Diagnostic efficiency of intravoxel incoherent motion-based virtual magnetic resonance elastography in pulmonary neoplasms.

BACKGROUND: The aim of the study were as below. (1) To investigate the feasibility of intravoxel incoherent motion (IVIM)-based virtual magnetic resonance elastography (vMRE) to provide quantitative estimates of tissue stiffness in pulmonary neoplasms. (2) To verify the diagnostic performance of shifted apparent diffusion coefficient (sADC) and reconstructed virtual stiffness values in distinguishing neoplasm nature. METHODS: This study enrolled 59 patients (37 males, 22 females) with one pulmonary neoplasm who underwent computed tomography-guided percutaneous transthoracic needle biopsy (PTNB) with pathological diagnosis (26 adenocarcinoma, 10 squamous cell carcinoma, 3 small cell carcinoma, 4 tuberculosis and 16 non-specific benign; mean age, 60.81 ± 9.80 years). IVIM was performed on a 3 T magnetic resonance imaging scanner before biopsy. sADC and virtual shear stiffness maps reflecting lesion stiffness were reconstructed. sADC and virtual stiffness values of neoplasm were extracted, and the diagnostic performance of vMRE in distinguishing benign and malignant and detailed pathological type were explored. RESULTS: Compared to benign neoplasms, malignant ones had a significantly lower sADC and a higher virtual stiffness value (P < 0.001). Subsequent subtype analyses showed that the sADC values of adenocarcinoma and squamous cell carcinoma groups were significantly lower than non-specific benign group (P = 0.013 and 0.001, respectively). Additionally, virtual stiffness values of the adenocarcinoma and squamous cell carcinoma subtypes were significantly higher than non-specific benign group (P = 0.008 and 0.001, respectively). However, no significant correlation was found among other subtype groups. CONCLUSIONS: Non-invasive vMRE demonstrated diagnostic efficiency in differentiating the nature of pulmonary neoplasm. vMRE is promising as a new method for clinical diagnosis.

A Minimally Invasive Approach for Laparoscopic-Perineal Sigmoid Colpoplasty: Case Report and Innovations.

BACKGROUND Laparoscopic-perineal neovagina construction by sigmoid colpoplasty is a popular therapeutic approach for patients with Mayer-Rokitansky-Kuster-Hauser (MRKH) syndrome. The conventional approach requires an auxiliary abdominal incision to exteriorize the descending colon to fix the anvil for end-to-end colorectal anastomosis. We modified the natural orifice specimen extraction surgery (NOSES) approach by exteriorizing the descending colon through the artificial neovaginal tunnel to replace the anvil extracorporeally, without requiring an auxiliary abdominal incision. It was a more minimally invasive technique. CASE REPORT We performed this modified laparoscopic-perineal sigmoid colpoplasty in a 26-year-old woman with MRKH syndrome. We cut off a segment of the sigmoid colon with a vascular pedicle to make a new vagina out of it, the same as in the traditional laparoscopic-perineal sigmoid colpoplasty. What is new about this technique is that it has no need for abdominal incision and is more minimally invasive. The operating time was 315 min. No postoperative complications occurred. The postoperative hospital stay was 4 days. The modified laparoscopic-perineal approach, free from an auxiliary abdominal incision, demonstrated advantages, including a shorter hospital stay, expedited recovery, and comparable anatomical outcomes, when compared with the traditional approach. This innovation improves the surgical experience for patients with MRKH syndrome, addressing the physical and psychological aspects of their condition. CONCLUSIONS This refined laparoscopic-perineal neovagina construction by sigmoid colpoplasty represents a feasible and minimally invasive technique. It is an attractive option for MRKH syndrome patients in need of vaginal reconstruction, offering a streamlined procedure with reduced postoperative recovery time and enhanced patient outcomes.

Advances in Understanding Fusarium graminearum: Genes Involved in the Regulation of Sexual Development, Pathogenesis, and Deoxynivalenol Biosynthesis.

The wheat head blight disease caused by Fusarium graminearum is a major concern for food security and the health of both humans and animals. As a pathogenic microorganism, F. graminearum produces virulence factors during infection to increase pathogenicity, including various macromolecular and small molecular compounds. Among these virulence factors, secreted proteins and deoxynivalenol (DON) are important weapons for the expansion and colonization of F. graminearum. Besides the presence of virulence factors, sexual reproduction is also crucial for the infection process of F. graminearum and is indispensable for the emergence and spread of wheat head blight. Over the last ten years, there have been notable breakthroughs in researching the virulence factors and sexual reproduction of F. graminearum. This review aims to analyze the research progress of sexual reproduction, secreted proteins, and DON of F. graminearum, emphasizing the regulation of sexual reproduction and DON synthesis. We also discuss the application of new gene engineering technologies in the prevention and control of wheat head blight.

