Two-dimensional Mo1-xB2 with ordered metal vacancies obtained for advanced thermoelectric applications based on first-principles calculations.

The study and development of high thermoelectric properties is crucial for the next generation of microelectronic and wearable electronics. Derived from the recent experimental realization of layers of transition metal molybdenum and boride, we report the theoretical realization of advanced thermoelectric properties in two-dimensional (2D) transition metal boride Mo1-xB2 (x = 0, 0.05, 0.10, 0.125, 0.15)-based defect sheets. The introduction of metal vacancies results in stronger d-p exchange interactions and hybridization between the Mo-d and B-p atoms. Meanwhile, the ordered metal vacancies enabled transition metal borides (n-type Mo0.9B2) to widen the d-bandwidth and raise the d-band center, leading to a relatively high carrier mobility of 3262 cm2 V-1 s-1 and conductivity twice that of a bug-free n-type MoB2 layer, which indicates that it presents good electronic and thermal transport properties. Furthermore, investigations of the thermoelectric performance exhibit a maximum ZT of up to 3.29, which is superior to those of currently reported 2D materials. Modulation by defect engineering suggests that 2D transition metal boride sheets with ordered metal vacancies have promising applications in microelectronics, wearable electronics and thermoelectric devices.

Nephrotic syndrome associated with solid malignancies: a systematic review.

BACKGROUND: Nephrotic syndrome (NS) can occur as a paraneoplastic disorder in association with various types of carcinoma. However, paraneoplastic nephrotic syndrome (PNS) is often misdiagnosed as idiopathic nephrotic syndrome or as an adverse effect of oncology treatment, leading to delayed diagnosis and suboptimal treatment. The characteristics of NS associated with solid malignancies are not yet elucidated. We systematically summarized the clinical data for 128 cases of NS combined with solid malignancies with the aim of informing the clinical management of PNS. METHODS: We searched the PubMed database for articles published from the date of inception through to October 2023 using the following keywords: "cancer" or "malignant neoplasms" or "neoplasia" or "tumors" and "nephrotic syndrome", "nephrotic" or "syndrome, nephrotic". All data were extracted from case reports and case series, and the extraction included a method for identifying individual-level patient data. RESULTS: A literature search yielded 105 cases of PNS and 23 of NS induced by cancer therapy. The median age at diagnosis was 60 years, with a male to female ratio of 1.8:1. In patients with PNS, manifestations of NS occurred before, concomitantly with, or after diagnosis of the tumor (in 36%, 30%, and 34% of cases, respectively). Membranous nephropathy (49%) was the most prevalent renal pathology and found particularly in patients with lung, colorectal, or breast carcinoma. Regardless of whether treatment was for cancer alone or in combination with NS, the likelihood of remission was high. CONCLUSION: The pathological type of NS may be associated with specific malignancies in patients with PNS. Prompt identification of PNS coupled with suitable therapeutic intervention has a significant impact on the outcome for patients.

Exceptional Light Sensitivity by Thiol-Ene Click Lithography.

Lithographic patterning, which utilizes the solubility switch of photoresists to convert optical signals into nanostructures on the substrate, is the primary top-down approach for nanoscale fabrication. However, the low light/electron-energy conversion efficiency severely limits the throughput of lithography. Thiol-ene reaction, as a photoinitiated radical addition reaction, is widely known as click reaction in the field of chemistry due to its extremely high efficiency. Here, we introduce a click lithography strategy utilizing the rapid thiol-ene click reaction to realize ultraefficient nanofabrication. This novel approach facilitated by the implementation of ultrahigh-functionality material designs enables high-contrast patterning of metal-containing nanoclusters under an extremely low deep-ultraviolet exposure dose, e.g., 7.5 mJ cm-2, which is 10-20 times lower than the dose used in the photoacid generator-based photoresist system. Meanwhile, 45 nm dense patterns were also achieved at a low dose using electron beam lithography, revealing the great potential of this approach in high-resolution patterning. Our results demonstrated the high-sensitivity and high-resolution features of click lithography, providing inspiration for future lithography design.

Charge Shielding-Oriented Design of Zinc-Based Nanoparticle Liquids for Controlled Nanofabrication.

