Based on the extraordinary electromagnetic properties of terahertz waves, such as broadband, low energy, high permeability, and biometric fingerprint spectra, terahertz sensors show great application prospects in the biochemical field. However, the sensitivity of terahertz sensing technology is increasingly required by modern sensing demands. With the development of terahertz technology and functional materials, graphene-based terahertz metasurface sensors with the advantages of high sensitivity, fingerprint identification, nondestructive and anti-interference are gradually gaining attention. In addition to providing ideas for terahertz biosensors, these devices have attracted in-depth research and development by scientists. An overview of graphene-based terahertz metasurfaces and their applications in the detection of biochemical molecules is presented. This includes sensor mechanism research, graphene metasurface index evaluation, protein and nucleic acid sensors, and other chemical molecule sensing. A comparative analysis of graphene, nanomaterials, silicon, and metals to develop material-integrated metasurfaces. Furthermore, a brief summary of the main performance results of this class of devices is presented, along with suggestions for improvements to the existing shortcoming. Terahertz supersurfaces and graphene materials for spectroscopic analysis.Elucidating the mechanism of terahertz detection of biochemical molecules.Application of terahertz to biochemical molecule detection. Terahertz waves are electromagnetic waves with a wavelength of 0.03–3 mm and a frequency in the range of 0.1–10 THz . CurrentTerahertz waves lie between infrared rays and microwaves. The response of biochemical molecules to terahertz radiation mainly comes from collective vibrational modes, such as vibrations and conformational bending, determined by biochemical molecular conformation and chemical bonding. Biochemical molecules dominate intramolecular vibrations, including weak interactions, hydrogen bonds, van der Waals forces, etc. –6, proviThere is a need to find high-performance and stabilizing terahertz devices. Graphene as a two-dimensional material has been widely used in recent years . CombiniThe challenges faced by terahertz biomolecular sensors include metasurfaces that have the ability to be efficient and selective in capturing specific biomolecular target substances. To enhance the detection of biochemical molecules target substances on the metasurfaces improve the detection effect . Among tIn the detection of terahertz metasurfaces sensing, when terahertz waves pass through the metasurfaces unit structure and some periodic subwavelength resonators with small thickness are added to the medium, the distributions of the reflection and refraction field at the boundary of the medium determine the boundary of the homogeneous medium, two reflect and refraction properties . TeraherThe four of valence electrons populate the sp orbitals, forming an extra-domain electron cloud, making graphene with ultra-high carrier migration, and resonance conditions of the electric field in order to excite the plasma resonance, graphite can be actively modulated by doping and applying gate pressure , 58, as Surface plasmon resonance (SPR) is also a sensing mechanism, and sensors based on SPR through the interaction between the sensing element and the metasurfaces , 61. ThiConsidering the scattering of the graphene plasma on the time discontinuity for a confined plasma, the projection and reflection coefficients of the photons in the plasma are analyzed, as given in Eqs. –6). It . It 6). Terahertz biochemical sensing developed by applying graphene involved in the design of the metasurfaces has enabled the detection of biochemical molecules such as; proteins, nucleic acids, viruses, pesticides, and other biochemical molecules. The electromagnetic environment in the vicinity of the graphene metasurfaces sensing can be significantly enhanced by the change in Fermi energy level caused by external induction, graphene metasurfaces to enhance sensor detection sensitivity.Terahertz waves are associated with subwavelength resonant cell unit structures and material properties. Terahertz wave metasurfaces are combined with graphene, and their metasurfaces interact to trigger plasmon resonant modes, circulating dipole polarization, Fano resonances, and capacitive resonance , 68. TheQ), sensitivity (S), and resonance characteristics (FOM) [Q reflects the quality of the resonance and reflects the resonance field enhancement produced by the metasurfaces resonance unit. The Q value is defined as  –72. The fined as .9\documef is the resonant center frequency and FWHM is the half-peak width.When the biochemical molecules to be detected cause changes in the frequency of the resonance spectrum, it can be used for the purpose of qualitative and quantitative detection of the biochemical molecules to be tested measured, and real-time detection of targets. Sensitivity is an index to measure the change of resonant frequency of biochemical target molecules with the refractive index, and it is an important index for the detection performance of terahertz metasurface detection, which can be expressed as .10\documf is the resonance shift and Δn is the amount of variation in the molecular change properties of the target to be measured. Resonance characteristics FOM to compare the sensing performance of metamaterial sensors. FOM can be expressed as Eq. (Δd as Eq. .11\documThe FOM reveals the resonant properties of the sensor metasurface itself and the corresponding capabilities of the measured biochemical molecules. This equation can be understood as the relationship between the factors controlling the detection performance to balance the resonance characteristics of the sensor device and improve the interaction with the biochemical molecules to be detected. Research into terahertz sensor technology and detection is a top priority.Graphene has been identified as one of the most attractive materials in recent decades . GraphenGraphene terahertz biosensor is a new detection and analysis method based on the development of the terahertz wave spectroscopy analysis method, as shown in Fig. −1 is achieved. Meanwhile, Zhang et al. [Ruan et al. developed an ultrasensitive terahertz biosensor with a graphene, waveguide hybrid Fano structure resonance as shown in Fig. g et al.  proposed2 as a substrate, with a single layer of graphene film inserted between the vertical structures as shown in Fig. −1. Graphene-based biosensors or gas sensors have promising applications in the terahertz frequency range.Tang et al.  proposedQ-factor of 13.76, the resonance peak of refractive index sensitivity of the analyte to be measured, and sensitivity of 851 GHz/RIU results, as shown in Fig. Nickpay et al.  proposedGraphene sensor devices with good selectivity, sensitivity and other advantages have attracted much attention , 86. GraProteins are the executing molecules that respond to instructions stored in the genetic material of any life form, and protein-targeted sensors can be made by immobilizing specific biomolecules on a surface . However16-22 peptides are the main marker of Alzheimer's disease. Xu et al. [16–22 peptides as shown in Fig. 16–22 peptides due to the change of the Fermi energy level leading to the change of the position of the Rabi splitting peak as in Fig. 16-22 decreases, the Fermi energy level of p-doped graphene moves away from the Dirac point with the aggregation process, and the movement of the Fermi energy level of graphene is important due to the stacking of the π–π bond between Aβ16-22 peptides and graphene.The Aβu et al.  proposedYao et al.  developeParmar et al.  proposedMetasurfaces learned by this method allow the detection of other components such as metal ions, carbon dioxide, other aromatic molecules, etc. . TherefoDNA is a macromolecular polymer composed of deoxyribonucleic acid. It is a