Configurable dual-topological-interface-states induced reflection in hybrid multilayers consisting of a Ge2Sb2Te5 film.

Active control of induced reflection is crucial for many potential applications ranging from slowing light to biosensing devices. However, most previous approaches require patterned nanostructures to achieve controllable induced reflection, which hinders their further applications due to complicated architectures. Herein, we propose a lithography-free multilayered structure to achieve the induced reflection through the coupling of dual-topological-interface-states. The multilayers consist of two one-dimensional (1D) photonic crystals (PCs) and an Ag film separated by a Spacer, topological edge state (TES) and topological Tamm state (TTS) can be excited simultaneously and their coupling induces the reflection window. The coupled-oscillator model is proposed to mimic the coupling between the TES and TTS, and the analytical results are in good agreement with finite element method (FEM). In addition, the TES-TTS induced reflection is robust to the variation of structural parameters. By integrating an ultra-thin phase-change film of Ge2Sb2Te5 (GST) into the multilayers, the induced reflection can be switched through the phase transition of the GST film. The multipole decomposition reveals that the vanished reflection window is arising from the disappearance of TTS associated with the toroidal dipole (TD) mode.

Flexible design of chiroptical response of planar chiral metamaterials using deep learning.

Optical chirality is highly demanded for biochemical sensing, spectral detection, and advanced imaging, however, conventional design schemes for chiral metamaterials require highly computational cost due to the trial-and-error strategy, and it is crucial to accelerate the design process particularly in comparably simple planar chiral metamaterials. Herein, we construct a bidirectional deep learning (BDL) network consists of spectra predicting network (SPN) and design predicting network (DPN) to accelerate the prediction of spectra and inverse design of chiroptical response of planar chiral metamaterials. It is shown that the proposed BDL network can accelerate the design process and exhibit high prediction accuracy. The average process of prediction only takes ∼15 ms, which is 1 in 40000 compared to finite-difference time-domain (FDTD). The mean-square error (MSE) loss of forward and inverse prediction reaches 0.0085 after 100 epochs. Over 95.2% of training samples have MSE ≤ 0.0042 and MSE ≤ 0.0044 for SPN and DPN, respectively; indicating that the BDL network is robust in the inverse deign without underfitting or overfitting for both SPN and DPN. Our founding shows great potentials in accelerating the on-demand design of planar chiral metamaterials.

Depleting profibrotic macrophages using bioactivated in vivo assembly peptides ameliorates kidney fibrosis.

Managing renal fibrosis is challenging owing to the complex cell signaling redundancy in diseased kidneys. Renal fibrosis involves an immune response dominated by macrophages, which activates myofibroblasts in fibrotic niches. However, macrophages exhibit high heterogeneity, hindering their potential as therapeutic cell targets. Herein, we aimed to eliminate specific macrophage subsets that drive the profibrotic immune response in the kidney both temporally and spatially. We identified the major profibrotic macrophage subset (Fn1+Spp1+Arg1+) in the kidney and then constructed a 12-mer glycopeptide that was designated as bioactivated in vivo assembly PK (BIVA-PK) to deplete these cells. BIVA-PK specifically binds to and is internalized by profibrotic macrophages. By inducing macrophage cell death, BIVA-PK reshaped the renal microenvironment and suppressed profibrotic immune responses. The robust efficacy of BIVA-PK in ameliorating renal fibrosis and preserving kidney function highlights the value of targeting macrophage subsets as a potential therapy for patients with CKD.

DIA-based phosphoproteomics identifies early phosphorylation events in response to EGTA and mannitol in Arabidopsis.

Osmotic stress significantly hampers plant growth and crop yields, emphasizing the need for a thorough comprehension of the underlying molecular responses. Previous research has demonstrated that osmotic stress rapidly induces calcium influx and signaling, along with the activation of a specific subset of protein kinases, notably the Raf-SnRK2 kinase cascades within minutes. However, the intricate interplay between calcium signaling and the activation of RAF-SnRK2 kinase cascades remains elusive. Here in this study, we discovered that Raf-like protein (RAF) kinases undergo hyperphosphorylation in response to osmotic shocks. Intriguingly, treatment with the calcium chelator EGTA robustly activates RAF-SnRK2 cascades, mirroring the effects of osmotic treatment. Utilizing high-throughput DIA-based phosphoproteomics, we unveiled the global impact of EGTA on protein phosphorylation. Beyond the activation of RAFs and sucrose non-fermenting-1-related protein kinase 2s (SnRK2s), EGTA treatment also activates mitogen-activated protein kinase (MAPKs) cascades, Calcium-dependent protein kinases (CDPKs), and receptor-like protein kinases, etc. Through overlapping assays, we identified potential roles of mitogen-activated protein kinase kinase kinase kinases (MAP4Ks) and receptor-like protein kinases in the osmotic-stress-induced activation of RAF-SnRK2 cascades. Our findings illuminate the regulation of phosphorylation and cellular events by Ca2+ signaling, offering insights into the (exocellular) Ca2+ deprivation during early hyperosmolality sensing and signaling.