Metal-containing nanoparticles possess nanoscale sizes, but the exploitation of their nanofeatures in nanofabrication processes remains challenging. Herein, we report the realization of a class of zinc-based nanoparticle liquids and their potential for applications in controlled nanofabrication. Utilizing the metal-core charge shielding strategy, we prepared nanoparticles that display glass-to-liquid transition behavior with glass transition temperature far below room temperature (down to -50.9 °C). Theoretical calculations suggest the outer surface of these unusual nanoparticles is almost neutral, thus leading to interparticle interactions weak enough to give them liquefaction characteristics. Such features endow them with extraordinarily high dispersibility and excellent film-forming capabilities. Twenty-two types of nanoparticles synthesized by this strategy have all shown good lithographic properties in the mid-ultraviolet, electron beam, or extreme ultraviolet light, and these nanoparticle liquids have achieved controlled top-down nanofabrication with predesigned 18 or 16 nm patterns. This proposed strategy is synthetically scalable and structurally extensible and is expected to inspire the design of entirely new forms of nanomaterials.

Process optimization of line patterns in extreme ultraviolet lithography using machine learning and a simulated annealing algorithm.

Resolution, line edge/width roughness, and sensitivity (RLS) are critical indicators for evaluating the imaging performance of resists. As the technology node gradually shrinks, stricter indicator control is required for high-resolution imaging. However, current research can improve only part of the RLS indicators of resists for line patterns, and it is difficult to improve the overall imaging performance of resists in extreme ultraviolet lithography. Here, we report a lithographic process optimization system of line patterns, where RLS models are first established by adopting a machine learning method, and then these models are optimized using a simulated annealing algorithm. Finally, the process parameter combination with optimal imaging quality of line patterns can be obtained. This system can control resist RLS indicators, and it exhibits high optimization accuracy, which facilitates the reduction of process optimization time and cost and accelerates the development of the lithography process.

Artificial intelligence of digital morphology analyzers improves the efficiency of manual leukocyte differentiation of peripheral blood.

BACKGROUND AND OBJECTIVE: Morphological identification of peripheral leukocytes is a complex and time-consuming task, having especially high requirements for personnel expertise. This study is to investigate the role of artificial intelligence (AI) in assisting the manual leukocyte differentiation of peripheral blood. METHODS: A total of 102 blood samples that triggered the review rules of hematology analyzers were enrolled. The peripheral blood smears were prepared and analyzed by Mindray MC-100i digital morphology analyzers. Two hundreds leukocytes were located and their cell images were collected. Two senior technologists labeled all cells to form standard answers. Afterward, the digital morphology analyzer unitized AI to pre-classify all cells. Ten junior and intermediate technologists were selected to review the cells with the AI pre-classification, yielding the AI-assisted classifications. Then the cell images were shuffled and re-classified without AI. The accuracy, sensitivity and specificity of the leukocyte differentiation with or without AI assistance were analyzed and compared. The time required for classification by each person was recorded. RESULTS: For junior technologists, the accuracy of normal and abnormal leukocyte differentiation increased by 4.79% and 15.16% with the assistance of AI. And for intermediate technologists, the accuracy increased by 7.40% and 14.54% for normal and abnormal leukocyte differentiation, respectively. The sensitivity and specificity also significantly increased with the help of AI. In addition, the average time for each individual to classify each blood smear was shortened by 215 s with AI. CONCLUSION: AI can assist laboratory technologists in the morphological differentiation of leukocytes. In particular, it can improve the sensitivity of abnormal leukocyte differentiation and lower the risk of missing detection of abnormal WBCs.

Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals.

Fatigue driving is one of the major factors that leads to traffic accidents. Long-term monotonous driving can easily cause a decrease in the driver's attention and vigilance, manifesting a fatigue effect. This paper proposes a means of revealing the effects of driving fatigue on the brain's information processing abilities, from the aspect of a directed brain network based on electroencephalogram (EEG) source signals. Based on current source density (CSD) data derived from EEG signals using source analysis, a directed brain network for fatigue driving was constructed by using a directed transfer function. As driving time increased, the average clustering coefficient as well as the average path length gradually increased; meanwhile, global efficiency gradually decreased for most rhythms, suggesting that deep driving fatigue enhances the brain's local information integration abilities while weakening its global abilities. Furthermore, causal flow analysis showed electrodes with significant differences between the awake state and the driving fatigue state, which were mainly distributed in several areas of the anterior and posterior regions, especially under the theta rhythm. It was also found that the ability of the anterior regions to receive information from the posterior regions became significantly worse in the driving fatigue state. These findings may provide a theoretical basis for revealing the underlying neural mechanisms of driving fatigue.