biomolecule of biological genetic information. It is very critical for the detection of biological macromolecules to provide information on living individuals, and DNA biosensors are widely used in different fields of clinical medical diagnosis, environmental detection, and drug development , 92. Theπ–π stacking with DNA molecules tightly bound; the nano-slot resonance assisted by strongly enhanced and aggregated terahertz waves, even in the presence of nmol/mm2 levels, can be observed [By designing graphene-combined nano-grooves with terahertz metasurfaces as shown in Fig. observed . More DNXu et al.  the applYang et al.  the propE. coli O157:H7 first. Selective detection of 100 nM DNS solution was achieved. Through mechanistic analysis due to n-type molecules doped by non-covalent surface, including van der Waals forces, π–π stacking, and hydrogen bonding. The p-type doped graphene Fermi energy levels move to the Dirac point, in both structures graphene on the metasurfaces of a large number of molecules will move the Fermi energy level to the Dirac point, resulting in the formation of the original graphene has a lower photoconductivity, thus higher transmittance and microfluidics binding to provide highly sensitive detection [Meanwhile, metasurfaces-assisted terahertz real-time label-free biosensors have attracted extensive research, and high sensitivity in the terahertz range is still challenging for specific detection of highly absorbing liquid samples or water molecular liquids, water has strong absorption of terahertz waves, to overcome these drawbacks researchers have used a microfluidic technique combined. Zhou et al.  developeetection .Fig. 17TAs an efficient platform for biosensing in liquid environments, it can selectively and label-free detect biomolecules. By changing the corresponding DNA sequence, the sensor can be altered to detect other different DNA molecules. It results in that the graphene-bound terahertz metasurface platform not only affords a broad research field for future medical applications but also proposed theoretical support for the optoelectronic effects of thin films such as 2D materials.Terahertz propagating in free space interacts weakly with the sample and is not sufficient for trace detection. By transferring graphene to the sample surface, the metasurfaces absorber constructs a graphene metasurfaces absorber heterogeneous structure as shown in Fig. The accurate and rapid detection in the terahertz band of this study contribute a novel mean of detection. Zhao et al.  proposedDing et al.  developeA bifunctional covalent scaffold for modification with metasurface functionalization was reported by Esteban Piccinini et al.  as shownThe graphene nanomaterials transformed metasurfaces devices have gained the attention of researchers. Graphene metasurfaces heterostructures become a research thermoelectric for terahertz wave modulators , 104. GrAlthough there are many reports on graphene terahertz ultra-surfaces, there are still many difficulties to develop from the research field to the commercial field, including the detection limit of graphene terahertz ultra-surfaces in practical analysis, the stability of biochemical molecules, and the hysteresis of biomolecule detection . Its nonIn summary, the combination of metasurfaces and optical matter obviously enhances the effect of becoming the focus of research on terahertz sensing technology. The development of devices using graphene metasurfaces has made substantial progress. The research using graphene metasurfaces as a starting point using different substrates and materials combinations has significantly enriched the relevant research results. The application of graphene terahertz sensor devices faces many challenges, and we need precise control of graphene generation, homogeneity, and surface functionalization. Current research on terahertz sensing detection technology requires two enhancements. The first of which is the design of the super-surface unit structure, and the reasonable selection of the substrate material is crucial for enhancing terahertz sensing to obtain stronger resonance properties. The second is the effective binding of the molecule to be measured to the target molecule by combining functionalized structural materials and optimization of analytical conditions. This is done while reducing terahertz loss to enhance the sensitivity of detection. However, it is promising that many graphene terahertz devices have achieved fast response and selectivity. Sensing technology is the interconnection of multidisciplinary technologies. With the development of novel spectroscopic techniques, data analysis algorithms, and materials science fields, technological advances are expected to promote the flourishing of biochemical sensing in biochemical detection, device performance, and applications in the foreseeable future.


ChatGPT is a large language model that has performed well on professional examinations in the fields of medicine, law, and business. However, it is unclear how ChatGPT would perform on an examination assessing professionalism and situational judgement for doctors.We evaluated the performance of ChatGPT on the Situational Judgement Test (SJT): a national examination taken by all final-year medical students in the United Kingdom. This examination is designed to assess attributes such as communication, teamwork, patient safety, prioritization skills, professionalism, and ethics.All questions from the UK Foundation Programme Office’s (UKFPO’s) 2023 SJT practice examination were inputted into ChatGPT. For each question, ChatGPT’s answers and rationales were recorded and assessed on the basis of the official UK Foundation Programme Office scoring template. Questions were categorized into domains of Good Medical Practice on the basis of the domains referenced in the rationales provided in the scoring sheet. Questions without clear domain links were screened by reviewers and assigned one or multiple domains. ChatGPT's overall performance, as well as its performance across the domains of Good Medical Practice, was evaluated.Overall, ChatGPT performed well, scoring 76% on the SJT but scoring full marks on only a few questions (9%), which may reflect possible flaws in ChatGPT’s situational judgement or inconsistencies in the reasoning across questions (or both) in the examination itself. ChatGPT demonstrated consistent performance across the 4 outlined domains in Good Medical Practice for doctors.Further research is needed to understand the potential applications of large language models, such as ChatGPT, in medical education for standardizing questions and providing consistent rationales for examinations assessing professionalism and ethics. ChatGPT is a large language model developed by OpenAI, which uses deep learning to provide responses to natural language input, by identifying the relationships between words and by generating coherent responses . It achiThese recent advances in language models demonstrate the potentially significant impact of artificial intelligence (AI) technologies on digital health. ChatGPT has already demonstrated its ability to pass professional examinations for postgraduates in the fields of law  and busiWith regard to the United States Medical Licensing Examination (USMLE), ChatGPT scored at, or near, the pass mark for each step of the examination . AlthougThe Situational Judgement Test (SJT) aims to assess many of the skills not covered in the USMLE  and in oOur aim was to evaluate the performance of ChatGPT on the SJT and determine how well it performs across the 4 key domains of Good Medical Practice. To our knowledge, this is the first study to investigate the performance of ChatGPT on a situational judgement and professionalism examination of this type.The ChatGPT model was trained on vast amounts of data from the internet, up to and including 2021, after which it has not been connected to the internet . Hence, The