Molecular Autonomous Pathfinder Using Deep Reinforcement Learning.

Diffusion in solids is a slow process that dictates rate-limiting processes in key chemical reactions. Unlike crystalline solids that offer well-defined diffusion pathways, the lack of similar structural motifs in amorphous or glassy materials poses great challenges in bridging the slow diffusion process and material failures. To tackle this problem, we propose an AI-guided long-term atomistic simulation approach: molecular autonomous pathfinder (MAP) framework based on deep reinforcement learning (DRL), where the RL agent is trained to uncover energy efficient diffusion pathways. We employ a Deep Q-Network architecture with distributed prioritized replay buffer, enabling fully online agent training with accelerated experience sampling by an ensemble of asynchronous agents. After training, the agents provide atomistic configurations of diffusion pathways with their energy profile. We use a piecewise nudged elastic band to refine the energy profile of the obtained pathway and the corresponding diffusion time on the basis of transition-state theory. With the MAP framework, we demonstrate atomistic diffusion mechanisms in amorphous silica with time scales comparable to experiments.

A novel deep learning method to segment parathyroid glands on intraoperative videos of thyroid surgery.

Introduction: The utilization of artificial intelligence (AI) augments intraoperative safety and surgical training. The recognition of parathyroid glands (PGs) is difficult for inexperienced surgeons. The aim of this study was to find out whether deep learning could be used to auxiliary identification of PGs on intraoperative videos in patients undergoing thyroid surgery. Methods: In this retrospective study, 50 patients undergoing thyroid surgery between 2021 and 2023 were randomly assigned (7:3 ratio) to a training cohort (n = 35) and a validation cohort (n = 15). The combined datasets included 98 videos with 9,944 annotated frames. An independent test cohort included 15 videos (1,500 frames) from an additional 15 patients. We developed a deep-learning model Video-Trans-U-HRNet to segment parathyroid glands in surgical videos, comparing it with three advanced medical AI methods on the internal validation cohort. Additionally, we assessed its performance against four surgeons (2 senior surgeons and 2 junior surgeons) on the independent test cohort, calculating precision and recall metrics for the model. Results: Our model demonstrated superior performance compared to other AI models on the internal validation cohort. The DICE and accuracy achieved by our model were 0.760 and 74.7% respectively, surpassing Video-TransUnet (0.710, 70.1%), Video-SwinUnet (0.754, 73.6%), and TransUnet (0.705, 69.4%). For the external test, our method got 89.5% precision 77.3% recall and 70.8% accuracy. In the statistical analysis, our model demonstrated results comparable to those of senior surgeons (senior surgeon 1: χ2 = 0.989, p = 0.320; senior surgeon 2: χ2 = 1.373, p = 0.241) and outperformed 2 junior surgeons (junior surgeon 1: χ2 = 3.889, p = 0.048; junior surgeon 2: χ2 = 4.763, p = 0.029). Discussion: We introduce an innovative intraoperative video method for identifying PGs, highlighting the potential advancements of AI in the surgical domain. The segmentation method employed for parathyroid glands in intraoperative videos offer surgeons supplementary guidance in locating real PGs. The method developed may have utility in facilitating training and decreasing the learning curve associated with the use of this technology.

The Maternal-Fetal Risk Factors of Hypertensive Disorders of Pregnancy and its Effects on Infant Complete Blood Count and Coagulation Factors.

