Plasticity in single-crystalline Mg3Bi2 thermoelectric material.

Most of the state-of-the-art thermoelectric materials are inorganic semiconductors. Owing to the directional covalent bonding, they usually show limited plasticity at room temperature1,2, for example, with a tensile strain of less than five per cent. Here we discover that single-crystalline Mg3Bi2 shows a room-temperature tensile strain of up to 100 per cent when the tension is applied along the (0001) plane (that is, the ab plane). Such a value is at least one order of magnitude higher than that of traditional thermoelectric materials and outperforms many metals that crystallize in a similar structure. Experimentally, slip bands and dislocations are identified in the deformed Mg3Bi2, indicating the gliding of dislocations as the microscopic mechanism of plastic deformation. Analysis of chemical bonding reveals multiple planes with low slipping barrier energy, suggesting the existence of several slip systems in Mg3Bi2. In addition, continuous dynamic bonding during the slipping process prevents the cleavage of the atomic plane, thus sustaining a large plastic deformation. Importantly, the tellurium-doped single-crystalline Mg3Bi2 shows a power factor of about 55 microwatts per centimetre per kelvin squared and a figure of merit of about 0.65 at room temperature along the ab plane, which outperforms the existing ductile thermoelectric materials3,4.

Optical modification of nonlinear crystals for quasi-parametric chirped-pulse amplification.

Quasi-parametric chirped-pulse amplification (QPCPA), which features a theoretical peak power much higher than those obtained with Ti:sapphire laser or optical parametric chirped-pulse amplification, is promising for future ultra-intense lasers. The doped rare-earth ion used for idler dissipation is critical for effective QPCPA, but is usually not compatible with traditional crystals. Thus far, only one dissipative crystal of Sm3+-doped yttrium calcium oxyborate has been grown and applied. Here we introduce optical means to modify traditional crystals for QPCPA applications. We theoretically demonstrate two dissipation schemes by idler frequency doubling and sum-frequency generation with an additional laser. In contrast to absorption dissipation, the proposed nonlinear dissipations ensure not only high signal efficiency but also high small-signal gain. The demonstrated ability to optically modify crystals will facilitate the wide application of QPCPA.

Bubbling resilient 3D free-standing nanoporous graphene with an encapsulated multicomponent nano-alloy for enhanced electrocatalysis.

The design and synthesis of highly durable and active electrocatalysts are crucial for improving the hydrogen evolution reaction (HER) and oxygen reduction reaction (ORR) in proton exchange membrane fuel cells (PEMFCs). In this work, we present a novel dealloyed nanoporous PtCuNiCoMn multicomponent alloy with ligaments/pores ranging from 2-3 nm, which is in situ encapsulated in a three-dimensional, free-standing nanoporous nanotubular graphene network featuring a pore/tube diameter of ∼200 to 300 nm. This method allows precise control over the noble metal loading and alloy composition while preventing noble metal loss throughout the preparation process. The innovative bimodal nanoporous graphene/alloy structure, coupled with an open 3D spongy morphology, and optimized surface Pt electronic structure through multicomponent interaction, significantly enhances the activity for the HER/ORR, outperforming commercial Pt/C. Moreover, this design addresses the issues of Pt nanoparticle aggregation and detachment from carbon supports that typically exist in Pt/C-type catalysts, thereby substantially improving the catalytic durability, even under intense gas bubbling conditions.

Optimizing the cell compatibility and mechanical properties in TiZrNbTa medium-entropy alloy/ß-Ti composites through phase transformation.