SJT examination is divided into 3 sections, with each question stem first introducing a scenario, followed by a question on how the candidate would approach the situation. These sections include (1) rating the appropriateness or importance of a response, action, or consideration; for example, very appropriate, appropriate, somewhat inappropriate, or inappropriate; (2) multiple-choice questions asking for the 3 most appropriate options from among 8 options; and (3) ranking the appropriateness, or importance, of 5 different actions or considerations in response to a scenario. The SJT is scored on a scale from 0 to 50 points and is not a pass-or-fail examination.Given the discrepancies in correct answers, and justifications among unofficial study resources, we used the most recent official 2023 SJT practice paper, which is publicly available from the official United Kingdom Foundation Programme Office (UKFPO) website , togetheEach question from the 2023 SJT practice paper was formatted identically into the ChatGPT text with the following additions: (1) the official candidate examination instructions were provided before each scenario , and (2)We used the official UKFPO scoring templates to determine the number of marks scored by ChatGPT in each of the 3 sections of the examination. The scoring for each question is not binary, and partial marks are awarded for answers that are nearly correct. For example, in the multiple-choice section, each question has 3 correct answers from a choice of 8 options; each correct answer is awarded 4 points with a maximum of 12 points per question. Therefore, a candidate can score 0, 4, 8, or 12 marks for each multiple-choice question. The rating and ranking sections award partial marks for an answer that is close to the correct one. ChatGPT’s performance was calculated as a percentage for each section using the official UKFPO scoring templates. We also determined the proportion of questions for which the answers were correct (defined as scoring 100% of the available marks for the given question), mostly correct (50%-99%), and mostly incorrect (<50%) for each section.The final SJT score provided to candidates is on a scale from 0 to 50, which is based on test-equating and scaling the raw marks achieved on the paper. This conversion formula varies between sittings and is not made publicly available by the UKFPO. We, therefore, reported ChatGPT’s performance as a percentage instead of reporting it on the 0-50–point scale, which is normally used to compare performance between human candidates. Both the SJT and Educational Performance Measure scores determine a final-year medical student’s ranking when applying to the Foundation Programme. The Educational Performance Measure is a measure of performance in medical school up to the point of application to the Foundation Programme with students grouped into deciles.In order to assess ChatGPT’s performance across the different domains of Good Medical Practice, each question was categorized into at least 1 domain. To classify the questions, we used the 2023 practice paper answer sheet provided by the UKFPO, which also contains the rationale for most answers. Many of the rationales contained direct references to at least 1 domain from the Good Medical Practice guidelines that were used for categorization. Questions with rationales, which had missing links to the domains, were categorized by 2 independent reviewers on the basis of both the question itself and the rationale provided by the UKFPO. Both reviewers recently completed the Foundation Programme on which this SJT examination is based. The reviewers were blinded to each other’s categorization of each question, and disagreements were resolved by a third reviewer who was a consultant radiologist within the National Health Service and was blinded to the categorizations made by the 2 initial reviewers. Once all questions in the examination were assigned domains combining the rationales offered by the UKFPO and the screening approach used for the remaining questions, ChatGPT’s performance in each domain of Good Medical Practice was assessed using the official scoring templates and reported as a percentage.A summary of the workflow for this study including sourcing, encoding, adjudicating results, and assessing performance can be found in This study did not involve human or animal participants and ethics approval was not required.Overall, ChatGPT scored 76% (929 of a possible 1217 marks) on this SJT examination .For the rating section of the examination, ChatGPT scored 78% (197/253 marks) with 0% (0/18 questions) entirely correct, 100% (18/18 questions) mostly correct, and 0% (0/18 questions) mostly incorrect responses .For the multiple-choice section of the examination, ChatGPT scored 65% (172/264 marks) with 23% (5/22 questions) entirely correct, 50% (11/22 questions) mostly correct, and 27% (6/22 questions) mostly incorrect responses .For the ranking section of the examination, ChatGPT scored 80% (560/700 marks) with 6% (2/35 questions) entirely correct, 94% (33/35 questions) mostly correct, and 0% (0/35 questions) mostly incorrect responses .There were 170 questions and answer statements that were classified into at least one of the GMC domains. Of these, 84  were explicitly linked to a GMC domain within the rationale provided in the UKFPO’s official answer sheet. The independent reviewers then screened the remaining 86 and agreed on which GMC domains they applied to for 76 (88%) of them. The remaining 10 were then assessed by the tiebreaker (consultant radiologist), and their labels were used for the analysis.ChatGPT scored 78% (328/419) in the Knowledge, Skills and Performance domain, 76% (484/635) in the Safety and Quality domain, 76% (340/448) in the Maintaining Trust domain, and 75% (784/1046) in the Communication, Partnership and Teamwork domain.In the rating section, ChatGPT’s worst performance was noted on a question related to the appropriateness of specific actions after discovering that a medical student has likely acquired detailed information about the scenarios that will feature in an upcoming examination. The official answers and rationale advise that it would be (1) somewhat appropriate to inform the medical student that their Educational Supervisor will be informed about the situation and (2) very appropriate to encourage the student to inform the medical school that they have acquired information about the examination. ChatGPT labeled these options as inappropriate and somewhat inappropriate, respectively, with the rationale that (1) “Threatening to inform her Educational Supervisor about her behaviour is not a productive or supportive approach…It is important to remember that as a facilitator, the doctor’s role is to support and guide the student in their learning, not to police their behaviour” and (2) “While it is important [for the student] to be honest about how she obtained information, encouraging her to declare this to the medical school may be premature at this point. It may be more appropriate to first have a conversation with [the student] to understand why she sought additional information and to provide guidance on appropriate conduct.”In the ranking section of the examination, ChatGPT scored its lowest marks (50%) on a question asking about the appropriateness of certain actions when one has arrived home after one’s shift and realizes that they forgot to handover an urgent blood sample that needs to be taken today. ChatGPT deemed returning to the ward immediately to perform the blood test as the most appropriate action, whereas the official marking labeled this is as one of the less appropriate options. ChatGPT also ranked telephoning the ward and leaving a message with the nursing team as a less appropriate option because “the nursing team may not have the necessary information or authority to take appropriate action for the patient,” while the official marking classified this as one of