CD206 modulates the role of M2 macrophages in the origin of metastatic tumors.

Tumor metastasis is a key factor affecting the life of patients with malignant tumors. For the past hundred years, scientists have focused on how to kill cancer cells and inhibit their metastasis in vivo, but few breakthroughs have been made. Here we hypothesized a novel mode for cancer metastasis. We show that the phagocytosis of apoptotic tumor cells by macrophages leads to their polarization into the M2 phenotype, and that the expression of stem cell related as well as drug resistance related genes was induced. Therefore, it appears that M2 macrophages have "defected" and have been transformed into the initial "metastatic cancer cells", and thus are the source, at least in part, of the distal tissue tumor metastasis. This assumption is supported by the presence of fused cells with characteristics of both macrophage and tumor cell observed in the peripheral blood and ascites of patients with ovarian cancer. By eliminating the expression of CD206 in M2 macrophages using siRNA, we show that the growth and metastasis of tumors was suppressed using both in vitro cell line and with experimental in vivo mouse models. In summary, we show that M2 macrophages in the blood circulation underwent a "change of loyalty" to become "cancer cells" that transformed into distal tissue metastasis, which could be suppressed by the knockdown of CD206 expression.

A 3-year follow-up clinical study on the preservation for vitality of involved tooth in jaw cysts through an innovative method.

Jaw cysts commonly affect the oral and maxillofacial region, involving adjacent tooth roots. The management of these teeth, particularly regarding root canal therapy and apicoectomy, lacks consensus. This study introduces a novel treatment concept and refined surgical approach to preserve pulp viability in teeth involved in jaw cysts. The objective was to investigate the effectiveness and potential benefits of this approach over a 36-month follow-up period. A conservative management approach prioritized vitality preservation, reserving root canal treatment and apicectomy for cases with post-operative discomfort. A comprehensive follow-up of 108 involved teeth from 36 jaw cyst cases treated with the modified method was conducted. Clinical observation, X-ray imaging, cone-beam computed tomography (CBCT), and pulp vitality testing assessed changes in cyst size, tooth color, pulp vitality, root structure, and surrounding alveolar bone. After 36 months, our modified surgical approach successfully preserved tooth vitality in 84 involved teeth. Adverse symptoms in 19 teeth, such as redness, swelling, fistula, and pain, resolved with postoperative root canal therapy. Follow-up was lost for five teeth in two cases. No cyst recurrences were observed, and in 34 cases, the bone cavity gradually disappeared, restoring normal bone density during long-term follow-up. Our modified surgical method effectively preserves tooth vitality in jaw cysts. This innovative approach has the potential to improve the management of teeth involved in jaw cysts.

RPTOR blockade suppresses brain metastases of NSCLC by interfering the ceramide metabolism via hijacking YY1 binding.

BACKGROUND: Ceramide metabolism is crucial in the progress of brain metastasis (BM). However, it remains unexplored whether targeting ceramide metabolism may arrest BM. METHODS: RNA sequencing was applied to screen different genes in primary and metastatic foci and whole-exome sequencing (WES) to seek crucial abnormal pathway in BM + and BM-patients. Cellular arrays were applied to analyze the permeability of blood-brain barrier (BBB) and the activation or inhibition of pathway. Database and Co-Immunoprecipitation (Co-IP) assay were adopted to verify the protein-protein interaction. Xenograft and zebrafish model were further employed to verify the cellular results. RESULTS: RNA sequencing and WES reported the involvement of RPTOR and ceramide metabolism in BM progress. RPTOR was significantly upregulated in BM foci and increased the permeability of BBB, while RPTOR deficiency attenuated the cell invasiveness and protected extracellular matrix. Exogenous RPTOR boosted the SPHK2/S1P/STAT3 cascades by binding YY1, in which YY1 bound to the regions of SPHK2 promoter (at -353 ~ -365 nt), further promoting the expression of SPHK2. The latter was rescued by YY1 RNAi. Xenograft and zebrafish model showed that RPTOR blockade suppressed BM of non-small cell lung cancer (NSCLC) and impaired the SPHK2/S1P/STAT3 pathway. CONCLUSION: RPTOR is a key driver gene in the brain metastasis of lung cancer, which signifies that RPTOR blockade may serve as a promising therapeutic candidate for clinical application.