Identifying the role of excess electrons and holes for initiating the photocatalytic dissociation of methanol on a TiO2(110) surface.

As a prototype for the catalytic oxidation of organic contaminants, photocatalytic methanol dissociation on rutile TiO2(110) has drawn much attention, but its reaction mechanism remains elusive. While polarons are ubiquitous in photovoltaics and heterogeneous catalysis, how surface polarons influence adsorption remains unclear. In this paper, density functional theory is used to study the effect of excess electrons and holes on methanol dissociation on rutile TiO2(110). The effect of excess carriers for three types of methanol dissociation on the metal oxides are compared. The results show that the excess electron and hole play different roles in the dissociation reactions even though they present similar adsorption behaviors. The excess electron is easily trapped in the lattice Ti atom, and it decreases the dissociation barrier of methanol to 0.13 eV. Furthermore, the excess hole prefers to stick with the hydroxyl radical, which increases the energy barrier of methanol dissociation up to 0.43 eV. It was found that the height of the dissociation barrier is dependent on the orientation of the methanol molecules, not on the distance to the modified electron. This study identifies the essential roles of excess electrons and holes for promoting O-H dissociation. Our findings contribute considerably to broadening the understanding of photocatalytic chemistry.

Clinical and CT imaging features between the first nucleic acid-negative and first nucleic acid-positive COVID-19 patients. / 2019å† çŠ¶ç— æ¯’ç— é¦–æ¬¡æ ¸é ¸é˜´æ€§ä¸Žé¦–æ¬¡æ ¸é ¸é˜³æ€§æ‚£è€ ä¸´åºŠåŠCT影像学表现.

OBJECTIVES: To investigate the role of chest CT for the diagnostic work-up for patients with suspected infection of coronavirus disease 2019 (COVID-19). METHODS: The clinical data and imaging findings of the first nucleic acid-negative COVID-19 patients were analyzed and compared with the first nucleic acid-positive patients. RESULTS: Compared with the first nucleic acid-positive patients, the onset time of the first nucleic acid-negative patients was shorter [(3.58±2.94) d], but the diagnosis was longer [(3.92±3.66) d]. There were no significant differences in the characteristics of the clinical data and radiological findings between the 2 groups (P>0.05). CONCLUSION: Chest CT examination is important to avoid COVID-19 missed diagnosis due to false negative nucleic acid.

Recent progress in 2D group IV-IV monochalcogenides: synthesis, properties and applications.

Coordination-related, 2D structural phase transitions are a fascinating facet of 2D materials with structural degeneracy. Phosphorene and its new phases, exhibiting unique electronic properties, have received considerable attention. The 2D group IV-IV monochalcogenides (i.e. GeS, GeSe, SnS and SnSe) like black phosphorous possess puckered layered orthorhombic structure. The 2D group IV-IV monochalcogenides with advantages of earth-abundance, less toxicity, environmental compatibility and chemical stability, can be widely used in optoelectronics, piezoelectrics, photodetectors, sensors, Li-batteries and thermoelectrics. In this review, we summarized recent research progress in theory and experiment, which studies the fundamental properties, applications and fabrication of 2D group IV-IV monochalcogenides and their new phases, and brings new perspectives and challenges for the future of this emerging field.

Phase diagram and superconductivity of compressed zirconium hydrides.

It is known that pressure can be applied to fundamentally alter the bonding patterns between the chemical elements. By employing an unbiased structure search method based on a particle swarm optimization (PSO) methodology, the phase diagram and crystal structures of Zr-H compounds are systematically investigated at a high pressure up to 150 GPa. Interestingly, some unexpectedly stable compounds with unusual chemical and physical properties are predicted to be formed, for example, four stable and metallic species with stoichiometries of ZrH, ZrH2, ZrH3, and ZrH6 are identified for the first time. It is interesting to note that Cmc21-ZrH6 adopts intriguing structures with H2 units. Surprisingly, it is found that Cmcm-ZrH is superconducting with Tc as high as 10.6 K. Our study opens a novel avenue for designing superconducting Zr-H compounds by applying pressure.

Cation Intercalation in Manganese Oxide Nanosheets: Effects on Lithium and Sodium Storage.