the more appropriate actions.We evaluated the performance of ChatGPT on the SJT: a national examination for final-year medical students in the United Kingdom, which assesses attributes including communication, teamwork, patient safety, prioritization skills, professionalism, and ethics. Overall, ChatGPT scored 76% on the examination. It answered 0%, 23%, and 6% of the questions entirely correctly in the rating, multiple-choice, and ranking sections of the examination, respectively, but was mostly correct for 100%, 50%, and 94% of the questions in these sections. ChatGPT scored consistently across the 4 key domains of Good Medical Practice.ChatGPT’s overall performance was impressive considering that it was correct or mostly correct for the majority of questions in the examination. However, the proportion of the questions that were answered with 100% accuracy was lower than expected with its best performance being in the multiple-choice section, in which it chose the 3 correct options in approximately one-fourth of the questions. This could be due to flaws in ChatGPT’s reasoning in some of these situations. However, ChatGPT’s low proportion of entirely correct (100%) answers may also reflect inconsistencies within the examination itself. Several reports and student commentaries have suggested that there are significant discrepancies in the correct answers chosen by different experts . If thisInterestingly, ChatGPT scored 65% in the multiple-choice section versus 78% and 80% in the rating and ranking sections, respectively. This may reflect that this large language model is better suited to tasks that involve ranking and prioritization rather than selecting from a list of most appropriate, or relevant, options for a given scenario. ChatGPT has been trained on a wide gamut of data available from the internet, which may not always be factually correct, but amalgamated together means that the model may be more competent at dealing with open-ended questions which involve listing options in order of importance or relevance, as opposed to questions with individual correct answers.ChatGPT performed consistently across the 4 domains of Good Medical Practice, having scored between 75% and 78% across them. ChatGPT performed slightly better in the Knowledge, Skills and Performance and Safety and Quality domains than in the Communication, Partnership and Teamwork domain. We speculate that this could be explained by questions pertaining to knowledge and safety being more objective in nature, whereby patient safety and delivering high-quality care are always prioritized. These types of scenarios may provide ChatGPT with a more straightforward approach to classifying the appropriateness of the options, compared to questions pertaining to communication and teamwork where decision-making is more subjective and nuanced. However, the differences in ChatGPT’s performance across these domains were too small to provide more definitive insight.There were several limitations in this study: First, in practice, the raw score for this examination is converted to a 0-50–point scale, which is based on test-equating and a scaling conversion method that is not publicly available. We also do not have access to the results of medical students taking this practice examination and are therefore unable to directly compare ChatGPT’s performance to that of final-year medical students. Second, the answer sheet and rationales provided by the UKFPO for this examination only explicitly linked 49% of the questions and answer statements to the GMC domains outlined in Good Medical Practice. We therefore devised a method to link the remaining questions to the domains, which involved 2 independent reviewers and a tiebreaker, the results of which may have differed from those of the UKFPO. Third, many questions pertained to more than 1 domain of Good Medical Practice; hence, there was an overlap in questions across different domains when assessing ChatGPT’s performance in each domain. Fourth, our search was run on the February 2023 version of ChatGPT, and given the constant development of this large language model, future iterations may yield different outcomes.Overall, ChatGPT performed well in the examination but scored 100% for only a few questions, which may reflect inconsistencies in the examination or errors in ChatGPT’s reasoning (or both). This builds on the existing literature by demonstrating that AI-driven large language models such as ChatGPT not only perform well on a wide range of clinically based examinations, but also offer, for the most part, rational responses to professional and ethical dilemmas faced by doctors. Future research should focus on identifying patterns and inconsistencies in the ethical approaches of AI language models and mitigating potential biases in them. Directly comparing the performance of these types of models with that of human candidates in relation to situational judgement dilemmas will provide more direct insight into their performance relative to that of humans. If the ethical foundations of models such as ChatGPT are deemed appropriate and reliable, it would provide the opportunity for integration directly into medical education with, for example, interactive platforms, simulated scenarios related to situational judgement, and personalized feedback, as well as standardization of examinations. Finally, in order to achieve this, it will be crucial to use a collaborative approach among experts in AI, medicine, and medical education to realize the full potential of these new technologies. Addressing these points will help develop this field and promote the integration of large language models, such as ChatGPT, into medical education, thus helping to standardize assessments that evaluate professionalism and ethics while maintaining high-quality and equitable medical education standards.


High-quality graphene-based van der Waals superlattices are crucial for investigating physical properties and developing functional devices. However, achieving homogeneous wafer-scale graphene-based superlattices with controlled twist angles is challenging. Here, we present a flat-to-flat transfer method for fabricating wafer-scale graphene and graphene-based superlattices. The aqueous solution between graphene and substrate is removed by a two-step spinning-assisted dehydration procedure with the optimal wetting angle. Proton-assisted treatment is further used to clean graphene surfaces and interfaces, which also decouples graphene and neutralizes the doping levels. Twist angles between different layers are accurately controlled by adjusting the macroscopic stacking angle through their wafer flats. Transferred films exhibit minimal defects, homogeneous morphology, and uniform electrical properties over wafer scale. Even at room temperature, robust quantum Hall effects are observed in graphene films with centimetre-scale linewidth. Our stacking transfer method can facilitate the fabrication of graphene-based van der Waals superlattices and accelerate functional device applications. The large-scale fabrication of twisted van der Waals heterostructures remains challenging due to the formation of defects and contaminations during the transfer process. Here, the authors report a transfer method to fabricate graphene-based van der Waals superlattices at the wafer scale, showing controllable twist angles and robust quantum Hall effect. Based on the vdWS assembled from 2D conductors, semiconductors, ferromagnets and superconductors, a series of functional devices have been constructed, such as vertical field effect tunnelling transistors3, superconducting Josephson junctions4, spintronic memories5 and photodetectors6. Nowadays, there are mainly two strategies to build 2D vdWS with wafer scale, including the bottom-up stacking growth8 and the top-down transfer10. Therein, chemical vapour deposition (CVD) growth of various large-sized transition metal dichalcogenides (TMDCs) heterostructures has been demonstrated8. Moreover, benefiting from the weak coupling between the wafer-scale TMDCs with the growth substrate, one layer-by-layer transfer method of namely programmed vacuum stack has also been developed for building the TMDCs-based vdWS10.Two-dimensional (2D) van der Waals superlattices (vdWS), which are vertically stacked by individual 2D layered materials, offer a platform for exploring physical properties and functionality11. Furthermore, graphene-based vdWS have also revealed numerous fresh physical properties. Such superlattice minibands of Hofstadter’s butterfly13, unconventional superconductivity14, correlated insulator behaviour15, ferromagnetism16, quantized anomalous Hall effect17 etc., are stimulated in the stacked graphene/hBN or double-layer graphene by precisely controlling their twist angles. Graphene-based vdWS offer possibilities for creating a generation of ultrathin transparent functional devices18. However, there are a few effective methods to stack growth double-layer graphene with controllable twist angles20, but the stacking growth strategy is usually hindered by the harsh growth conditions. Wafer-scale graphene films are typically grown via CVD method at a relatively high temperature 21, which is much higher than most of other 2D materials. Therefore, graphene is commonly used as the underlying layer while growing in the stack manner23, which makes it difficult to integrate graphene into the programmed order of vdWS. In contrast, the layer-by-layer transfer shows more flexibility to build diverse graphene-based vdWS. Actually, most graphene-based superlattices with specific properties are fabricated through the modified dry transfer technique24, where the graphene or other 2D materials are limited to dozens of microns. Until now, it is relatively easy to realize the transfer of wafer-scale monolayer graphene34, and there are also a few approaches for manipulating the twist angles between the adjacent graphene layers37. However, stacking homogeneous graphene-based vdWS at wafer size remains a challenge, and the homogeneous vdWS with controlled twisted angles is more difficult.Graphene, as the first discovered and highly conductive 2D material with remarkable mechanical and electrical properties, is likely to be the most compatible component of future vdWS and their functional devices39. Wrinkles or some folds, which are formed during CVD growth at high temperature, can be eliminated by the proton-assisted or low-temperature methods40. However, most folds, cracks and tears are induced by the inappropriate transfer operation of pasting graphene on the target substrate or other 2D materials38, where the trapped water or residual nanoparticles (NPs) should be the main obstacles41. Once graphene is firmly attached to substrate, it is difficult to reduce the trapped water, NPs and consequent folds through the process like nitrogen gas blow42. The adsorbed water also causes the heavily doping state of graphene and degenerates their electrical performance43. Thus, the transfer defects and the residues on the bottom 2D materials should be completely eliminated if stacking transfer a homogeneous vdWS. Furthermore, the hydrophilic substrates show more helpful for transferring defect-free graphene films than the hydrophobic ones30, the substrates with flatter surfaces are also better than the rough ones44, and the residual NPs trapped in the interlayers of stacked vdWS are considered to be avoided through the layer-by-layer hydrogen annealing45. However, most 2D materials are approximately hydrophobic, and the wetting angle of DI water on intrinsic graphene (θ1) is more than 80 degree46, hence the high-quality stacking transfer of wafer-scale graphene-based vdWS is still plagued by the trapped air, water or NPs in the interlayers.During traditional operations, the macroscopical transfer defects are the main trouble for the homogenous feature and the subsequent stacking transferIn this work, we develop a flat-to-flat transfer method, i.e. transferring flat graphene films onto the flat substrate, which consists of the spinning-assisted process and the proton-assisted treatment (PAT) process which is based on the permeable protons and recombined hydrogen, to exhaust the trapped water and decouple the transferred graphene. The wafer-scale monolayer graphene and graphene-based vdWS with ultra-flat surface and scarcely transfer defects are obtained through adjusting the appropriate wetting angles in different aqueous solutions. The twist angles of double-layer and triple-layer graphene can be precisely adjusted by the macroscopically stacking angles according to the wafer flats of graphene. The transferred graphene and graphene-based vdWS exhibit excellent morphological, structural, optical, and electrical homogeneity over the wafer size. Robust quantum Hall effect (QHE) arises in the transferred graphene films, and even appears at room temperature  with linewidth of centimetres.2/Si under wet condition. Because the wetting angle of DI water on SiO2/Si (θ2) is usually less than 60 degree30, the approximative hydrophilia makes DI water can be used as the transfer solution here . Atomic force microscope (AFM) measurements show that the thickness of the DI water layer trapped between graphene and SiO2/Si is ~40 nm and the roughness of PMMA/graphene/DI water/SiO2/Si is ±25 nm  trapped in the interface between graphene and substrate, which are difficult to be eliminated through the thermal annealing  over 100 × 100 μm2 of transferred graphene after different treatments are shown in Fig. 2D/IG of the as-transferred graphene is concentrated at the values of 1.6 ± 0.3, demonstrating its monolayer nature. The I2D/IG decreased to 1 ± 0.2 after vacuum annealing, mostly caused by the annealing introduced substrate doping47. In contrast, the distribution of I2D/IG after PAT is more uniform and exhibits minimal variation. Typical Raman mapping for I2D/IG of the as-transferred graphene and graphene after annealing are shown in supplementary Fig. D/IG of the graphene after PAT are plotted in supplementary Fig. 21. Figure ω2D), and it shows that the as-transferred graphene presents the least value of Δω2D, indicating the weakest coupling between graphene and the substrate. After vacuum annealing, the Δω2D become the largest, meaning that the interaction is significantly enhanced. In contrast, Δω2D after PAT process present much smaller, indicating the PAT graphene remains the similar coupling state with the as-transferred film, and PAT is able to decouple graphene from the substrate and weaken their coupling. The detailed spectra are plotted in supplementary Fig. Subsequently, the statistical distribution of Raman intensity ratio of 2D to G peaks (IVbg), and the inset shows the typical optical image of the Hall bars with 200 μm linewidth. The as-transferred graphene films show the heaviest p-doping level with the carrier density of 4.4 × 1012 cm–2, where the Dirac point is located at Vbg of 58 V. This should reasonably be attributed to the large amounts of residual NPs and the adsorbed H2O molecules. After the vacuum annealing, the Dirac point of graphene film shifts to 16 V, i.e. the p-doping level is significantly reduced. In contrast, the graphene films after PAT process exhibits the weakest doping level with the carrier density of 2.9 × 1011 cm–2 and Dirac point at 3.8 V, indicating that PAT process can truly decouple graphene and neutralize the doping level. The statistical distribution of Dirac points and the corresponding carrier density of the transferred graphene films are compared in supplementary Fig. 2 V–1 s–1, and the hole mobility increases to ~6950 cm2 V–1 s–1 after vacuum annealing. In contrast, the PAT graphene presents the best state, where the hole and electron mobilities reach ~13,000 cm2 V–1 s–1 and ~10,600 cm2 V–1 s–1 at 1.5 K, and they also reach ~7450 cm2 V–1 s–1 and ~7250 cm2 V–1 s–1 at