On the Robustness of Average Losses for Partial-Label Learning.

Partial-label learning (PLL) utilizes instances with PLs, where a PL includes several candidate labels but only one is the true label (TL). In PLL, identification-based strategy (IBS) purifies each PL on the fly to select the (most likely) TL for training; average-based strategy (ABS) treats all candidate labels equally for training and let trained models be able to predict TL. Although PLL research has focused on IBS for better performance, ABS is also worthy of study since modern IBS behaves like ABS in the beginning of training to prepare for PL purification and TL selection. In this paper, we analyze why ABS was unsatisfactory and propose how to improve it. Theoretically, we propose two problem settings of PLL and prove that average PL losses (APLLs) with bounded multi-class losses are always robust, while APLLs with unbounded losses may be non-robust, which is the first robustness analysis for PLL. Experimentally, we have two promising findings: ABS using bounded losses can match/exceed state-of-the-art performance of IBS using unbounded losses; after using robust APLLs to warm start, IBS can further improve upon itself. Our work draws attention to ABS research, which can in turn boost IBS and push forward the whole PLL.

PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning.

Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set with the ground-truth label included. However, in a more practical but challenging scenario, the annotator may miss the ground-truth and provide a wrong candidate set, which is known as the noisy PLL problem. To remedy this problem, we propose the PiCO+ framework that simultaneously disambiguates the candidate sets and mitigates label noise. Core to PiCO+, we develop a novel label disambiguation algorithm PiCO that consists of a contrastive learning module along with a novel class prototype-based disambiguation method. Theoretically, we show that these two components are mutually beneficial, and can be rigorously justified from an expectation-maximization (EM) algorithm perspective. To handle label noise, we extend PiCO to PiCO+, which further performs distance-based clean sample selection, and learns robust classifiers by a semi-supervised contrastive learning algorithm. Beyond this, we further investigate the robustness of PiCO+ in the context of out-of-distribution noise and incorporate a novel energy-based rejection method for improved robustness. Extensive experiments demonstrate that our proposed methods significantly outperform the current state-of-the-art approaches in standard and noisy PLL tasks and even achieve comparable results to fully supervised learning.

Achieving 2.2 GPa Ultra-High Strength in Low-Alloy Steel Using a Direct Quenching and Partitioning Process.

Advanced high-strength steels (AHSS) have a wide range of applications in equipment safety and lightweight design, and enhancing the strength of AHSS to the ultra-high level of 2 GPa is currently a key focus. In this study, a new process of thermo-mechanical control process followed by direct quenching and partitioning (TMCP-DQP) was developed based on Fe-0.4C-1Mn-0.6Si (wt.%) low-alloy steel, and the effects of microstructure evolution on mechanical properties under TMCP-DQP process and conventional hot rolled quenched and tempered process (HR-QT) were comparatively studied. The results show that the TMCP-DQP process not only shortened the processing steps but also achieved outstanding comprehensive mechanical properties. The TMCP-DQP steel exhibited a tensile strength of 2.23 GPa, accompanied by 11.9% elongation and a Brinell hardness of 624 HBW, with an impact toughness of 28.5 J at -20 °C. In contrast, the HR-QT steel exhibited tensile strengths ranging from 2.16 GPa to 1.7 GPa and elongations between 5.2% and 12.2%. The microstructure of TMCP-DQP steel primarily consisted of lath martensite, containing thin-film retained austenite (RA), nanoscale rod-shaped carbides, and a minor number of nanoscale twins. The volume fraction of RA reached 7.7%, with an average carbon content of 7.1 at.% measured by three-dimensional atom probe tomography (3DAP). Compared with the HR-QT process, the TMCP-DQP process resulted in a finer microstructure, with a prior austenite grain (PAG) size of 11.91 µm, forming packets and blocks with widths of 5.12 µm and 1.63 µm. The TMCP-DQP process achieved the ultra-high strength of low-alloy steel through the synergistic effects of grain refinement, dislocation strengthening, and precipitation strengthening. The dynamic partitioning stage stabilized the RA through carbon enrichment, while the relaxation stage reduced a small portion of the dislocations generated by thermal deformation, and the self-tempering stage eliminated internal stresses, all guaranteeing considerable ductility and toughness. The TMCP-DQP process may offer a means for industries to streamline their manufacturing processes and provide a technological reference for producing 2.2 GPa grade AHSS.