Objective: Our aim was to analyze the risk factors of hypertensive disorders of pregnancy (HDP) and explore its influence on fetal risk factors, infant's blood cells and markers of inflammation. Methods: A total of 123 patients with HDP were in the HDP group, and 121 healthy pregnant women were selected as the control group. The general clinical data of the participants were recorded. Statistics of maternal and infant outcomes, delivery methods, routine blood lab results and coagulation factors of the newborn were recorded. Univariate analysis and multi-factor analysis were used to explore the risk factors for HDP. Results: The overall incidence of poor maternal outcomes in the HDP group was higher than in the control group. The incidence of premature delivery; postpartum hemorrhage; coagulopathy; placental abruption; heart failure and hemolysis, elevated liver enzymes, and low platelets (HELLP) syndrome was significantly higher in the HDP group than in the control group (P < .05). The cesarean section rate in the HDP group was significantly higher than in the control group (P < .05). The overall incidence of poor outcomes in fetuses and newborns in the HDP group was higher than in the control group. The incidence of infant low birth weight, intraventricular hemorrhage (IVH), neonatal respiratory distress syndrome (NRDS), asphyxia and all-cause neonatal death were higher than in the control group (P < .05). The incidence of small gestational age (SGA), fetal distress and intrauterine death in the HDP group were higher than in the control group (P < .05). In the HDP group, neonatal white blood cells (WBC), neutrophils (NEUT) and platelets (PLT) were significantly lower than in the control group (P < .05), while hemoglobin (Hgb) and hematocrit (Hct) were higher (P < .05). Conclusions: HDP endangers the health of mother and infant; Age, body mass index (BMI) (>24 kg/m2), parity, history of hypertension, family history of hypertension and other factors may be involved in the occurrence and development of HDP.

NEAT1 Deficiency Promotes Corneal Epithelial Wound Healing by Activating cAMP Signaling Pathway.

Purpose: This study aimed to investigate the role of the long non-coding RNA (lncRNA) NEAT1 in corneal epithelial wound healing in mice. Methods: The central corneal epithelium of wild-type (WT), MALAT1 knockout (M-KO), NEAT1 knockout (N-KO), and NEAT1 knockdown (N-KD) mice was scraped to evaluate corneal epithelial and nerve regeneration rates. RNA sequencing of the corneal epithelium from WT and N-KO mice was performed 24 hours after debridement to determine the role of NEAT1. Quantitative PCR (qPCR) and ELISA were used to confirm the bioinformatic analysis. The effects of the cAMP signaling pathway were evaluated in N-KO and N-KD mice using SQ22536, an adenylate cyclase inhibitor. Results: Central corneal epithelial debridement in N-KO mice significantly promoted epithelial and nerve regeneration rates while suppressing inflammatory cell infiltration. Furthermore, the expression of Atp1a2, Ppp1r1b, Calm4, and Cngb1, which are key components of the cAMP signaling pathway, was upregulated in N-KO mice, indicative of its activation. Furthermore, the cAMP pathway inhibitor SQ22536 reversed the accelerated corneal epithelial wound healing in both N-KO and N-KD mice. Conclusions: NEAT1 deficiency contributes to epithelial repair during corneal wound healing by activating the cAMP signaling pathway, thereby highlighting a potential therapeutic strategy for corneal epithelial diseases.

An automatic parathyroid recognition and segmentation model based on deep learning of near-infrared autofluorescence imaging.

INTRODUCTION: Near-infrared autofluorescence imaging (NIFI) can be used to identify parathyroid gland (PG) during surgery. The purpose of the study is to establish a new model, help surgeons better identify, and protect PGs. METHODS: Five hundred and twenty three NIFI images were selected. The PGs were recorded by NIFI and marked with artificial intelligence (AI) model. The recognition rate for PGs was calculated. Analyze the differences between surgeons of different years of experience and AI recognition, and evaluate the diagnostic and therapeutic efficacy of AI model. RESULTS: Our model achieved 83.5% precision and 57.8% recall in the internal validation set. The visual recognition rate of AI model was 85.2% and 82.4% on internal and external sets. The PG recognition rate of AI model is higher than that of junior surgeons (p < 0.05). CONCLUSIONS: This AI model will help surgeons identify PGs, and develop their learning ability and self-confidence.

Phase-change metasurfaces for dynamic control of chiral quasi-bound states in the continuum.