RT, respectively.We continue to measure the doping levels of transferred graphene films after different treatments via the back-gate field effect transistor (FET) devices, which are batch fabricated by the simple etching through a shadow mask and present the same Hall bar configurations. Figure Rxx) and Hall conductivity (σxy) for the same Hall devices in Fig. B┴) of 6.5 T and the temperature of 1.5 K are plotted in supplementary Fig. Rxx and σxy at different temperatures, where the first σxy plateaus at ±2e2/h easily appears even at RT and the fifth integer plateaus is still obvious under B┴ of 7 T and at 1.5 K. The robust QHE plateaus emerged under relatively mild measurement conditions demonstrate the high homogeneity and high quality of the flat-to-flat transferred films on a wafer scale. Moreover, the hole mobilities of this centimetre-sized graphene film are still up to ~12,000 cm2 V–1 s–1 at 1.5 K and ~9300 cm2 V–1 s–1 at RT, as shown in supplementary Fig. The realizations of QHE usually represent the high electrical quality of transferred graphene films. The longitudinal resistances  and the substrate (θ2) are both hydrophobic, the folds are frequently formed in the stacked graphene films, and the typical optical image is shown in the top inset of Fig. The flat-to-flat method is easily generalized to the stacking transfer of wafer-scale few-layer graphene-based vdWS. Figure 2O clusters could be reduced and excluded from the graphene edges during the PAT process. Furthermore, the residues trapped in the interlayer of double-layer graphene can also be reduced and the double-layer graphene is further flattened through the PAT process. Double-layer graphene films during the multi-cycled PAT processes are non-destructive, and the Ar plasma treatment seems ineffective in eliminating the intralayer residues. All the evolutional results are compared in supplementary Fig. After spinning-assisted transfer, the stacked graphene film is also treated by PAT process. Typical AFM images of the stacking transferred double-layer graphene are shown in Fig. ωG and ω2D for double-layer graphene films, and their distributions are both within the range of ±2.5 cm–1 in large area, indicating high homogeneity. The typical Raman mapping of I2D/IG, FWHM of G and 2D bands, ωG and ω2D across the monolayer, double-layer and triple-layer transition regions are shown in Fig. 48. We further count hundreds of double-layer graphene films which are stacked randomly and find their twist angles approximately follow the normal distribution, as shown in Fig. Figure α) between the upper and bottom graphene films, we can preliminarily obtain a double-layer graphene film with a controlled twist angle, illustrated in Fig. Furthermore, this stacking transfer can be also applied to control the twist angles between the double-layer graphene films at wafer scale. Benefiting from the approximately single crystalline graphene films grown on Cu111)/sapphire  patterns at random locations. Two pieces of 2-inch double-layer graphene films with designed stacking angles of ~6° and ~38°, as well as corresponding SAED patterns, are shown in supplementary Fig. Rxx of the double-layer graphene with ~10° twist angle changes with varying Vbg at 1.5 K, 77 K and RT, and the inset is the typical optical image of the Hall bar with 50 μm linewidth. This double-layer graphene film present slight p-type doping and the hole and electron mobilities reach ~7750 and ~7500 cm2V−1s−1 at 77 K, ~5950 and ~6250 cm2 V–1 s–1 at RT, respectively  and 10° (2nd and 3rd) also shows QHE plateaus at 1.5 K under B┴ up 2 T  films on c-plane sapphire, growth temperature of 650 °C, pressure of 6 Pa, CH4/H2 ratio of 1:20, plasma power of 15 W and growth time of 5 min. The wrinkled graphene films are grown in a tube furnace by the traditional CVD method, typical parameter is as follows: sputtered 800 nm Cu-Ni(111) films on c-plane sapphire and then annealed at 1050 °C under the hydrogen atmosphere for 60 min to flatten the surface. During growth, growth temperature of 1050 °C, CH4/H2/Ar ratio of 0.1:10:500 and growth time of 10 min. To simplify writing, we use Cu(111) instead of Cu-Ni(111) alloy in our descriptions below.Ultra-flat graphene films are grown by the proton-assisted CVD methods as reported before2 and MoSe2 films are grown by the two-step vapour deposition method, which includes the typical physical vapour deposition (PVD) and CVD processes, typical parameters are as follows: for PVD process, sputtered 0.8 nm Mo films on SiO2/Si substrate; then for CVD process, Mo films and S or Se powders are located in a two-zone tube furnace, Mo films are heated to 750 °C and S powder is heated to 150 °C for forming MoS2 films, Mo films are heated to 750 °C and Se powder is heated to 280 °C for MoSe2. The H2/Ar mixture gas is used as carrier gas and their flow rate is both 100 sccm. The growth times are all 30 min. Wafer-sized hBN films are grown in a three-inch tube furnace and assisted with the proton process, typical growth parameter is as follows: sputtered 800 nm Cu(111) films on c-plane sapphire and then annealed at 1050 °C under the hydrogen atmosphere for 60 min to flatten the surface. During growth, flattened Cu(111) substrate is heated to 650 °C, the set pressure is 1 Torr, H2/Ar ratio is 100:10, ammonia borane is used as the precursor and loaded into a sealed chamber at the upstream of the growth substrate with the constant temperature at 70 °C. The plasma power is 40 W and the growth time is 10 min. The grown MoS2, MoSe2 and hBN films are all polycrystalline, and they all have ultra-flat surfaces.Wafer-sized MoS2, MoSe2 films on SiO2/Si are spin-coated with double PMMA films  as the protection layer. Then, 1 M (NH4)2S2O8 aqueous solution is used to etch the Cu(111) substrate and 1 M KOH aqueous solution is used to etch the SiO2. The PMMA/2D material, like PMMA/graphene, is then pulled out from the etchant by a clean polyethylene terephthalate (PET) sheet and moved to DI water for cleaning. The cleaning is normally needed to repeat three times. After that, a two-step spinning-assisted process is developed during pasting the graphene or other 2D materials films onto the flat target substrates (like SiO2/Si). The slow spinning rate of 90–150 rpm is used for the first stage with a spinning time of 6 min, and the fast spinning rate of 300–600 rpm is used for the second stage with a time of 10 min. Then, all the pasted PMMA/graphene on the target substrate is backed at 80 °C for 30 min and at 150 °C for 15 min on a hot plate in sequence. Finally, PMMA is removed by acetone, then the film is dried by a gentle nitrogen gas blow. To further clean the transferred graphene films and decouple them from the substrate, vacuum annealing or PAT is performed. The annealing temperature is 450 °C, the annealing time is 40 min and the vacuum degree is less than 5 × 10–5 Pa. The PAT to decouple graphene is performed at 450 °C under 6 Pa, the H2 plasma power is constant 15 W and treatment time is usually 5 min, and the PAT to clean hBN, MoS2 and MoSe2 needs the temperature of 450 °C, 350 °C, and 350 °C, respectively.The as-grown graphene, hBN films on Cu(111)/sapphire and the as-grown MoS2/graphene, the range of IPA is from 10 vol% to 22.5 vol%, and 15 % of IPA is preferred. For MoS2/MoSe2, the range of IPA is from 7.5 vol% to 17.5 vol%, and 12.5% of IPA is preferred. After the removal of PMMA, the PAT is also performed to clean the surface and the interface between the stacked graphene films. In order to avoid the appearance of C-H bonds during the PAT process, the treatment parameter of double-layer and triple-layer graphene is as follows: H2 plasma of 15 W, treatment time of 5 min, constant pressure of 6 Pa and temperature of 550 °C.During the stacking transfer process, the as-transferred 2D materials like graphene are used as the flat target substrate. A two-step spinning-assisted process which consists of the same transfer process