Medium-entropy alloys (MEAs) typically exhibit outstanding mechanical properties, but their high Young's modulus results in restricted clinical applications. Mismatched Young's modulus between implant materials and human bones can lead to "stress shielding" effects, leading to implant failure. In contrast, ß-Ti alloys demonstrate a lower Young's modulus compared to MEAs, albeit with lower strength. In the present study, based on the bimodal grain size distribution (BGSD) strategy, a series of high-performance TiZrNbTa/Ti composites are obtained by combining TiZrNbTa MEA powders with nano-scale grain sizes and commercially pure Ti (CP-Ti) powders with micro-scale grain sizes. Concurrently, Zr, Nb, and Ta that are ß-Ti stabilizer elements diffuse into Ti, inducing an isomorphous transformation in Ti from the high Young's modulus α-Ti phase to the low Young's modulus ß-Ti phase at room temperature, optimizing the mechanical biocompatibility. The TiZrNbTa/ß-Ti composite demonstrates a yield strength of 1490 ± 83 MPa, ductility of 20.7 % ± 2.9 %, and Young's modulus of 87.6 ± 1.6 GPa. Notably, the yield strength of the TiZrNbTa/ß-Ti composite surpasses that of sintered CP-Ti by 2.6-fold, and its ductility outperforms TiZrNbTa MEA by 2.3-fold. The Young's modulus of the TiZrNbTa/ß-Ti composite is reduced by 28 % and 36 % compared to sintered CP-Ti and TiZrNbTa MEA, respectively. Additionally, it demonstrates superior biocompatibility compared to CP-Ti plate, sintered CP-Ti, and TiZrNbTa MEA. With a good combination of mechanical properties and biocompatibility, the TiZrNbTa/ß-Ti composite exhibits significant potential for clinical applications as metallic biomaterials. STATEMENT OF SIGNIFICANCE: This work combines TiZrNbTa MEA with nano-grains and commercially pure Ti with micro-grains to fabricate a TiZrNbTa/ß-Ti composite with bimodal grain-size, which achieves a yield strength of 1490 ± 83 MPa and a ductility of 20.7 % ± 2.9 %. Adhering to the ISO 10993-5 standard, the TiZrNbTa/ß-Ti composite qualifies as a non-cytotoxic material, achieving a Class 0 cytotoxicity rating and demonstrating outstanding biocompatibility akin to commercially pure Ti. Drawing on element diffusion, Zr, Nb, and Ta serve not only as solvent atoms to achieve solid-solution strengthening but also as stabilizers for the transformation of the ß-Ti crystal structure. This work offers a novel avenue for designing advanced biomedical Ti alloys with elevated strength and plasticity alongside a reduced Young's modulus.

A Conceptual Framework to Understand the Self-Assembly of Chemically Active Colloids.

Colloids that generate chemicals, or "chemically active colloids", can interact with their neighbors and generate patterns via forces arising from such chemical gradients. Examples of such assemblies of chemically active colloids are abundant in the literature, but a unified theoretical framework is needed to rationalize the scattered results. Combining experiments, theory, Brownian dynamics, and finite element simulations, we present here a conceptual framework for understanding how immotile, yet chemically active, colloids assemble. This framework is based on the principle of ionic diffusiophoresis and diffusioosmosis and predicts that a chemically active colloid interacts with its neighbors through short- and long-range interactions that can be either repulsive or attractive, depending on the relative diffusivity of the released cations and anions, and the relative zeta potential of a colloidal particle and the planar surface on which it resides. As a result, 4 types of pairwise interactions arise, leading to 4 different types of colloidal assemblies with distinct patterns. Using short-range attraction and long-range attraction (SALR) systems as an example, we show quantitative agreement between the framework and experiments. The framework is then applied to rationalize a wide range of patterns assembled from chemically active colloids in the literature exhibiting other types of pairwise interactions. In addition, the framework can predict what the assembly looks like with minimal experimental information and help infer ionic diffusivity and zeta potential values in systems where these values are inaccessible. Our results represent a solid step toward building a complete theory for understanding and controlling chemically active colloids, from the molecular level to their mesoscopic superstructures and ultimately to the macroscopic properties of the assembled materials.

Deep optimization of water quality index and positive matrix factorization models for water quality evaluation and pollution source apportionment using a random forest model.