A Parametrical Model for Instance-Dependent Label Noise.

In label-noise learning, estimating the transition matrix is a hot topic as the matrix plays an important role in building statistically consistent classifiers. Traditionally, the transition from clean labels to noisy labels (i.e., clean-label transition matrix (CLTM)) has been widely exploited on class-dependent label-noise (wherein all samples in a clean class share the same label transition matrix). However, the CLTM cannot handle the more common instance-dependent label-noise well (wherein the clean-to-noisy label transition matrix needs to be estimated at the instance level by considering the input quality). Motivated by the fact that classifiers mostly output Bayes optimal labels for prediction, in this paper, we study to directly model the transition from Bayes optimal labels to noisy labels (i.e., Bayes-Label Transition Matrix (BLTM)) and learn a classifier to predict Bayes optimal labels. Note that given only noisy data, it is ill-posed to estimate either the CLTM or the BLTM. But favorably, Bayes optimal labels have no uncertainty compared with the clean labels, i.e., the class posteriors of Bayes optimal labels are one-hot vectors while those of clean labels are not. This enables two advantages to estimate the BLTM, i.e., (a) a set of examples with theoretically guaranteed Bayes optimal labels can be collected out of noisy data; (b) the feasible solution space is much smaller. By exploiting the advantages, this work proposes a parametrical model for estimating the instance-dependent label-noise transition matrix by employing a deep neural network, leading to better generalization and superior classification performance.

Comprehensive nomogram models for predicting checkpoint inhibitor pneumonitis.

Checkpoint inhibitor pneumonitis (CIP) is a common type of immune-related adverse events (irAEs) with poor clinical prognosis. Currently, there is a lack of effective biomarkers and predictive models to predict the occurrence of CIP. This study retrospectively enrolled 547 patients who received immunotherapy. The patients were divided into CIP cohorts of any grade, or grade ≥2 or ≥3. Multivariate logistic regression analysis was used to determine the independent risk factors, based on which we established Nomogram A and B for respectively predicting any grade or grade ≥2 CIP. For Nomogram A to predict any grade CIP, the C indexes in the training and validation cohorts were 0.827 (95% CI=0.772-0.881) and 0.860 (95% CI=0.741-0.918), respectively. Similarly, for Nomogram B to predict grade 2 or higher CIP, the C indexes of the training and validation cohorts were 0.873 (95% CI=0.826-0.921) and 0.904 (95% CI=0.804-0.973), respectively. In conclusion, the predictive power of nomograms A and B has proven satisfactory following internal and external verification. They are promising clinical tools that are convenient, visual, and personalized for assessing the risks of developing CIP.

Patients without Increased Lymphocyte Counts and Decreased CT Scores During the Early 2nd Week of Illness Onset may Develop to Severe COVID-19.

BACKGROUND: Lymphopenia and high CT score is associated with COVID-19 severity. Herein we describe the change pattern in lymphocyte count and CT score during hospitalization and explore a possible association with the severity of COVID-19. METHODS: In this retrospective study, 13 non-severe COVID-19 patients diagnosed at admission were enrolled. One patient progressed to severe disease. Change patterns in lymphocyte counts and CT scores of all patients were analyzed. RESULTS: Lymphocyte count increased gradually from day 5 post-illness onset (day 5 vs. day 15, p = 0.001). Lymphocyte count of the severe patient fluctuated at low levels throughout the 15-day period. Chest CT scores of non-severe patients increased significantly during the first 5 days of illness onset, but decreased gradually beginning day 9 (illness onset vs. day 5, p = 0.002, day 9 vs. day 15, p = 0.015). In the severe patient, CT score continued to increase over the 11 days post-illness onset period. CONCLUSIONS: Non-severe COVID-19 patients had significantly increased lymphocyte counts and decreased CT scores beginning day 5 and day 9 of illness onset, respectively. The patients without increased lymphocyte counts and decreased CT scores during the early 2nd week of illness onset may develop to severe COVID-19.

On the Effectiveness of Adversarial Training Against Backdoor Attacks.