The rapid development of advanced energy-storage devices requires significant improvements of the electrode performance and a detailed understanding of the fundamental energy-storage processes. In this work, the self-assembly of two-dimensional manganese oxide nanosheets with various metal cations is introduced as a general and effective method for the incorporation of different guest cations and the formation of sandwich structures with tunable interlayer distances, leading to the formation of 3D Mx MnO2 (M=Li, Na, K, Co, and Mg) cathodes. For sodium and lithium storage, these electrode materials exhibited different capacities and cycling stabilities. The efficiency of the storage process is influenced not only by the interlayer spacing but also by the interaction between the host cations and shutter ions, confirming the crucial role of the cations. These results provide promising ideas for the rational design of advanced electrodes for Li and Na storage.

Tinene: a two-dimensional Dirac material with a 72 meV band gap.

Dirac materials have attracted great interest for both fundamental research and electronic devices due to their unique band structures, but the usual near zero bandgap of graphene results in a poor on-off ratio in the corresponding transistors. Here, we report on tinene, monolayer gray tin, as a new two-dimensional material with both Dirac characteristics and a remarkable 72 meV bandgap based on density functional theory calculations. Compared with silicene and germanene, tinene has a similar hexagonal honeycomb monolayer structure, but it has an obviously larger buckling height (∼0.70 Å). Interestingly, such a moderate buckling structure results in phonon dispersion without appreciable imaginary modes, indicating the strong dynamic stability of tinene. Significantly, a distinct transformation is discovered from the band structure that six Dirac cones would appear at high symmetry K points in the first Brillouin zone when gray tin is thinned from the bulk to monolayer, but a bandgap as large as 72 meV is still preserved. Considering the recent successful realization of silicene and germanene with a similar structure, the predicted stable tinene with Dirac characteristics and a suitable bandgap is a possibility for the "more than Moore" materials and devices.

Modulating the phase transition between metallic and semiconducting single-layer MoS2 and WS2 through size effects.

The first-principles calculations are performed to investigate the electronic properties and the atomic mechanism of the single layer MoS2 or WS2 homo-junction structure. The results reveal that both the stability and electronic structure of the homo-junction structure are greatly affected by the type of boundaries, which connect the different phase structures, either the semiconducting hexagonal (H) structure or the metallic trigonal (T) structure. Through tuning the size of the lateral homo-junction structure of either MoS2 or WS2, the phase transformation between H and T can occur. Interestingly, the electronic structures of homo-junction structures can be tuned between the metal and the semiconductor by changing the size of the nanoribbons.

A first-principles study of lithium-decorated hybrid boron nitride and graphene domains for hydrogen storage.

First-principles calculations are performed to investigate the adsorption of hydrogen onto Li-decorated hybrid boron nitride and graphene domains of (BN)(x)C(1-x) complexes with x = 1, 0.25, 0.5, 0.75, 0, and B0.125C0.875. The most stable adsorption sites for the nth hydrogen molecule in the lithium-decorated (BN)(x)C(1-x) complexes are systematically discussed. The most stable adsorption sites were affected by the charge localization, and the hydrogen molecules were favorably located above the C-C bonds beside the Li atom. The results show that the nitrogen atoms in the substrate planes could increase the hybridization between the 2p orbitals of Li and the orbitals of H2. The results revealed that the (BN)(x)C(1-x) complexes not only have good thermal stability but they also exhibit a high hydrogen storage of 8.7% because of their dehydrogenation ability.

The effect of institutions and urbanization on environmental quality: evidence from the Belt and Road Initiative countries using dynamic panel models.

Increased globalization in urban areas raise energy consumption that leads to high carbon dioxide discharge and degrade environmental quality. Other economic activities also produce emission; however, a well-established institutional framework can overcome the issues of environmental degradation and minimize the effect of harmful factors on the environment. In this regard, this study investigates the effect of urbanization, energy consumption, and industrialization on carbon dioxide emission by taking into consideration the role of institutional quality in the Belt and Road Initiative (BRI) countries for the period of 2002 to 2019. Employing dynamic panel techniques, the results are in line with theories which show that increased urbanization, energy consumption, industrialization, and economic growth raise carbon dioxide emission and lead to environmental degradation. The study also found that international trade and political stability reduce emission; however, institutional quality as a whole positively affects carbon dioxide emission. The study also found a U-shape relationship between urbanization and carbon dioxide emission. The interaction term between institutional quality and urbanization significantly mitigates carbon dioxide emission and raise environmental sustainability. The findings of this study have considerable policy suggestions for the sample countries.