Effective evaluation of water quality and accurate quantification of pollution sources are essential for the sustainable use of water resources. Although water quality index (WQI) and positive matrix factorization (PMF) models have been proven to be applicable for surface water quality assessments and pollution source apportionments, these models still have potential for further development in today's data-driven, rapidly evolving technological era. This study coupled a machine learning technique, the random forest model, with WQI and PMF models to enhance their ability to analyze water pollution issues. Monitoring data of 12 water quality indicators from six sites along the Minjiang River from 2015 to 2020 were used to build a WQI model for determining the spatiotemporal water quality characteristics. Then, coupled with the random forest model, the importance of 12 indicators relative to the WQI was assessed. The total phosphorus (TP), total nitrogen (TN), chemical oxygen demand (CODCr), dissolved oxygen (DO), and five-day biochemical oxygen demand (BOD5) were identified as the top five significant parameters influencing water quality in the region. The improved WQI model constructed based on key parameters enabled high-precision (R2 = 0.9696) water quality prediction. Furthermore, the feature importance of the indicators was used as weights to adjust the results of the PMF model, allowing for a more reasonable pollutant source apportionment and revealing potential driving factors of variations in water quality. The final contributions of pollution sources in descending order were agricultural activities (30.26%), domestic sewage (29.07%), industrial wastewater (26.25%), seasonal factors (6.45%), soil erosion (6.19%), and unidentified sources (1.78%). This study provides a new perspective for a comprehensive understanding of the water pollution characteristics of rivers, and offers valuable references for the development of targeted strategies for water quality improvement.

Nearly diffraction-limited picosecond pulse amplification from LMA fluoride fiber at 2.8 µm.

We demonstrated the generation of a nearly diffraction-limited picosecond pulse from a large-mode-area (LMA) fluoride fiber amplifier. Seeded with a mode-locked fiber oscillator at 2.8 µm, the LMA Er:ZBLAN fiber amplifier delivered the pulse of 16 µJ with a duration of 70 ps at 5 kHz. The nearly diffraction-limited beam was obtained from the 50 µm LMA fiber using the fundamental mode excitation technique, with a measured M2 value of 1.25 for x axis and 1.27 for y axis, respectively. This high-beam-quality high-energy picosecond fiber-based system of 2.8 µm exhibits a great potential in the high-precision biomaterial processing.

Tractography-based automated identification of the retinogeniculate visual pathway with novel microstructure-informed supervised contrastive learning.

The retinogeniculate visual pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus. Identification and visualization of the RGVP are important in studying the anatomy of the visual system and can inform the treatment of related brain diseases. Diffusion MRI (dMRI) tractography is an advanced imaging method that uniquely enables in vivo mapping of the 3D trajectory of the RGVP. Currently, identification of the RGVP from tractography data relies on expert (manual) selection of tractography streamlines, which is time-consuming, has high clinical and expert labor costs, and is affected by inter-observer variability. In this paper, we present a novel deep learning framework, DeepRGVP , to enable fast and accurate identification of the RGVP from dMRI tractography data. We design a novel microstructure-informed supervised contrastive learning method that leverages both streamline label and tissue microstructure information to determine positive and negative pairs. We propose a simple and successful streamline-level data augmentation method to address highly imbalanced training data, where the number of RGVP streamlines is much lower than that of non-RGVP streamlines. We perform comparisons with several state-of-the-art deep learning methods that were designed for tractography parcellation, and we show superior RGVP identification results using DeepRGVP. In addition, we demonstrate a good generalizability of DeepRGVP to dMRI tractography data from neurosurgical patients with pituitary tumors and we show DeepRGVP can successfully identify RGVPs despite the effect of lesions affecting the RGVPs. Overall, our study shows the high potential of using deep learning to automatically identify the RGVP.

Cycling Reconstructed Hierarchical Nanoporous High-Entropy Oxides with Continuously Increasing Capacity for Li Storage.