Although adversarial training (AT) is regarded as a potential defense against backdoor attacks, AT and its variants have only yielded unsatisfactory results or have even inversely strengthened backdoor attacks. The large discrepancy between expectations and reality motivates us to thoroughly evaluate the effectiveness of AT against backdoor attacks across various settings for AT and backdoor attacks. We find that the type and budget of perturbations used in AT are important, and AT with common perturbations is only effective for certain backdoor trigger patterns. Based on these empirical findings, we present some practical suggestions for backdoor defense, including relaxed adversarial perturbation and composite AT. This work not only boosts our confidence in AT's ability to defend against backdoor attacks but also provides some important insights for future research.

Effects of Charge Trapping on Memory Characteristics for HfO2-Based Ferroelectric Field Effect Transistors.

A full understanding of the impact of charge trapping on the memory window (MW) of HfO2-based ferroelectric field effect transistors (FeFETs) will permit the design of program and erase protocols, which will guide the application of these devices and maximize their useful life. The effects of charge trapping have been studied by changing the parameters of the applied program and erase pulses in a test sequence. With increasing the pulse amplitude and pulse width, the MW increases first and then decreases, a result attributed to the competition between charge trapping (CT) and ferroelectric switching (FS). This interaction between CT and FS is analyzed in detail using a single-pulse technique. In addition, the experimental data show that the conductance modulation characteristics are affected by the CT in the analog synaptic behavior of the FeFET. Finally, a theoretical investigation is performed in Sentaurus TCAD, providing a plausible explanation of the CT effect on the memory characteristics of the FeFET. This work is helpful to the study of the endurance fatigue process caused by the CT effect and to optimizing the analog synaptic behavior of the FeFET.

Representation learning for continuous action spaces is beneficial for efficient policy learning.

Deep reinforcement learning (DRL) breaks through the bottlenecks of traditional reinforcement learning (RL) with the help of the perception capability of deep learning and has been widely applied in real-world problems. While model-free RL, as a class of efficient DRL methods, performs the learning of state representations simultaneously with policy learning in an end-to-end manner when facing large-scale continuous state and action spaces. However, training such a large policy model requires a large number of trajectory samples and training time. On the other hand, the learned policy often fails to generalize to large-scale action spaces, especially for the continuous action spaces. To address this issue, in this paper we propose an efficient policy learning method in latent state and action spaces. More specifically, we extend the idea of state representations to action representations for better policy generalization capability. Meanwhile, we divide the whole learning task into learning with the large-scale representation models in an unsupervised manner and learning with the small-scale policy model in the RL manner. The small policy model facilitates policy learning, while not sacrificing generalization and expressiveness via the large representation model. Finally, the effectiveness of the proposed method is demonstrated by MountainCar, CarRacing and Cheetah experiments.

FDA Approval Summary: Lutetium Lu 177 Vipivotide Tetraxetan for Patients with Metastatic Castration-Resistant Prostate Cancer.

On March 23, 2022, the FDA approved Pluvicto (lutetium Lu 177 vipivotide tetraxetan, also known as 177Lu-PSMA-617) for the treatment of adult patients with prostate-specific membrane antigen (PSMA)-positive metastatic castration-resistant prostate cancer (mCRPC) who have been treated with androgen receptor pathway inhibition and taxane-based chemotherapy. The recommended 177Lu-PSMA-617 dose is 7.4 gigabecquerels (GBq; 200 mCi) intravenously every 6 weeks for up to six doses, or until disease progression or unacceptable toxicity. The FDA granted traditional approval based on VISION (NCT03511664), which was a randomized (2:1), multicenter, open-label trial that assessed the efficacy and safety of 177Lu-PSMA-617 plus best standard of care (BSoC; n = 551) or BSoC alone (n = 280) in men with progressive, PSMA-positive mCRPC. Patients were required to have received ≥1 androgen receptor pathway inhibitor, and one or two prior taxane-based chemotherapy regimens. There was a statistically significant and clinically meaningful improvement in overall survival (OS), with a median OS of 15.3 months in the 177Lu-PSMA-617 plus BSoC arm and 11.3 months in the BSoC arm, respectively (HR: 0.62; 95% confidence interval: 0.52-0.74; P < 0.001). The most common adverse reactions (≥20%) occurring at a higher incidence in patients receiving 177Lu-PSMA-617 were fatigue, dry mouth, nausea, anemia, decreased appetite, and constipation. The most common laboratory abnormalities that worsened from baseline in ≥30% of patients receiving 177Lu-PSMA-617 were decreased lymphocytes, decreased hemoglobin, decreased leukocytes, decreased platelets, decreased calcium, and decreased sodium. This article summarizes the FDA review of data supporting traditional approval of 177Lu-PSMA-617 for this indication.