Chiral quasi-bound states in the continuum (QBIC) offer novel mechanisms to achieve intrinsic chiroptical responses. However, current studies on chiral QBIC metasurfaces are restricted to the excitation of intrinsic chirality and fail to dynamically control its circular dichroism (CD) responses. Herein, we construct a phase-change metasurface based on paired Ge2Sb2Te5 (GST) bars to demonstrate the dynamic control of the CD responses of chiral QBIC. The modified coupled mode theory (CMT) is proposed to evaluate the intrinsic chirality, and the predicted results are in good agreement with the finite-difference time-domain (FDTD) results. The maximal intrinsic chirality is associated with the spin-selected dipole mode, i.e., the coupled magnetic dipole (MD) QBIC mode for the left-handed circularly polarized (LCP) light and the decoupled electric dipole (ED) QBIC mode for the right-handed circularly polarized (RCP) light. By varying the volume fraction of GST, the location of chiral BIC can be tuned linearly, and the corresponding chiral response can be switched.

Topological transport in heterostructure of valley photonic crystals.

We propose a heterogeneous structure, which are composed of two valley photonic crystals (VPCs) with opposite valley Chern numbers and air channel. With the increasing width of the air channel, valley-locked waveguide modes are found in topological bandgap by analyzing energy bands. Finite element method (FEM) simulation results show that the fundamental and high order modes are valley-locked, propagating unidirectionally under the excitation of chiral source, and possess higher flux compared to the valley-locked topological edge state in the domain wall. Besides, the immunity to backscattering in bend and couplers, and the robustness to random disorders are discussed in detail. We also investigate the one-way multimode interference (MMI) effect based on valley-locked waveguide modes, and design topological beam splitters. Our study provides a novel idea for topological transport with high flux, and more freedom to design valley-locked waveguide devices, including bends, couplers and splitters.

Construction and validation of a predictive model for the risk of peritoneal dialysis-associated peritonitis after peritoneal dialysis catheterization.

Aim: To construct and validate a risk prediction model for the development of peritoneal dialysis-associated peritonitis (PDAP) in patients undergoing peritoneal dialysis (PD). Methods: This retrospective analysis included patients undergoing PD at the Department of Nephrology, the First Affiliated Hospital of Anhui University of Chinese Medicine, between January 2016 and January 2021. Baseline data were collected. The primary study endpoint was PDAP occurrence. Patients were divided into a training cohort (n = 264) and a validation cohort (n = 112) for model building and validation. Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to optimize the screening variables. Predictive models were developed using multifactorial logistic regression analysis with column line plots. Receiver operating characteristic (ROC) curves, calibration curves, and Hosmer-Lemeshow goodness-of-fit tests were used to verify and evaluate the discrimination and calibration of the prediction models. Decision curve analysis (DCA) was used to assess the clinical validity of the prediction models. Results: Five potential predictors of PDAP after PD catheterization were screened using LASSO regression analysis, including neutrophil-to-lymphocyte ratio (NLR), serum ALBumin (ALB), uric acid (UA), high sensitivity C-reactive protein (hsCRP), and diabetes mellitus (DM). Predictive models were developed by multi-factor logistic regression analysis and plotted in columns. The area under the ROC curve (AUC) values were 0.891 (95% confidence interval [CI]: 0.829-0.844) and 0.882 (95% CI: 0.722-0.957) for the training and validation cohorts, respectively. The Hosmer-Lemeshow test showed a good fit (p = 0.829 for the training cohort; p = 0.602 for the validation cohort). The DCA curves indicated that the threshold probabilities for the training and validation cohorts were 4-64% and 3-90%, respectively, predicting a good net gain for the clinical model. Conclusion: NLR, ALB, UA, hsCRP, and DM are independent predictors of PDAP after PD catheterization. The column line graph model constructed based on the abovementioned factors has good discriminatory and calibrating ability and helps to predict the risk of PDAP after PD catheterization.

Active control of resonant asymmetric transmission based on topological edge states in paired photonic crystals with a Ge2Sb2Te5 film.

For many high-precision applications such as filtering, sensing, and photodetection, active control of resonant responses of metasurfaces is crucial. Herein, we demonstrate that active control of resonant asymmetric transmission can be realized based on the topological edge state (TES) of an ultra-thin G e 2 S b 2 T e 5 (GST) film in a photonic crystal grating (PCG). The PCG is composed of two pairs of one-dimensional photonic crystals (PCs) separated by a GST film. The phase change of the GST film re-distributes the field distributions of the PCG; thus active control of narrowband asymmetric transmission can be achieved due to the switch of the on-off state of the TES. According to multipole decompositions, the appearance and disappearance of the synergistically reduced dipole modes are responsible for the high-contrast asymmetric transmission of the PCG. In addition, the asymmetric transmission performances are robust to the variation of structural parameters, and good unidirectional transmission performances with a high peak transmission and high contrast ratio can be balanced, as the layer number of the two PCs is set as four. By changing the crystallization fraction of GST, the peak transmission and peak contrast ratio of asymmetric transmission can be flexibly tuned with the resonance locations kept almost the same.