High-entropy oxides (HEOs) have been showing great promise in a wide range of applications. There remains a lack of clarity regarding the influence of nanostructure and composition on their Li storage performance. Herein, a dealloying technique to synthesize hierarchical nanoporous HEOs with tunable compositions is employed. Building upon the extensively studied quinary AlFeNiCrMnOx , an additional element (Co, V, Ti, or Cu) is introduced to create senary HEOs, allowing for investigation of the impact of the added component on Li storage performance. With higher specific surface areas and oxygen vacancy concentrations, all their HEOs exhibit high Li storage performances. Remarkably, the senary HEO with the addition of V (AlNiFeCrMnVOx ) achieves an impressive capacity of 730.2 mAh g-1 at 2.0 A g-1 , which surpasses all reported performance of HEOs. This result demonstrates the synergistic interaction of the six elements in one HEO nanostructure. Additionally, the battery cycling-induced reconstruction and cation diffusion in the HEOs is uncovered, which results in an initial capacity decrease followed by a subsequent continuous capacity increase and enhanced Li ion diffusion. The results highlight the crucial roles played by both nanoporous structure design and composition optimization in enhancing Li storage of HEOs.

Different Results from Varied Angles: The Positive Impact of Work Connectivity Behavior After-Hours on Work Engagement.

With the development of communication technology and the COVID-19 pandemic, it has become increasingly common for employees to maintain work connectivity after-hours, which has a significant impact on their psychological state at work. However, most of the existing studies have not reached a consensus on the impact of work connectivity behavior after-hours on employees' psychological state at work, and the existing studies have led to theoretical and practical disagreements. Based on the Job Demands-Resources model, we built a two-path model of work autonomy and emotional exhaustion to explore the impact of work connectivity behavior after-hours on work engagement. In addition, we compared the differences between different workplace statuses (managers and ordinary employees). Through surveys and analyses of 257 employees, the results show that work connectivity behavior after-hours positively impacts employees' work engagement by increasing managers' work autonomy and reducing ordinary employees' emotional exhaustion. This study not only reveals that work connectivity behavior after-hours positively affects work engagement but also illustrates the differences in impact between managers and ordinary employees; these findings contribute to the development of a consensus on the influence of work connectivity behavior after-hours on employees' psychological state at work, which provides insights for organizations seeking to manage work connectivity behavior after-hours, for example, by adopting different connectivity management strategies for employees with different workplace statuses.

Sub-picosecond tunable mid-infrared light source for driving high-efficiency optical rectification.

Optical rectification (OR) is a popular way to generate coherent terahertz radiation. Here, we develop a sub-picosecond mid-infrared (mid-IR) light source with a tailored wavelength and pulse duration for enhancing the OR efficiency. Numerical simulations for a LiNbO3-based OR with tilted pulse-front excitation are first conducted to determine the optimal parameters of pump wavelength and pulse duration, demonstrating that the OR efficiency pumped by 4-µm sub-picosecond (0.5-0.6 ps) pulses is approximately twice the value with 0.8-µm pump at the same conditions. Guided by the simulation results, we build a BaGa4Se7-based optical parametric chirped-pulse amplification system with 1030-nm thin-disk pump and broadband mid-IR seeds. The output performances of >200-µJ pulse energy, ∼600-fs pulse duration and 1-kHz pulse repetition rate are achieved in a spectral range tunable from 3.5 to 5 µm. The large energy scalability and high parameter tunability make the light source attractive to high-efficiency OR in various materials.

Self-Floating Nanoporous High-Entropy Oxides with Tunable Bandgap for Efficient Solar Seawater Desalination.

Nanoporous high-entropy oxide (np-HEO) powders with tunable composition are integrated with a poly(vinylidene fluoride) network to create self-floating solar absorber films for seawater desalination. By progressively increasing the element count, we obtain an optimized 9-component AlNiCoFeCrMoVCuTi-Ox. Density functional theory (DFT) calculations reveal a remarkable reduction in its bandgap, facilitating the light-induced migration of electrons to conduction bands to generate electron-hole pairs, which recombine to produce heat. Simultaneously, the intricate light reflection and refraction pathways, shaped by the nanoporous structure, coupled with the reduced thermal conductivity attributed to the suboptimal crystalline quality of the np-HEO ensure an effective conversion of captured light into thermal energy. Consequently, all these films demonstrate an impressive absorbance rate exceeding 93% across the 250-2500 nm spectral range. Under one sun, the surface temperature of the 9-component film rapidly rises to 110 °C within 90 s with a high pure water evaporation rate of 2.16 kg m-2 h-1.

