidence from Chinese-listed firms.

The target responsibility system of environmental protection is one of the vital channels to achieve a win-win situation for both economic development and environmental protection. Comprehensively investigating how local environmental target constraints drive enterprises to improve their green innovation quality is of enormous theoretical and practical significance for optimizing the implementation effect of environmental target constraints policies and boosting enterprises' green and high-quality development. We empirically examine the mechanism of the impact of the intensity of different types of local environmental target constraints on the quality of corporate green innovation and the nonlinear relationship between them through innovatively constructing indicators of local environmental target constraint intensity and utilizing the knowledge width of green patents of listed companies as a proxy variable for enterprise green innovation quality. First, the strength of indirect environmental target constraints has a significant positive effect on the quality of green innovation, but further nonlinear characteristics reveal a significant inverse U-shaped relationship between them. Second, indirect environmental target constraint intensity has an inverted U-shaped trend in increasing the intensity of environmental regulation and influencing the digitalization of enterprises, which in turn forms an inverted U-shaped relationship with the quality of green innovation. Third, indirect environmental target constraint intensity works better in areas with policies prioritizing city over province, with mayors less than 57 years old, and for enterprises in technology-intensive industries.

The Spatial Effect of Air Pollution Governance on Labor Productivity: Evidence from 262 Chinese Cities.

According to epidemiological studies, air pollution can increase the rate of medical visits and morbidity. Empirical studies have also shown that air pollutants are toxic to animals. Using data from 262 Chinese cities for the period 2005 to 2018, this study systematically investigated the spatial spillover effect and transmission mechanism of air pollution governance on urban labor productivity. In this study, we also explored the changing trend of labor productivity in China from a dynamic perspective. Additionally, we selected the air flow coefficient and environmental regulations as two instrumental variables of air pollution governance to effectively alleviate endogenous problems existing in the model. The results show that air pollution governance plays a significant role in promoting the improvement of labor productivity. The effect of air pollution governance on labor productivity in eastern cities is better than that in central and western cities, and its effect in developed cities is better than that in undeveloped cities. With the increased intensity of air pollution governance, its effect on labor productivity is also strengthened. Urban innovation capacity and residents' health are important channels for air pollution governance in the promotion of labor productivity. Finally, this study proposes policy recommendations, such as implementing a joint prevention and governance mechanism, as well as improving air pollution prevention and government regulations.

Bi0.33(Bi6S9)Br compositing in Bi2S3 bulk materials forwards high thermoelectric properties.

The hexagonal Bi0.33(Bi6S9)Br intermediate was incorporated to enhance the thermoelectric properties of Bi2S3 by a facile synthesis process. As a result of the increase of carrier concentration caused by Br diffusion doping and the enhancement of phonon scattering caused by pores, point defects, and secondary phase interfaces, a maximum ZT value of 0.64 was achieved at 773 K in Bi2S3 + 5% Bi0.33(Bi6S9)Br. This study provides a strategy for achieving Br doping in the Bi2S3 system by adding the Bi0.33(Bi6S9)Br intermediate alloy, while the nanostructure was maintained in the matrix, which may be also suitable for other thermoelectric materials to obtain higher performance.

The Effect of Environmental Policy Uncertainty on Enterprises' Pollution Emissions: Evidence from Chinese Industrial Enterprise.

In response to the global call for emission reduction, China has assumed international responsibility for energy conservation and emission reduction by enacting several environmental policies to save energy and reduce consumption. However, it is debatable whether the increased uncertainty in environmental policies negatively affects firms' emission reduction. Few studies have examined this relationship based on micro-level data. Therefore, this study constructs a theoretical framework of environmental policy uncertainty affecting firms' pollution emissions. Based on comprehensive data from the Chinese Industrial Enterprise Database, the Chinese Industrial Enterprise Pollution Emission Database, and the Chinese Patent Database from 2002 to 2014, we empirically analyzed the impact of environmental policy uncertainty on firms' pollution emissions. The results show that (1) environmental policy uncertainty significantly aggravates the pollution emission intensity of industrial enterprises; (2) environmental policy uncertainty inhibits the improvement of enterprises' innovation capacity, reduces their human capital stock and foreign investment, and aggravates their pollution emission; (3) environmental policy uncertainty has significant heterogeneity on enterprise pollution emissions, that is, environmental policy uncertainty has a greater impact on non-export enterprises, large enterprises, young enterprises, capital-intensive enterprises, state-owned enterprises, and enterprises in polluting industries and central regions. This study provides a useful reference for the improvement of environmental policy and the green transformation of enterprises.

Tunable Electrical Conductivity and Simultaneously Enhanced Thermoelectric and Mechanical Properties in n-type Bi2 Te3.