Outward foreign direct investment and GVC position of manufacturing industry: A perspective on China's general trade and processing trade structure.

Depending on the trading modes, the effect of Outward Foreign Direct Investment (OFDI) on the manufacturing industry's position within the global value chain (GVC) may differ considerably. This paper examines the GVC position of China's manufacturing industry from 2003 to 2018, specifically focusing on the general trade and processing trade. Drawing upon this premise, this paper analyzes the effect and mechanism by which OFDI influences the GVC position of China's manufacturing industry. The result demonstrates that: (1) China's processing trade manufacturing industry has a much lower GVC position than general trade manufacturing industry. The GVC position of China's general trade manufacturing industry rose from 2.76 to 2.90 from 2003 to 2018, while processing trade manufacturing industry remained around 1.93. (2) OFDI boosts the GVC position of general trade manufacturing industry through facilitating reverse technology spillover, inducing industry structure upgrading, and enabling export scale expansion. (3) OFDI hinders the GVC position growth of processing trade manufacturing industry. The research findings offer theoretical backing for China to develop OFDI strategies that are tailored to different trading modes within the new framework of dual circulation. These strategies aim to facilitate the transformation and advancement of the manufacturing industry, as well as the growth of the GVC position.

Multi-objective evolutionary multitasking algorithm based on cross-task transfer solution matching strategy.

The superior performance of evolutionary multitasking (EMT) algorithms is largely owing to the potential synergy between tasks. Current EMT algorithms only involve a unidirectional process of transferring individuals from the source task to the target task. This method does not consider the search preference of the target task in the process of finding transferred individuals; therefore, the potential synergy between tasks is not fully utilized. Herein, we propose a bidirectional knowledge transfer method, which refers to the search preference of the target task in the process of finding transferred individuals. These transferred individuals fit the search process well for the target task. In addition, an adaptive strategy for adjusting the intensity of the knowledge transfer is proposed. This method enables the algorithm to adjust the intensity of knowledge transfer independently according to the living conditions of the individuals to be transferred to balance the convergence of the population with the computational intensity of the algorithm. The proposed algorithm is compared with comparison algorithms on 38 multi-objective multitasking optimization benchmarks. Experimental results show that the proposed algorithm is not only outperforming other comparison algorithms in more than 30 benchmarks, but also has considerable convergence efficiency.

Porous carbon-based metal-free monolayers towards highly stable and flexible wearable thermoelectrics and microelectronics.

In the search for high mechanical strength and flexibility, ultrahigh semiconducting speed is crucial for the next generation of microelectronic and wearable electronics. Herein, we propose two 2D graphene-like macrocyclic complex carbon-based monolayers, namely g-MC-A and g-MC-B. Both monolayers are dynamically stable according to phonon dispersion and ab initio molecular dynamics simulations. The yield stress of these two layers reaches half that of graphene, revealing remarkably high mechanical strength. Besides, both monolayers are semiconductors. The electron mobility of g-MC-A is high: up to 104 cm2 V-1 s-1, comparable to black phosphorene. Furthermore, these two monolayers exhibit excellent inherent conductivity with anisotropic characteristics. Interestingly, an extra valley is observed near the conduction band edge for both layers, further simulation predicted both metal-free monolayers will exhibit ZT > 1, implying high thermoelectric performance. Therefore, these two C-based metal-free layers have promising applications in mechanical enhancement, microelectronics, wearable electronics and thermoelectric devices.

Critical dimension prediction of metal oxide nanoparticle photoresists for electron beam lithography using a recurrent neural network.

The critical dimension (CD) of lithographic patterns is the most significant indicator for evaluating the imaging performance of photoresists, and its value is seriously affected by process conditions. However, the lithographic imaging system is highly nonlinear, and extensive exposure experiments are needed to obtain the desired CD. This consumes lots of time, manpower, and cost in screening for optimal process conditions. Here, we report a combined electron beam lithography (EBL) experiment and recurrent neural network (RNN) study on the CDs of metal oxide nanoparticle photoresists, and establish a CD RNN model. Leveraging the RNN model, a process condition filter is developed to screen suitable process conditions. The experimental results demonstrate that the prediction accuracy of the CD model exceeds 93%, and the photoresist patterns under the screened process conditions can satisfy the requirements of a preset CD. This work opens up a novel perspective for accurate EBL process modeling, and provides guidance for EBL experiments.