for monolayer graphene is used for stacking the double layers of graphene films. The difference is that the DI water is replaced by mixing IPA or acetic acid in DI water as the transfer solution, which ensures the wetting angle of solution on graphene falls within the range of 40°–60°. For double-layer graphene, the optimal wetting angles for IPA/DI water volume ratio range from 10 vol% to 25 vol%. For hBN/graphene, the range of IPA is from 15 vol% to 25 vol%, and 20 % of IPA is preferred. For MoS–8 Pa), and the probe electron beam size is about 1 mm.The optical images are captured by the optical microscope (Nikon LV100ND). Large-area photographs and wetting angles are captured by a high-resolution digital camera. AFM measurements are carried out by a Bruker Dimension Fastscan system at tapping mode under ambient conditions. Raman spectroscopy is carried out by a Witec/alpha 300R confocal microscope with 532 nm laser under ambient conditions, and the in situ variable temperature Raman is performed in a temperature-controlled stage (Linkam THMS600) with introducing pure Ar. All the laser power is set below 2 mW to avoid heating. SAED is performed by a transmission electron microscope (TECNAI F20) with an accelerating voltage of 200 kV, and the sizes of the selected area are all chosen to be ~1 μm. LEED  is measured using electron beam energy of 170 eV at RT under an ultrahigh vacuum of (1 × 104He cryostat with a superconducting magnet (Oxford Teslatron 8T and lowest temperature 1.5 K) with source metre (Keithley 6430) and lock-in amplifier (Stanford SR830). All the transport data are transformed by tensor inversion. The field effect mobility is extracted by μFE = C-1 (dσ/dVg), where C is the gate capacitance per unit area and calculated as 1.21× 10-4 F/m2 for 285 nm SiO2 layer.The electrical transport measurements are performed in a Supplementary InformationPeer Review File


Covalently interconnected graphene (CIG) nanoscaffoldswith a preadjusted finely tuned graphene layer–layer distancefacilitated the formation of sandwiched US–AuNPs . The elemental composition analysis byX-ray photoelectron spectroscopy displayed an aniline group additionper ∼55 graphene carbon atoms. It provided information on covalentinterconnection via amidic linkages, while Ramanspectroscopy offered evidence of covalent surface functionalizationand the number of graphene layers (≤2–3 layers). High-resolutiontransmission electron microscopy images indicated a layer–layerdistance of 2.04 nm, and low-angle X-ray diffraction peaks (2θat 24.8 and 12.5°) supported a layer–layer distance increasecompared to the characteristic (002) reflection (2θ at 26.5°).Combining covalent bridging with NP synthesis may provide precisecontrol over the metal/metal oxide NP size and arrangement between2D layered materials, unlocking new possibilities for advanced applicationsin energy storage, electrochemical shielding, and membranes.The covalent functionalizationof graphene for enhancing theirstability, improving their electrical or optical properties, or creatinghybrid structures has continued to attract extensive attention; however,a fine control of nanoparticle (NP) size between graphene layers  The gold-filledsandwiches showed the lowest sheet resistance, a red-shifted broadoptical absorption spectrum, and a shifted work function, suggestiveof distinct properties from simple graphene laminations or graphenewith metallic overlayers. In addition, a concept study that followsgraphene growth, nanoparticle decoration, and graphene layer transferto prepare sandwich structures of graphene (or other 2D sheets) wasreported by using various nanoparticles, including Au (∼10–24nm), Pd (∼15 nm), and PbSe (∼20 nm).17 Although this method offered a route for preparing sandwichnanoparticles (≥10 nm) between graphene layers, the size controlof nanoparticles was highly dependent on the optimization of reactionparameters, such as precursor concentration and reducing agent. Asacrificed template-based technique was also developed to sandwichlarge cobalt (Co) nanoparticles and their oxides between carbon nanosheets via high-temperature calcination of glucose-based polymer/(NH4)2Co8(CO3)(OH)6:4H2O under an inert atmosphere.18 Although ultrasmall nanoparticles (USNPs), with sizes of 1 to 3nm, are known to have unique properties highly distinct from largercounterparts,19 the precision-tuning ofNPs’ size between layered nanomaterials has been studied less.For example, the US–AuNP (2–4 nm)-decoratedgraphenenanocomposites, prepared by laser irradiation, were reported as promisingmaterials for photothermal therapy and the efficient conversion ofsolar energy into useable heat for a variety of thermal, thermochemical,and thermomechanical applications.Herein, we report for the first time the synthesis of ultrasmallgold nanoparticles (mean particle size of 1.94 ± 0.20 nm) (US–AuNPs)sandwiched between graphene layers with a preadjusted finely tunedlayer–layer distance of ∼2.04 nm.20 Natural graphite flakes  were used for bulk-functionalization experiments. N,N-Dimethylformamide (DMF) , 1,4-diaminobenzene , pyromellitic dianhydride (PMDA) , and sodiumnitrite (NaNO2)  were purchasedfrom Sigma-Aldrich and used as received. High-purity distilled waterwas obtained from an Elga PURELAB Prima deionized water machine (15Ω).Graphite was provided by GraphexelLimited (graphite grade 2369) and used as received to prepare highlyexfoliated graphene flakes following the literature.21 In a typical procedure, a cooled solution (0 °C) ofNaNO2 was added dropwise to 1,4-diaminobenzene . The stoichiometric ratio wasadjusted to 1 equiv/1 equiv to ensure that only one amine group is diazotized.22 The resulting yellow solution was stirred for 30 min andmaintained at 0 °C. A cooled solution (0 °C) of highly exfoliatedgraphene flakes in DMF20  was then added dropwise. The reaction mixture was heldat 0 °C, stirred for 4 h, and then mixed for 15 h at room temperature.The surface-modified graphene flakes were filtered through a PTFEmembrane (0.5 μm), redispersed in 2 M HCl (20 mL), and filteredand washed with a copious amount of water until pH was neutral. Theresultant solid material was dispersed in 2 M sodium hydroxide (NaOH)(10 mL) and stirred overnight to convert the produced aniline hydrochloridesalt to aniline groups. The aniline-modified graphene flakes werethen isolated by filtration through a PTFE membrane (0.5 μm),washed with water until pH neutral, redispersed, and filtered usingTHF (2 × 10 mL), acetone (2 × 10 mL), and ethanol (2 ×10 mL), respectively, and dried at 80 °C in an oven. A bifunctionalmolecule, PMDA, was used to covalently interconnect aniline-modifiedgraphene flakes. A two-step reaction was followed to ensure the formationof a molecular bridge between the aniline-modified graphene flakes.Initially, the aniline-modified graphene flakes (10 mL) were reactedwith excess PMDA in dry DMF at 60 °C to enable only the one-sideaddition of PMDA to the aniline-modified graphene flakes. UnreactedPMDA was removed by membrane filtration, and the wet cake was washedwith DMF (100 mL) and THF (100 mL). The aniline-PMDA-modified grapheneflakes were redispersed and filtered using dry DMF (4 × 10 mL)and dry THF (4 × 10 mL), respectively, and then dried under N2 flow. The resultant aniline-PMDA-modified graphene flakeswere redispersed in dry DMF to prepare a highly stable dispersion.Then, 10 mL of well-dispersed aniline-modified graphene flakes indry DMF was added to the aniline-PMDA-modified graphene flakes’solution. The mixture was heated overnight to 60 °C under nitrogen.The above-mentioned purification steps  were repeated to isolate theprepared covalently interconnected graphene nanoscaffolds. For bulkfunctionalization, a graphite dispersion (instead of a stable graphenedispersion) in DMF  wasused to prepare aniline-functionalized graphene nanosheets followingthe modified literature procedure described above,21 and the resulting