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As an excellent optical device, photodetectors have many important applications, such as communication technology, display technology, scientific measurement, fire monitoring, aerospace and biomedical research, and it’s of great significance in the research of nanotechnology and optoelectronics. Graphene, as the first two-dimensional (2D) single-element nanomaterial, has the advantages of high carrier mobility, high strength, high light transmittance and excellent thermal conductivity, and it’s widely used in various nano-optical devices. The great success of graphene has led scientists to extensive research on other 2D single-element nanomaterials. Recently, a group of novel 2D single-element nanomaterials have attracted a lot of attention from scientists because of its excellent physical, chemical, electronic, mechanical and optical properties. Furthermore, it has opened a new door for the realization of new and efficient photodetectors. The group of 2D single-element nanomaterials are called 2D-Xenes and used to make high-performance photodetectors. Currently, there are few studies on photodetectors based on 2D-Xenes, but some 2D-Xenes have been applied to photodetectors and reported. Some of these have excellent photodetection performance, such as high photoresponsivity ( R ), broad spectral response range, fast photoresponse speed and high specific detectivity ( D *). Based on the novel 2D-Xenes, this review explores the types and preparation methods of 2D-Xenes, and the working mechanisms of 2D-Xenes photodetectors. Finally, the challenges and development trends of 2D-Xenes in the future are discussed. The research of 2D-Xenes is of great significance for the development of high-performance photodetectors in the future, and is expected to be widely used in other nanoelectronics and optical devices.

With the improvement of medical level, the number of elderly patients is increasing, and the postoperative outcome of the patients cannot be ignored. However, there have been no studies on the relationship between preoperative heart rate variability (HRV) and Perioperative Neurocognitive Disorders (PND). The purpose of this study was to explore the correlation between (HRV) and (PND), postoperative intensive care unit (ICU), and hospital stay in patients undergoing non-cardiac surgery. This retrospective analysis included 687 inpatients who underwent 24-hour dynamic electrocardiogram examination in our six departments from January 2021 to January 2022. Patients were divided into two groups based on heart rate variability (HRV): high and low. Possible risk factors of perioperative outcomes were screened using univariate analysis, and risk factors were included in multivariate logistic regression to screen for independent risk factors. The subgroup analysis was carried out to evaluate the robustness of the results. The nomogram of PND multi-factor logistic prediction model was constructed. The receiver operating characteristic (ROC) curve was drawn, and the calibration curve was drawn by bootstrap resampling 1000 times for internal verification to evaluate the prediction ability of nomogram. A total of 687 eligible patients were included. The incidence of low HRV was 36.7% and the incidence of PND was 7.6%. The incidence of PND in the low HRV group was higher than that in the high HRV group (11.8% vs 5.2%), the postoperative ICU transfer rate was higher (15.9% than 9.3%P = 0.009), and the hospital stay was longer [15 (11, 19) vs (13), 0.015]. The multivariable logistic regression analysis showed that after adjusting for other factors, decreased low HRV was identified as an independent risk factor for the occurrence of PND (Adjusted Odds Ratio = 2.095; 95% Confidence Interval: 1.160-3.784; P = 0.014) and postoperative ICU admission (Adjusted Odds Ratio = 1.925; 95% Confidence Interval: 1.128-3.286; P = 0.016). This study drew a nomogram column chart for a multivariate logistic regression model, incorporating age and HRV. The calibration curve shows that the predicted value of the model for the occurrence of cardio-cerebrovascular events is in good agreement with the actual observed value, with C-index of 0.696 (95% CI: 0.626 ~ 0.766). Subgroup analysis showed that low HRV was an independent risk factor for PND in patients with gastrointestinal surgery and ASA Ⅲ, aged ≥ 65 years. In patients undergoing non-cardiac surgery, the low HRV was an independent risk factor for PND and postoperative transfer to the ICU, and the hospitalization time of patients with low HRV was prolonged. Through establishing a risk prediction model for the occurrence of PND, high-risk patients can be identified during the perioperative period for early intervention.

Efficiently capturing multidimensional signals containing spectral and temporal information is crucial for intelligent machine vision. Although in-sensor computing shows promise for efficient visual processing by reducing data transfer, its capability to compress temporal/spectral data is rarely reported. Here we demonstrate a programmable two-dimensional (2D) heterostructure-based optoelectronic sensor integrating sensing, memory, and computation for in-sensor data compression. Our 2D sensor captured and memorized/encoded optical signals, leading to in-device snapshot compression of dynamic videos and three-dimensional spectral data with a compression ratio of 8:1. The reconstruction quality, indicated by a peak signal-to-noise ratio value of 15.81 dB, is comparable to the 16.21 dB achieved through software. Meanwhile, the compressed action videos (in the form of 2D images) preserve all semantic information and can be accurately classified using in-sensor convolution without decompression, achieving accuracy on par with uncompressed videos (93.18% vs 83.43%). Our 2D optoelectronic sensors promote the development of efficient intelligent vision systems at the edge. In-sensor computing offers a promising solution for image processing with reduced data transfer. Here, the authors report programmable and multifunctional van der Waals optoelectronic sensors, showing their application for snapshot compression and recognition of dynamic videos and 3D spectral data.