The recent growing energy crisis draws considerable attention to high-performance thermoelectric materials. n-type bismuth telluride is still irreplaceable at near room temperature for commercial application, and therefore, is worthy of further investigation. In this work, nanostructured Bi2 Te3 polycrystalline materials with highly enhanced thermoelectric properties are obtained by alkali metal Na solid solution. Na is chosen as the cation site dopant for n-type polycrystalline Bi2 Te3 . Na enters the Bi site, introducing holes in the Bi2 Te3 matrix and rendering the electrical conductivity tunable from 300 to 1800 Scm-1 . The solid solution limit of Na in Bi2 Te3 exceeds 0.3 wt%. Owing to the effective solid solution, the Fermi level of Bi2 Te3 is properly regulated, leading to an improved Seebeck coefficient. In addition, the scattering of both charge carriers and phonons is modulated, which ensured a high-power factor and low lattice thermal conductivity. Benefitting from the synergistic optimization of both electrical and thermal transport properties, a maximum figure of merit (ZT) of 1.03 is achieved at 303 K when the doping content is 0.25 wt%, which is 70% higher than that of the pristine sample. This work disclosed an effective strategy for enhancing the performance of n-type bismuth telluride-based alloy materials.

Mediating Point Defects Endows n-Type Bi2 Te3 with High Thermoelectric Performance and Superior Mechanical Robustness for Power Generation Application.

Bi2 Te3 -related alloys dominate the commercial thermoelectric market, but the layered crystal structure leads to the dissociation and intrinsic brittle fracture, especially for single crystals that may worsen the practical efficiency. In this work, point defect configuration by S/Te/I defects engineering is engaged to boost thermoelectric and mechanical properties of n-type Bi2 Te3 alloy, which, coupled with p-type BiSbTe, shows a competitive conversion efficiency for the fabricated module. First, as S alloying suppresses the intrinsic B i T e , antisite defects and forms a donor-like effect, electronic transport properties are optimized, associated with the decreased thermal conductivity due to the point defect scattering. The periodide compound TeI4 is afterward adopted to further tune carrier concentration for the realization of an optimal ZT. Finally, an advanced average ZT of 1.05 with ultra-high compressive strength of 230 MPa is achieved for Bi2 Te2.9 S0.1 (TeI4 )0.0012 . Based on this optimum composition, a fabricated 17-pair module demonstrates a maximum conversion efficiency of 5.37% under the temperature difference of 250 K, rivaling the current state-of-the-art Bi2 Te3 modules. This work reveals the novel mechanism of point defect reconfiguration in synergistic enhancement of thermoelectric and mechanical properties for durably commercial application, which may be applicable to other thermoelectric systems.

Simultaneous determination of penthiopyrad enantiomers and its metabolite in vegetables, fruits, and cereals using ultra-high performance liquid chromatography-tandem mass spectrometry.

Penthiopyrad is a novel succinate dehydrogenase inhibitor that has one chiral center and exists a metabolite, 1-methyl-3-trifluoromethyl-1H-pyrazole-4-carboxamide in its residue definition. An efficient analytical method for the simultaneous determination of penthiopyrad enantiomers and its metabolite in eight matrices were developed using modified quick, easy, cheap, effective, rugged, safe method, coupled with chiral stationary phase and ultra-high performance liquid chromatography-tandem mass spectrometry. The absolute configuration of penthiopyrad enantiomers was confirmed by polarimetry and electronic circular dichroism. Eight polysaccharide-based chiral stationary phases were evaluated in terms of the enantioseparation of penthiopyrad and separation-related factors (the mobile phase, flow rate and the column temperature) were optimized. To obtain an optimal purification, different sorbent combinations were assessed. The linearities of this method were acceptable in the range of 0.005 to 1 mg/L with R2  > 0.998, while the limits of detection and quantification were 0.0015 mg/kg and 0.01 mg/kg for two enantiomers and its metabolite. The average recoveries of R-(-)-penthiopyrad, S-(+)-penthiopyrad and the metabolite ranged from 75.4 to 109.1, 69.5 to 112.8, and 70.0 to 108.5%, respectively. The intra-day and inter-day relative standard deviations were less than 18.8%. The analytical method was accurate and convenient, which can support their further research on stereoselective degradation, residual monitoring and risk assessment.

Ternary Ag2Se1-xTex: A Near-Room-Temperature Thermoelectric Material with a Potentially High Figure of Merit.