New insights into pollution source analysis using receptor models in the upper Yangtze river basin: Effects of land use on source identification and apportionment.

To effectively control pollution and improve water quality, it is essential to accurately analyze the potential pollution sources in rivers. The study proposes a hypothesis that land use can influence the identification and apportionment of pollution sources and tested it in two areas with different types of water pollution and land use. The redundancy analysis (RDA) results showed that the response mechanisms of water quality to land use differed among regions. In both regions, the results indicated that the water quality response relationship to land use provided important objective evidence for pollution source identification, and the RDA tool optimized the procedure of source analysis for receptor models. Positive matrix decomposition (PMF) and absolute principal component score-multiple linear regression (APCS-MLR) receptor models identified five and four pollution sources along with their corresponding characteristic parameters. PMF attributed agricultural nonpoint sources (23.8%) and domestic wastewater (32.7%) as the major sources in regions 1 and 2, respectively, while APCS-MLR identified mixed sources in both regions. In terms of model performance parameters, PMF demonstrated better-fit coefficients (R2) than APCS-MLR and had a lower error rate and proportion of unidentified sources. The results show that considering the effect of land use in the source analysis can overcome the subjectivity of the receptor model and improve the accuracy of pollution source identification and apportionment. The results of the study can help managers clarify the priorities of pollution prevention and control, and provide a new methodology for water environment management in similar watersheds.

Prognostic significance of perihematomal edema in basal ganglia hemorrhage after minimally invasive endoscopic evacuation.

OBJECTIVE: Spontaneous basal ganglia hemorrhage is a common type of intracerebral hemorrhage (ICH) with no definitive treatment. Minimally invasive endoscopic evacuation is a promising therapeutic approach for ICH. In this study the authors examined prognostic factors associated with long-term functional dependence (modified Rankin Scale [mRS] score ≥ 4) in patients who had undergone endoscopic evacuation of basal ganglia hemorrhage. METHODS: In total, 222 consecutive patients who underwent endoscopic evacuation between July 2019 and April 2022 at four neurosurgical centers were enrolled prospectively. Patients were dichotomized into functionally independent (mRS score ≤ 3) and functionally dependent (mRS score ≥ 4) groups. Hematoma and perihematomal edema (PHE) volumes were calculated using 3D Slicer software. Predictors of functional dependence were assessed using logistic regression models. RESULTS: Among the enrolled patients, the functional dependence rate was 45.50%. Factors independently associated with long-term functional dependence included female sex, older age (≥ 60 years), Glasgow Coma Scale score ≤ 8, larger preoperative hematoma volume (OR 1.02), and larger postoperative PHE volume (OR 1.03, 95% CI 1.01-1.05). A subsequent analysis evaluated the effect of stratified postoperative PHE volume on functional dependence. Specifically, patients with large (≥ 50 to < 75 ml) and extra-large (≥ 75 to 100 ml) postoperative PHE volumes had 4.61 (95% CI 0.99-21.53) and 6.75 (95% CI 1.20-37.85) times greater likelihood of long-term dependence, respectively, than patients with a small postoperative PHE volume (≥ 10 to < 25 ml). CONCLUSIONS: A large postoperative PHE volume is an independent risk factor for functional dependence among basal ganglia hemorrhage patients after endoscopic evacuation, especially with postoperative PHE volume ≥ 50 ml.

Cultivation pattern affects starch structure and physicochemical properties of yam (Dioscorea persimilis).