Dynamic contrast-enhanced MRI perfusion parameters are imaging biomarkers for angiogenesis in lung cancer.

BACKGROUND: Quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may have the potential to reflect angiogenesis and proliferation of pulmonary neoplasms. PURPOSE: To verify whether DCE-MRI can identify pulmonary neoplasm property and evaluate the correlation of DCE-MRI perfusion parameters with microvessel density (MVD) and Ki-67 in lung cancer. MATERIAL AND METHODS: This study enrolled 65 patients with one pulmonary neoplasm who underwent computed tomography-guided percutaneous lung biopsy with pathological diagnosis (43 malignant, 22 benign; mean age = 59.71 ± 11.72 years). All patients did DCE-MRI before biopsy. Quantitative MRI parameters including endothelial transfer constant (Ktrans), flux rate constant (Kep), and fractional extravascular extracellular space (EES) volume (Ve) were calculated by extended Tofts linear model. MVD was evaluated by CD34-expressing tumor vessels. Proliferation was assessed by Ki-67 staining. The correlations of parameters with MVD and Ki-67 expression were analyzed. RESULTS: Ktrans and Kep values were significantly increased in malignant lesions compared to benign lesions (P = 0.001 and 0.022, respectively), whereas no statistical difference in Ve was found. The CD34 expression was positively correlated to Ktrans (r = 0.608; P = 0.004) and Kep (r = 0.556; P = 0.001). Subsequent subtype analyses also showed positive correlations of Ktrans and Kep with MVD in adenocarcinoma group (r = 0.550 and 0.563; P = 0.012 and 0.015, respectively). No significant correlation was found between these parameters and Ki-67. CONCLUSION: Ktrans and Kep may distinguish benign and malignant pulmonary neoplasm. Ktrans and Kep, with their positive correlation to MVD, can be used as non-invasive parameters reflecting lung cancer angiogenesis.

Class-Wise Denoising for Robust Learning Under Label Noise.

Label noise is ubiquitous in many real-world scenarios which often misleads training algorithm and brings about the degraded classification performance. Therefore, many approaches have been proposed to correct the loss function given corrupted labels to combat such label noise. Among them, a trend of works achieve this goal by unbiasedly estimating the data centroid, which plays an important role in constructing an unbiased risk estimator for minimization. However, they usually handle the noisy labels in different classes all at once, so the local information inherited by each class is ignored which often leads to unsatisfactory performance. To address this defect, this paper presents a novel robust learning algorithm dubbed "Class-Wise Denoising" (CWD), which tackles the noisy labels in a class-wise way to ease the entire noise correction task. Specifically, two virtual auxiliary sets are respectively constructed by presuming that the positive and negative labels in the training set are clean, so the original false-negative labels and false-positive ones are tackled separately. As a result, an improved centroid estimator can be designed which helps to yield more accurate risk estimator. Theoretically, we prove that: 1) the variance in centroid estimation can often be reduced by our CWD when compared with existing methods with unbiased centroid estimator; and 2) the performance of CWD trained on the noisy set will converge to that of the optimal classifier trained on the clean set with a convergence rate [Formula: see text] where n is the number of the training examples. These sound theoretical properties critically enable our CWD to produce the improved classification performance under label noise, which is also demonstrated by the comparisons with ten representative state-of-the-art methods on a variety of benchmark datasets.

Multiwavelength High-Detectivity MoS2 Photodetectors with Schottky Contacts.

Photodetection is one of the vital functions for the multifunctional "More than Moore" (MtM) microchips urgently required by Internet of Things (IoT) and artificial intelligence (AI) applications. The further improvement of the performance of photodetectors faces various challenges, including materials, fabrication processes, and device structures. We demonstrate in this work MoS2 photodetectors with a nanoscale channel length and a back-gate device structure. With the mechanically exfoliated six-monolayer-thick MoS2, a Schottky contact between source/drain electrodes and MoS2, a high responsivity of 4.1 × 103 A W-1, and a detectivity of 1.34 × 1013 cm Hz1/2 W-1 at 650 nm were achieved. The devices are also sensitive to multiwavelength lights, including 520 and 405 nm. The electrical and optoelectronic properties of the MoS2 photodetectors were studied in depth, and the working mechanism of the devices was analyzed. The photoinduced Schottky barrier lowering (PIBL) was found to be important for the high performance of the phototransistor.