Discovering high-performance near-room-temperature thermoelectric materials is extremely imperative to widen the practical application in thermoelectric power generation and refrigeration. Here, ternary Ag2Se1-xTex (x = 0.1, 0.2, 0.3, 0.4, and 0.5) materials are prepared via the wet-mechanical alloying and spark plasma sintering process to investigate their near-room-temperature thermoelectric properties. From density functional theory calculation and single-parabolic-band modeling study, we found that the reduced contribution of Se 4p orbitals to the total density of states decreases the carrier effective mass with increasing Te content, which should enhance the theoretically maximum zT. These calculation results are also verified by the experimental results. Meanwhile, complex microstructures including dislocations, nanograins, high-density boundaries, TeSe substitution, lattice distortions, and localized strain have been observed in ternary Ag2Se1-xTex. These complex microstructures strengthen phonon scattering and in turn lead to ultralow lattice thermal conductivity in the range of 0.21-0.31 W m-1 K-1 in ternary Ag2Se1-xTex at 300 K. Although the increased deformation potential suppresses the carrier mobility, benefiting from the engineered band structures and ultralow lattice thermal conductivity, a high zT of >1 can be potentially obtained in the ternary Ag2Se1-xTex with appropriate carrier concentration. This study indicates that ternary Ag2Se1-xTex is a promising candidate for near-room-temperature thermoelectric applications.

Highly Enhanced Thermoelectric and Mechanical Properties of Bi-Sb-Te Compounds by Carrier Modulation and Microstructure Adjustment.

Bi0.42Sb1.58Te3 + x wt % Cu1.8S (x = 0, 0.03, 0.05, and 0.1) bulk materials with enhanced thermoelectric and mechanical properties were fabricated by a solid-state reaction and spark plasma sintering. The thermoelectric properties, such as electrical transport properties and thermal conductivity, are highly dependent on the Cu1.8S content. The highest value of ZT obtained for Bi0.42Sb1.58Te3 with 0.05 wt % Cu1.8S is 1.23 at 373 K, and an optimistic average ZT of 1.2 is achieved at temperatures in the range of 323-448 K, which is 34% higher than that of the pristine sample. The highly enhanced ZT of the doped sample is attributed to the increased electrical conductivity and reduced lattice thermal conductivity caused by the effective element doping and the multiscale phonon scattering by quantities of point defects, twin boundaries, and nanopores. Further, the hardness obtained for this sample is 1.02 GPa, which is increased by 16% in comparison with that of the pristine sample. The conversion efficiency of the doped sample is also significantly higher than that of the pristine sample. Therefore, Cu1.8S is considered to be a promising dopant for enhancing the thermoelectric and mechanical properties of Bi-Sb-Te-based thermoelectric materials.

Excellent thermoelectric performance achieved in Bi2Te3/Bi2S3@Bi nanocomposites.

A Bi2Te3/Bi2S3@Bi nanocomposite with a network microstructure was successfully synthesized via a hydrothermal method and spark plasma sintering. This composite was constructed from Bi2Te3 nanoparticles and Bi2S3@Bi nanowires, and its network structure is beneficial for obtaining excellent thermoelectric performance. A ZT peak of 1.2 at 450 K was realized for the nanocomposite sample.

MultiBench: Multiscale Benchmarks for Multimodal Representation Learning.

Learning multimodal representations involves integrating information from multiple heterogeneous sources of data. It is a challenging yet crucial area with numerous real-world applications in multimedia, affective computing, robotics, finance, human-computer interaction, and healthcare. Unfortunately, multimodal research has seen limited resources to study (1) generalization across domains and modalities, (2) complexity during training and inference, and (3) robustness to noisy and missing modalities. In order to accelerate progress towards understudied modalities and tasks while ensuring real-world robustness, we release MultiBench, a systematic and unified large-scale benchmark for multimodal learning spanning 15 datasets, 10 modalities, 20 prediction tasks, and 6 research areas. MultiBench provides an automated end-to-end machine learning pipeline that simplifies and standardizes data loading, experimental setup, and model evaluation. To enable holistic evaluation, MultiBench offers a comprehensive methodology to assess (1) generalization, (2) time and space complexity, and (3) modality robustness. MultiBench introduces impactful challenges for future research, including scalability to large-scale multimodal datasets and robustness to realistic imperfections. To accompany this benchmark, we also provide a standardized implementation of 20 core approaches in multimodal learning spanning innovations in fusion paradigms, optimization objectives, and training approaches. Simply applying methods proposed in different research areas can improve the state-of-the-art performance on 9/15 datasets. Therefore, MultiBench presents a milestone in unifying disjoint efforts in multimodal machine learning research and paves the way towards a better understanding of the capabilities and limitations of multimodal models, all the while ensuring ease of use, accessibility, and reproducibility. MultiBench, our standardized implementations, and leaderboards are publicly available, will be regularly updated, and welcomes inputs from the community.