SPR Sensor Based on a Concave Photonic Crystal Fiber Structure with MoS2/Au Layers.

We propose a surface plasmon resonance (SPR) sensor based on the concave photonic crystal fiber (PCF) coated with molybdenum disulfide (MoS2) and Au layers, which can detect the refractive index (RI) of the analyte. The finite element method (FEM) was used to verify our design, and the loss spectra of the fundamental mode are calculated. Compared with the SPR sensor with only a Au layer, the wavelength sensitivity can be improved by from 3700 to 4400 nm/RIU. Our proposed sensor works in near-infrared band and has a wide RI range from 1.19 to 1.40. The influences of the geometrical parameters of PCF and the thicknesses of Au and MoS2 layers on the loss spectra are discussed in detail, and the maximum wavelength sensitivity of 5100 nm/RIU can be achieved. Meanwhile, a high resolution of 1.96 × 10-5 RIU and the largest FOM of 29.143 can be obtained. It is believed that our findings show the sensor's excellent potential in medical testing, unknown biological detection, environmental monitoring and organic chemical detection.

Enhanced antitumor and anti-metastasis by VEGFR2-targeted doxorubicin immunoliposome synergy with NK cell activation.

Liposomal doxorubicin exhibits stronger drug accumulation at the tumor site due to the Enhanced Permeability and Retention (EPR) effect. However, the prognosis for the patient is poor due to this drug's lack of targeting and tumor metastasis during treatment. Vascular epidermal growth factor receptor (VEGFR2) plays an important role in angiogenesis and cancer metastasis. To enhance antitumor efficacy of PEGylated liposomal doxorubicin, we constructed a VEGFR2-targeted and doxorubicin-loaded immunoliposome (Lipo-DOX-C00) by conjugating a VEGFR2-specific, single chain antibody fragment to DSPE-PEG2000-MAL, and then we inserted the antibody-conjugated polymer into liposomal doxorubicin (Lipo-DOX). The immunoliposome was formed uniformly with high affinity for VEGFR2. In vitro, Lipo-DOX-C00 enhanced doxorubicin internalization into LLC and 4T1 cells compared with non-conjugated, liposomal doxorubicin. In vivo, Lipo-DOX-C00 delivered DOX to tumor tissues effectively, which exhibited an improved antitumor and anti-metastasis efficacy in both LLC subcutaneous tumor models and 4T1 tumor models. In addition, the combined therapy of a VEGFR2-MICA bispecific antibody (JZC01) and Lipo-DOX-C00 achieved enhanced inhibition of cancer growth and metastasis due to activation of the immune system. Our study provides a promising approach to clinical application of liposomal doxorubicin.

Selective excitation of hyperbolic phonon polaritons-induced broadband absorption via α-MoO3 square pyramid arrays.

Optical anisotropy of α-MoO3 in its reststrahlen (RS) bands provides exciting opportunities for constructing the polarization-dependent devices. However, achieving broadband anisotropic absorptions through the same α-MoO3 arrays is still challenging. In this study, we demonstrate that selective broadband absorption can be achieved by using the same α-MoO3 square pyramid arrays (SPAs). For both the x and y polarizations, the absorption responses of the α-MoO3 SPAs calculated by using the effective medium theory (EMT) agreed well with those of the FDTD, indicating the excellent selective broadband absorption of the α-MoO3 SPAs are associated with the resonant hyperbolic phonon polaritons (HPhPs) modes assisted by the anisotropic gradient antireflection (AR) effect of the structure. The near-field distribution of the absorption wavelengths of the α-MoO3 SPAs shows that the magnetic-field enhancement of the lager absorption wavelength tends to shift to the bottom of the α-MoO3 SPAs due to the lateral Fabry-Pérot (F-P) resonance, and the electric-field distribution exhibits the ray-like light propagation trails due to the resonance nature of the HPhPs modes. In addition, broadband absorption of the α-MoO3 SPAs can be maintained if the width of the bottom edge of the α-MoO3 pyramid is large than 0.8 µm, and excellent anisotropic absorption performances are almost immune to the variations of the thickness of the spacer and the height of the α-MoO3 pyramid.

Fiber-specific white matter alterations in Parkinson's disease patients with freezing of gait.