With the improvement of medical level, the number of elderly patients is increasing, and the postoperative outcome of the patients cannot be ignored. However, there have been no studies on the relationship between preoperative heart rate variability (HRV) and Perioperative Neurocognitive Disorders (PND). The purpose of this study was to explore the correlation between (HRV) and (PND), postoperative intensive care unit (ICU), and hospital stay in patients undergoing non-cardiac surgery. This retrospective analysis included 687 inpatients who underwent 24-hour dynamic electrocardiogram examination in our six departments from January 2021 to January 2022. Patients were divided into two groups based on heart rate variability (HRV): high and low. Possible risk factors of perioperative outcomes were screened using univariate analysis, and risk factors were included in multivariate logistic regression to screen for independent risk factors. The subgroup analysis was carried out to evaluate the robustness of the results. The nomogram of PND multi-factor logistic prediction model was constructed. The receiver operating characteristic (ROC) curve was drawn, and the calibration curve was drawn by bootstrap resampling 1000 times for internal verification to evaluate the prediction ability of nomogram. A total of 687 eligible patients were included. The incidence of low HRV was 36.7% and the incidence of PND was 7.6%. The incidence of PND in the low HRV group was higher than that in the high HRV group (11.8% vs 5.2%), the postoperative ICU transfer rate was higher (15.9% than 9.3%P = 0.009), and the hospital stay was longer [15 (11, 19) vs (13), 0.015]. The multivariable logistic regression analysis showed that after adjusting for other factors, decreased low HRV was identified as an independent risk factor for the occurrence of PND (Adjusted Odds Ratio = 2.095; 95% Confidence Interval: 1.160-3.784; P = 0.014) and postoperative ICU admission (Adjusted Odds Ratio = 1.925; 95% Confidence Interval: 1.128-3.286; P = 0.016). This study drew a nomogram column chart for a multivariate logistic regression model, incorporating age and HRV. The calibration curve shows that the predicted value of the model for the occurrence of cardio-cerebrovascular events is in good agreement with the actual observed value, with C-index of 0.696 (95% CI: 0.626 ~ 0.766). Subgroup analysis showed that low HRV was an independent risk factor for PND in patients with gastrointestinal surgery and ASA â¢, aged [greater than or equal to] 65 years. In patients undergoing non-cardiac surgery, the low HRV was an independent risk factor for PND and postoperative transfer to the ICU, and the hospitalization time of patients with low HRV was prolonged. Through establishing a risk prediction model for the occurrence of PND, high-risk patients can be identified during the perioperative period for early intervention.

2D layered materials turn out to be the most attractive hotspot in materials for their unique physical and chemical properties. A special class of 2D layered material refers to materials exhibiting phase transition based on environment variables. Among these materials, transition metal dichalcogenides (TMDs) act as a promising alternative for their unique combination of atomic‐scale thickness, direct bandgap, significant spin–orbit coupling and prominent electronic and mechanical properties, enabling them to be applied for fundamental studies as catalyst materials. Metal phosphorous trichalcogenides (MPTs), as another potential catalytic 2D phase transition material, have been employed for their unusual intercalation behavior and electrochemical properties, which act as a secondary electrode in lithium batteries. The preparation of 2D TMD and MPT materials has been extensively conducted by engineering their intrinsic structures at the atomic scale. In this study, advanced synthesis methods of preparing 2D TMD and MPT materials are tested, and their properties are investigated, with stress placed on their phase transition. The surge of this type of report is associated with water‐splitting catalysis and other catalytic purposes. This study aims to be a guideline to explore the mentioned 2D TMD and MPT materials for their catalytic applications. 2D phase transition materials, including transition metal dichalcogenides (TMDs) and metal phosphorous trichalcogenides (MPTs), attract researchers' interests for their unique combination of large surface area, direct bandgap, intrigue chemical, and mechanical properties. Great efforts are devoted to preparing 2D TMD and MPT materials by engineering their intrinsic structures at the atomic scale, aiming to achieve active catalyst toward catalytic purposes.

