Engineering the band gap chemically by organic molecules is a powerful tool with which to optimize the properties of inorganic 2D materials. The obtained materials are however still limited by ...inhomogeneous compositions and properties at nanoscale and small adjustable band gap ranges. To overcome these problems in the traditional exfoliation and then organic modification strategy, an organic modification and then exfoliation strategy was explored in this work for preparing 2D organic metal chalcogenides (OMCs). Unlike the reported organically modified 2D materials, the inorganic layers of OMCs are fully covered by long-range ordered organic functional groups. By changing the electron-donating ability of the organic functional groups and the electronegativity of the metals, the band gaps of OMCs were varied by 0.83 eV and their conductivities were modulated by 9 orders of magnitude, which are 2 and 10
times higher than the highest values observed in the reported chemical methods, respectively.
Machine learning has been recently used for novel perovskite designs owing to the availability of a large amount of perovskite formability data. Trustworthy results should be based on the valid and ...reliable data that can reveal the nature of materials as much as possible. In this study, a procedure has been developed to identify the formability of perovskites for all of the compounds with the stoichiometry of ABX3 and (A′A″)(B′B′′)X6 that exist in experiments and are stored in the Materials Projects database. Our results have enriched the data of perovskite formability to a large extent and corrected the possible errors of previous data in ABO3 compounds. Furthermore, machine learning with a multiple models approach has identified the A2B′B″O6 compounds that have suspicious formability results in the current experimental data. Therefore, further experimental validation experiments are called for. This work paves a way for cleaning perovskite formability data for reliable machine-learning work in future.
This study explored the application of generative pre-trained transformer (GPT) agents based on medical guidelines using large language model (LLM) technology for traumatic brain injury (TBI) ...rehabilitation-related questions. To assess the effectiveness of multiple agents (GPT-agents) created using GPT-4, a comparison was conducted using direct GPT-4 as the control group (GPT-4). The GPT-agents comprised multiple agents with distinct functions, including "Medical Guideline Classification", "Question Retrieval", "Matching Evaluation", "Intelligent Question Answering (QA)", and "Results Evaluation and Source Citation". Brain rehabilitation questions were selected from the doctor-patient Q&A database for assessment. The primary endpoint was a better answer. The secondary endpoints were accuracy, completeness, explainability, and empathy. Thirty questions were answered; overall GPT-agents took substantially longer and more words to respond than GPT-4 (time: 54.05 vs. 9.66 s, words: 371 vs. 57). However, GPT-agents provided superior answers in more cases compared to GPT-4 (66.7 vs. 33.3%). GPT-Agents surpassed GPT-4 in accuracy evaluation (3.8 ± 1.02 vs. 3.2 ± 0.96, p = 0.0234). No difference in incomplete answers was found (2 ± 0.87 vs. 1.7 ± 0.79, p = 0.213). However, in terms of explainability (2.79 ± 0.45 vs. 07 ± 0.52, p < 0.001) and empathy (2.63 ± 0.57 vs. 1.08 ± 0.51, p < 0.001) evaluation, the GPT-agents performed notably better. Based on medical guidelines, GPT-agents enhanced the accuracy and empathy of responses to TBI rehabilitation questions. This study provides guideline references and demonstrates improved clinical explainability. However, further validation through multicenter trials in a clinical setting is necessary. This study offers practical insights and establishes groundwork for the potential theoretical integration of LLM-agents medicine.
Strain serves as a powerful freedom to effectively, reversibly, and continuously engineer the physical and chemical properties of two-dimensional (2D) materials, such as bandgap, phase diagram, and ...reaction activity. Although there is a high demand for full characterization of the strain vector at local points, it is still very challenging to measure the local strain amplitude and its direction. Here, we report a novel approach to monitor the local strain vector in 2D molybdenum diselenide (MoSe2) by polarization-dependent optical second-harmonic generation (SHG). The strain amplitude can be evaluated from the SHG intensity in a sensitive way (−49% relative change per 1% strain); while the strain direction can be directly indicated by the evolution of polarization-dependent SHG pattern. In addition, we employ this technique to investigate the interlayer locking effect in 2H MoSe2 bilayers when the bottom layer is under stretching but the top layer is free. Our observation, combined with ab initio calculations, demonstrates that the noncovalent interlayer interaction in 2H MoSe2 bilayers is strong enough to transfer the strain of at least 1.4% between the bottom and top layers to prevent interlayer sliding. Our results establish that SHG is an effective approach for in situ, sensitive, and noninvasive measurement of local strain vector in noncentrosymmetric 2D materials.
The implementation of a progressive rehabilitation training model to promote patients' motivation efforts can greatly restore damaged central nervous system function in patients. Patients' active ...engagement can be effectively stimulated by assist-as-needed (AAN) robot rehabilitation training. However, its application in robotic therapy has been hindered by a simple determination method of robot-assisted torque which focuses on the evaluation of only the affected limb's movement ability. Moreover, the expected effect of assistance depends on the designer and deviates from the patient's expectations, and its applicability to different patients is deficient. In this study, we propose a control method with personalized treatment features based on the idea of estimating and mapping the stiffness of the patient's healthy limb. This control method comprises an interactive control module in the task-oriented space based on the quantitative evaluation of motion needs and an inner-loop position control module for the pneumatic swing cylinder in the joint space. An upper-limb endpoint stiffness estimation model was constructed, and a parameter identification algorithm was designed. The upper limb endpoint stiffness which characterizes the patient's ability to complete training movements was obtained by collecting surface electromyographic (sEMG) signals and human-robot interaction forces during patient movement. Then, the motor needs of the affected limb when completing the same movement were quantified based on the performance of the healthy limb. A stiffness-mapping algorithm was designed to dynamically adjust the rehabilitation training trajectory and auxiliary force of the robot based on the actual movement ability of the affected limb, achieving AAN control. Experimental studies were conducted on a self-developed pneumatic upper limb rehabilitation robot, and the results showed that the proposed AAN control method could effectively estimate the patient's movement needs and achieve progressive rehabilitation training. This rehabilitation training robot that simulates the movement characteristics of the patient's healthy limb drives the affected limb, making the intensity of the rehabilitation training task more in line with the patient's pre-morbid limb-use habits and also beneficial for the consistency of bilateral limb movements.