Freezing of gait (FOG) is a gait disorder that usually occurs in advanced stages of Parkinson's disease (PD). Understanding the underlying mechanism of FOG is important for treatment and prevention. Previous studies have investigated white matter (WM) structure to explore the pathology of FOG. However, the pathology is still unclear, possibly due to the methodological limitation in identifying specific fiber tracts. This study aimed to investigate tract-specific WM structural changes in FOG patients and their relationships with clinical characteristics. We enrolled 19 PD patients with FOG (PD-FOG), 19 without FOG (PD-woFOG) and 21 controls. Fixel-based analysis is a novel framework to avoid the effect of crossing fibers, which provides the metrics to assess WM morphology. By combining a method for segmenting fibers, we identified abnormalities in the specific fiber tracts. Compared to PD-woFOG, PD-FOG showed significant increased fiber-bundle cross-section (FC) in the corpus callosum (CC), fornix (FX), inferior longitudinal fasciculus (ILF), striato-premotor (ST_PREM), superior thalamic radiation (STR), thalamo-premotor (T_PREM), increased fiber density and cross-section (FDC) in the STR, and decreased fiber density (FD) in the CC and ILF. Additionally, the ILF was correlated with motor, cognition and memory, the CC was correlated with anxiety, and the T_PREM was also correlated with cognition. In conclusion, in addition to impairments of WM found in PD-FOG, we found enhancements in WM, which may imply compensatory mechanisms. Furthermore, multiple fiber tracts were correlated with clinical characteristics, especially the ILF, validating the involvement of transmission circuits of multiple distinct information in mechanisms of FOG.

Nanocomposite Hydrogels with Polymer Grafted Silica Nanoparticles, Using Glucose Oxidase.

Nanocomposite hydrogels offer remarkable potential for applications in bone tissue engineering. They are synthesized through the chemical or physical crosslinking of polymers and nanomaterials, allowing for the enhancement of their behaviour by modifying the properties and compositions of the nanomaterials involved. However, their mechanical properties require further enhancement to meet the demands of bone tissue engineering. Here, we present an approach to improve the mechanical properties of nanocomposite hydrogels by incorporating polymer grafted silica nanoparticles into a double network inspired hydrogel (gSNP Gels). The gSNP Gels were synthesised via a graft polymerization process using a redox initiator. gSNP Gels were formed by grafting 2-acrylamido-2-methylpropanesulfonic acid (AMPS) as the first network gel followed by a sequential second network acrylamide (AAm) onto amine functionalized silica nanoparticles (ASNPs). We utilized glucose oxidase (GOx) to create an oxygen-free atmosphere during polymerization, resulting in higher polymer conversion compared to argon degassing. The gSNP Gels showed excellent compressive strengths of 13.9 ± 5.5 MPa, a strain of 69.6 ± 6.4%, and a water content of 63.4% ± 1.8. The synthesis technique demonstrates a promising approach to enhance the mechanical properties of hydrogels, which can have significant implications for bone tissue engineering and other soft tissue applications.

Mid-infrared circular-polarization-sensitive photodetector based on a chiral metasurface with a photothermoelectric effect.

Photothermoelectric conversion in chiral metasurfaces with thermoelectric material provides an effective way to achieve circular polarization recognition. In this paper, we propose a circular-polarization-sensitive photodetector in a mid-infrared region, which is mainly composed of an asymmetric silicon grating, a film of gold (Au), and the thermoelectric B i 2 T e 3 layer. The asymmetric silicon grating with the Au layer achieves high circular dichroism absorption due to a lack of mirror symmetry, which results in a different temperature increasing on the surface of the B i 2 T e 3 layer under right-handed circularly polarized (RCP) and left-handed circularly polarized (LCP) excitation. Then the chiral Seebeck voltage and output power density are obtained, thanks to the thermoelectric effect of B i 2 T e 3. All the works are based on the finite element method, and the simulation results are conducted by the Wave Optics module of COMSOL, which is coupled with the Heat Transfer module and Thermoelectric module of COMSOL. When the incident flux is 1.0W/c m 2, the output power density under RCP (LCP) light reaches 0.96m W/c m 2 (0.01m W/c m 2) at a resonant wavelength, which achieves a high capability of detecting circular polarization. Besides, the proposed structure shows a faster response time than that of other plasmonic photodetectors. Our design provides a novel, to the best of our knowledge, method for chiral imaging, chiral molecular detection, and so on.