Yam (Dioscorea spp.) is a major food source in many countries due to its tuber rich in starch (60 %-89 % of the dry weight) and various important micronutrients. Orientation Supergene Cultivation (OSC) pattern is a simple and efficient cultivation mode developed in China in recent years. However, little is known about its effect on yam tuber starch. In this study, the starchy tuber yield, starch structure and physicochemical properties were compared and analyzed in detail between OSC and Traditional Vertical Cultivation (TVC) with Dioscorea persimilis "zhugaoshu", a widely cultivated variety. The results proved that OSC significantly increased tuber yield (23.76 %-31.86 %) and commodity quality (more smooth skin) compared with TVC in three consecutive years of field experiments. Moreover, OSC increased amylopectin content, resistant starch content, granule average diameter and average degree of crystallinity by 2.7 %, 5.8 %, 14.7 % and 9.5 %, respectively, while OSC decreased starch molecular weight (Mw). These traits resulted in starch with lower thermal properties (To, Tp, Tc, ΔHgel), but higher pasting properties (PV, TV). Our results indicated that cultivation pattern affected the yam production and starch physicochemical properties. It would not only provide a practical basis for OSC promotion, but also provide valuable information on how to guide the yam starch end use in food and non-food industries.

Efficient continuous wave and passively Q switched Er:GdScO3 laser using Fe:ZnSe at 2.8 µm.

We report on diode-pumped continuous wave and passively Q switched Er:GdScO3 crystal lasers at around 2.8 µm. A continuous wave output power of 579 mW was obtained with a slope efficiency of 16.6%. Using Fe:ZnSe as a saturable absorber, a passively Q switched laser operation was realized. A maximum output power of 32 mW was generated with the shortest pulse duration of 286 ns at a repetition rate of 157.3 kHz, leading to a pulse energy of 204 nJ and a pulse peak power of 0.7 W.

Designing strategies and enhancing mechanism for multicomponent high-entropy catalysts.

High-entropy materials (HEMs) are new-fashioned functional materials in the field of catalysis owing to their large designing space, tunable electronic structure, interesting "cocktail effect", and entropy stabilization effect. Many effective strategies have been developed to design advanced catalysts for various important reactions. Herein, we firstly review effective strategies developed so far for optimizing HEM-based catalysts and the underlying mechanism revealed by both theoretical simulations and experimental aspects. In light of this overview, we subsequently present some perspectives about the development of HEM-based catalysts and provide some serviceable guidelines and/or inspiration for further studying multicomponent catalysts.

CNTSeg: A multimodal deep-learning-based network for cranial nerves tract segmentation.

The segmentation of cranial nerves (CNs) tracts based on diffusion magnetic resonance imaging (dMRI) provides a valuable quantitative tool for the analysis of the morphology and course of individual CNs. Tractography-based approaches can describe and analyze the anatomical area of CNs by selecting the reference streamlines in combination with ROIs-based (regions-of-interests) or clustering-based. However, due to the slender structure of CNs and the complex anatomical environment, single-modality data based on dMRI cannot provide a complete and accurate description, resulting in low accuracy or even failure of current algorithms in performing individualized CNs segmentation. In this work, we propose a novel multimodal deep-learning-based multi-class network for automated cranial nerves tract segmentation without using tractography, ROI placement or clustering, called CNTSeg. Specifically, we introduced T1w images, fractional anisotropy (FA) images, and fiber orientation distribution function (fODF) peaks into the training data set, and design the back-end fusion module which uses the complementary information of the interphase feature fusion to improve the segmentation performance. CNTSeg has achieved the segmentation of 5 pairs of CNs (i.e. optic nerve CN II, oculomotor nerve CN III, trigeminal nerve CN V, and facial-vestibulocochlear nerve CN VII/VIII). Extensive comparisons and ablation experiments show promising results and are anatomically convincing even for difficult tracts. The code will be openly available at https://github.com/IPIS-XieLei/CNTSeg.

Efficient generation of mid-infrared few-cycle pulses by the intrapulse difference-frequency generation in YCOB.

Yttrium calcium oxyborate (YCOB) crystals have been widely applied for generating intense near-infrared laser pulses by optical parametric amplification. Here, we show that the YCOB crystals oriented in both the XZ and XY principal planes possess broadband phase-matching property of intrapulse difference-frequency generation in the mid-infrared region. Few-cycle pulses tunable from 2 to 4 µm are experimentally produced by using a 7.5-fs pump laser at 800 nm, in which the conversion efficiency can be as high as 2.5%. With a large-size crystal and high-power pump laser, intrapulse difference-frequency generation based on YCOB may provide a new route for directly producing intense few-cycle mid-infrared pulses.