Heterostructures based on graphene and other 2D atomic crystals exhibit fascinating properties and intriguing potential in flexible optoelectronics, where graphene films function as transparent ...electrodes and other building blocks are used as photoactive materials. However, large‐scale production of such heterostructures with superior performance is still in early stages. Herein, for the first time, the preparation of a submeter‐sized, vertically stacked heterojunction of lead iodide (PbI2)/graphene on a flexible polyethylene terephthalate (PET) film by vapor deposition of PbI2 on graphene/PET substrate at a temperature lower than 200 °C is demonstrated. This film is subsequently used to fabricate bendable graphene/PbI2/graphene sandwiched photodetectors, which exhibit high responsivity (45 A W−1 cm−2), fast response (35 µs rise, 20 µs decay), and high‐resolution imaging capability (1 µm). This study may pave a facile pathway for scalable production of high‐performance flexible devices.
Epitaxial growth of 2D PbI2 crystals on a graphene film is reported using a low‐temperature vapor deposition method with a lattice matching design. After a simple one‐step transfer and encapsulation, a graphene/PbI2/graphene sandwich structure is obtained and fabricated into an ultrathin device with a good flexibility for photodetection and imaging.
Two dimensional (2D) materials-based plasmon-free surface-enhanced Raman scattering (SERS) is an emerging field in nondestructive analysis. However, impeded by the low density of state (DOS), an ...inferior detection sensitivity is frequently encountered due to the low enhancement factor of most 2D materials. Metallic transition-metal dichalcogenides (TMDs) could be ideal plasmon-free SERS substrates because of their abundant DOS near the Fermi level. However, the absence of controllable synthesis of metallic 2D TMDs has hindered their study as SERS substrates. Here, we realize controllable synthesis of ultrathin metallic 2D niobium disulfide (NbS2) (<2.5 nm) with large domain size (>160 μm). We have explored the SERS performance of as-obtained NbS2, which shows a detection limit down to 10–14 mol·L–1. The enhancement mechanism was studied in depth by density functional theory, which suggested a strong correlation between the SERS performance and DOS near the Fermi level. NbS2 features the most abundant DOS and strongest binding energy with probe molecules as compared with other 2D materials such as graphene, 1T-phase MoS2, and 2H-phase MoS2. The large DOS increases the intermolecular charge transfer probability and thus induces prominent Raman enhancement. To extend the results to practical applications, the resulting NbS2-based plasmon-free SERS substrates were applied for distinguishing different types of red wines.
Three-dimensional printing (3 Dp) is being increasingly used in medical education. Although the use of such lifelike models is beneficial, well-powered, randomized studies supporting this statement ...are scarce. Two spinal fracture simulation models were generated by 3 Dp. Altogether, 120 medical students (54.2% females) were randomized into three teaching module groups two-dimensional computed tomography images (CT), 3D, or 3 Dp and asked to answer 10 key anatomical and 4 evaluative questions. Students in the 3 Dp or 3D group performed significantly better than those in the CT group, although males in the 3D group scored higher than females. Students in the 3 Dp group were the first to answer all questions, and there were no sex-related differences. Pleasure, assistance, effect, and confidence were more predominant in students in the 3 Dp group than in those in the 3D and CT groups. This randomized study revealed that the 3 Dp model markedly improved the identification of complex spinal fracture anatomy by medical students and was equally appreciated and comprehended by both sexes. Therefore, the lifelike fracture model made by 3 Dp technology should be used as a means of premedical education.
In the era of big data, text-based medical data, such as electronic health records (EHR) and electronic medical records (EMR), are growing rapidly. EHR and EMR are collected from patients to record ...their basic information, lab tests, vital signs, clinical notes, and reports. EHR and EMR contain the helpful information to assist oncologists in computer-aided diagnosis and decision making. However, it is time consuming for doctors to extract the valuable information they need and analyze the information from the EHR and EMR data. Recently, more and more research works have applied natural language processing (NLP) techniques, i.e., rule-based, machine learning-based, and deep learning-based techniques, on the EHR and EMR data for computer-aided diagnosis in oncology. The objective of this review is to narratively review the recent progress in the area of NLP applications for computer-aided diagnosis in oncology. Moreover, we intend to reduce the research gap between artificial intelligence (AI) experts and clinical specialists to design better NLP applications. We originally identified 295 articles from the three electronic databases: PubMed, Google Scholar, and ACL Anthology; then, we removed the duplicated papers and manually screened the irrelevant papers based on the content of the abstract; finally, we included a total of 23 articles after the screening process of the literature review. Furthermore, we provided an in-depth analysis and categorized these studies into seven cancer types: breast cancer, lung cancer, liver cancer, prostate cancer, pancreatic cancer, colorectal cancer, and brain tumors. Additionally, we identified the current limitations of NLP applications on supporting the clinical practices and we suggest some promising future research directions in this paper.
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