The growth and wetting of water on two-dimensional(2D) materials are important to understand the development of 2D material based electronic, optoelectronic, and nanomechanical devices. Here, we visualize the liquefaction processes of water on the surface of graphene, MoS 2 and black phosphorus (BP) via optical microscopy. We show that the shape of the water droplets forming on the surface of BP, which is anisotropic, is elliptical. In contrast, droplets are rounded when they form on the surface of graphene or MoS 2 , which do not possess orthometric anisotropy. Molecular simulations show that the anisotropic liquefaction process of water on the surface of BP is attributed to the different binding energies of H 2 O molecules on BP along the armchair and zigzag directions. The results not only reveal the anisotropic nature of water liquefaction on the BP surface but also provide a way for fast and nondestructive determination of the crystalline orientation of BP. Structural anisotropy of surfaces determines properties relevant for applications. Here the authors observe a relationship between the shape of water droplets forming on graphene, MoS 2 and black phosphorous and the surface structure, proposing a method to determine lattice orientation by optical microscopy.

Great success in 2D van der Waals (vdW) heterostructures based photodetectors is obtained owing to the unique electronic and optoelectronic properties of 2D materials. Performance of photodetectors based 2D vdW heterojunctions at atomic scale is more sensitive to the nanointerface of the heterojunction than conventional bulk heterojunction. Here, a nanoengineered heterostructure for the first‐time demonstration of a nanointerface using an inserted graphene layer between black phosphorus (BP) and InSe which inhibits interlayer recombination and greatly improves photodetection performances is presented. In addition, a transition of the transport characteristics of the device is induced by graphene, from diffusion motion of minority carriers to drift motion of majority carriers. These two reasons together with an internal photoemission effect make the BP/G/InSe‐based photodetector have ultrahigh specific detectivity at room temperature. The results demonstrate that high‐performance vdW heterostructure photodetectors can be achieved through simple structural manipulation of the heterojunction interface on nanoscale. A nanoscale interface engineering in which few‐layer graphene (G) is inserted into the interlayer of black phosphorus (BP)/InSe, forming a 2D BP/G/InSe van der Waals heterojunction, is introduced. Effective electron transfer from BP to InSe is facilitated by the insertion of G, which induces a better photoresponse performance of the BP/G/InSe device.

In recent years, monoelemental 2D materials (Xenes) such as graphene, graphdiyne, silicene, phosphorene, and tellurene, have gained significant traction in biosensing applications. Owing to their ultra‐thin layered structure, exceptionally high specific surface area, unique surface electronic properties, excellent mechanical strength, flexibility, and other distinctive features, Xenes are recognized for their potential as materials with low detection limits, high speed, and exceptional flexibility in biosensing applications. In this review, the unique properties of Xenes, their synthesis, and recent theoretical and experimental advances in applications related to biosensing, including DNA/RNA biosensors, protein biosensors, small molecule biosensors, cell, and ion biosensors are comprehensively summarized. Finally, the challenges and prospects of this emerging field are discussed. Xenes (monoelemental 2D materials like graphene, phosphorene, tellurene) enable ultrasensitive FET (Field‐Effect Transistor) biosensors due to their tunable bandgap, high carrier mobility, and large surface area. Advances in synthesis (CVD, MBE) and functionalization allow detection of DNA/RNA (via CRISPR‐Cas systems), proteins, small molecules, and ions with femtomolar limits. Challenges include environmental stability, scalable fabrication, and integration into portable systems. Future focuses on heterostructures, surface engineering, and clinical validation for real‐world diagnostics.

Density functional theory calculations of the layer (L)-dependent electronic band structure, work function and optical properties of β-InSe have been reported. Owing to the quantum size effects (QSEs) in β-InSe, the band structures exhibit direct-to-indirect transitions from bulk β-InSe to few-layer β-InSe. The work functions decrease monotonically from 5.22 eV (1 L) to 5.0 eV (6 L) and then remain constant at 4.99 eV for 7 L and 8 L and drop down to 4.77 eV (bulk β-InSe). For optical properties, the imaginary part of the dielectric function has a strong dependence on the thickness variation. Layer control in two-dimensional layered materials provides an effective strategy to modulate the layer-dependent properties which have potential applications in the next-generation high performance electronic and optoelectronic devices.

Since atomically thin two-dimensional (2D) graphene was successfully synthesized in 2004, it has garnered considerable interest due to its advanced properties. However, the weak optical absorption and zero bandgap strictly limit its further development in optoelectronic applications. In this regard, other 2D materials, including black phosphorus (BP), transition metal dichalcogenides (TMDCs), 2D Te nanoflakes, and so forth, possess advantage properties, such as tunable bandgap, high carrier mobility, ultra-broadband optical absorption, and response, enable 2D materials to hold great potential for next-generation optoelectronic devices, in particular, mid-infrared (MIR) band, which has attracted much attention due to its intensive applications, such as target acquisition, remote sensing, optical communication, and night vision. Motivated by this, this article will focus on the recent progress of semiconducting 2D materials in MIR optoelectronic devices that present a suitable category of 2D materials for light emission devices, modulators, and photodetectors in the MIR band. The challenges encountered and prospects are summarized at the end. We believe that milestone investigations of 2D materials beyond graphene-based MIR optoelectronic devices will emerge soon, and their positive contribution to the nano device commercialization is highly expected.