aniline-functionalized graphene was usedfor nanoscaffold preparation. AuNPs were prepared in the presenceof the covalently linked graphene nanoscaffolds following the Turkevichmethod.24 In addition,two control experiments were performed with and without aniline-modifiedgraphene. In a typical procedure, ∼10 μg/mL of covalentlylinked graphene nanoscaffolds was prepared in high-purity water (20mL) using bath sonication (30 min). Then, 2 mL of freshly preparedaqueous HAuCl4 (0.005 M) was added to the covalently linkedgraphene nanoscaffold solution. This mixture was initially stirredat room temperature for 30 min, and the solution temperature was raisedto 90 °C. After that, 1.2 mL of hot (90 °C) Na3Cit solution (0.05 M), required to prepare the citrate-to-gold molarratio of 6-to-1, was added to the HAuCl4/covalently linkedgraphene nanoscaffold solution at 90 °C. The mixture was stirredfor an additional 2 h at the same temperature. After cooling to roomtemperature, it was filtered through a PTFE membrane (0.5 μm)and washed with copious amounts of water to remove the unreacted goldsalt precursor. The resultant material was dried at 80 °C ina vacuum oven to give a gold nanoparticle/covalently linked graphenenanoscaffold hybrid structure.A modified procedure was usedto functionalize the surface of the graphene flakes.–1. Purchased graphite flakes and the covalently linked graphene nanoplateletswere tested in powder form. A stable DMF dispersion of the covalentlylinked graphene nanoplatelets was deposited on the substrate surfaceand tested in powder form. Raman analysis was performed on a cleanSi-wafer substrate to prevent the substrate’s interferencewith the spectra and to observe any peak shift relative to the Sireference material at 520 cm–1. Ultraviolet visible(UV/vis) spectra were obtained from stable dispersions of the producedgraphene in DMF using a Shimadzu UV-2550 UV/vis spectrophotometer.High-resolution X-ray photoelectron spectroscopy (XPS) was performedby a Thermo Scientific K-alpha photoelectron spectrometer with monochromaticAl Kα radiation; peak positions were referenced to the C 1sline at 284.5 eV, and the CasaXPS software with Shirley backgroundwas used while deconvoluting N 1s spectra. Transmission electron microscopy(TEM) and high-resolution transmission electron microscopy (HRTEM)were performed using Jeol JEM-1010 and JEOL-2010F coupled with EDSdetector (Oxford Instruments) instruments, respectively. All TEM sampleswere prepared in DMF and dropped on carbon-coated copper grids, whichwere dried in air before imaging. The particle size distribution ofAuNPs was estimated from TEM data by using ImageJ software. X-raydiffraction (XRD) analysis was performed using a Bruker D8 ADVANCE5–90°, 0.02 step size, Cu Kα (1.5406 A) at 40 kV10 mA, and carbon nanomaterials dispersed in ethanol were used toprepare XRD samples. All graphs, statistical analyses, and Raman spectralfittings were performed using OriginLab software (Origin 9.1).Raman spectra were obtainedfrom a Renishaw InVia Raman Microscope using a 514.5 nm excitationlaser and wavenumbers ranging between 100 and 3000 cmvia chemical reaction-mediated self-assemblyof functionalizedgraphene nanoplatelets. Stable graphene dispersion (∼15 μg/mL)in DMF  was usedfor functionalization experiments, and it was prepared following aliterature method,20 which produces defect-freesingle-layer graphene via microwave solid exfoliation.In a typical CIG nanoscaffold preparation, exfoliated graphene nanosheetswere first functionalized with aniline groups following a modifieddiazonium salt addition procedure (21 The stoichiometricratio between sodium nitrite (NaNO2) and 1,4-diaminobenzenewas 1 equiv to ensure that only a single amine was diazotized.22 The aniline-functionalized graphene nanoplateletswere covalently interconnected using a bifunctional linker [pyromelliticdianhydride (PMDA)] to prepare CIG nanoscaffolds.The molecularly designed CIG nanoscaffoldswere prepared rocedure 1.21 The 27 The D-band observedat ca. 1350 cm–1 is widely regardedas a reliable measure of surface defects and chemical functionalization.Therefore, the area ratio of the D-band to the G-band (AD/AG) is used to determinethe degree of surface defects on a graphitic material.25 Raman spectra of pristine graphite and CIG nanoscaffoldsare shown in AD/AG ratio of 0.36, compared to0.07 and 0.11 for graphite and exfoliated graphene, respectively,indicating the successful covalent addition of aniline groups to thegraphene surface  compared to the pristine graphite with an I2D/IG ratio of ∼0.14at ca. 2728 cm–1  suggests the formation of rotationally reordered single-layergraphenes.29 On the other hand, the 2D-bandof the CIG nanoscaffolds displays an intense peak at ca. 2723 cm–1 (I2D/IG at 1580 cm–1 = 0.41)  1a, and a = 0.41) 1d, chara30 In such reactions, primary aminegroups convert into amide linkages; thus, one can expect a chemicalenvironmental change in the nitrogen atom of aniline. Furthermore,adding a bulk group such as the oxygen-rich PMDA (C10H2O6) results in a considerable difference in theelemental composition of the functionalized graphene compared withthat of the pristine one.Thecovalent molecular interconnection between the graphene layerswas further characterized by X-ray photoelectron spectroscopy (XPS).The aniline-functionalized graphene flakes shown in Supporting Information Figure S2 for high-resolution XPS spectraof C 1s and O 1s). The elemental composition of nitrogen (1.48 at%) corresponds to approximately 1 aniline group per 55 graphitic carbonatoms. In addition, the XPS elemental analysis of the CIG nanoscaffoldsshows 81.96, 17.32, and 0.72 atom % C (284.68 eV), O (532.28 eV),and N (400.38 eV), respectively . There was a significant decrease in the elemental nitrogen(0.72 at %) and carbon (81.96 at %) compositions in the CIG nanoscaffolds,while there was an apparent increase in the oxygen content (17.32at %) compared to that in the aniline-functionalized graphene. Theobserved changes were attributed to the covalent addition of oxygen-richPMDA (C10H2O6) groups per approximately55 graphitic carbon atoms. The N 1s spectra of both aniline-functionalizedgraphene and CIG nanoscaffolds were deconvoluted to give an idea aboutthe covalent interconnection between graphene layers  and 399.20 (cont. 19.14%) eV,attributable to O=C–N (amide)32 and unreacted NH2 groups,31 respectively , O(532.78 eV), and N (399.78 eV), respectively  precursors inan aqueous solution following the Turkevich method24 TEM is about 7–8 nm . However,a few large aggregates (≥200 nm) are also present on the graphenesurface (0 (111).19 The experimentallyderived mean particle size of US–AuNPs (1.99 ± 0.20) isin very close agreement with the estimated layer–layer distanceof the CIG nanoscaffolds (∼2.02 nm). In addition, there areAuNPs (∼10–25 nm) and some large aggregates (≥200nm) on the CIG surface, which are comparable to the results obtainedfor the aniline-functionalized graphene. However, no US–AuNPis observed in HRTEM images of the aniline-functionalized graphene,suggesting that they can be produced only when the layer–layerdistance is finely tuned. Overall, HRTEM results underscore that thefine-tuning of AuNPs between graphene nanoplatelets can be achieved via precise covalent chemistry.Analysis of the graphene dispersion by transmission electron microscopy(TEM) shows randomly stacked few-layer graphene nanoplatelets 3a, whichngements 3b, attringements 3c,g.On ngements 3d,h,whi39 In addition, the spacing between graphene layers can be altered via intercalation,20 or covalentcross-linking chemistries,41 and the covalentlyinterconnected graphene architectures can be characterized by HRTEM42 using lateral distances.Further evidence of the finely tuned graphene layer–layerdistance and the accompanying US–AuNP formation comes fromthe lattice spacing distance of the CIG nanoscaffolds analyzed byHRTEM. Theoretically, the distance between two graphene layers is∼0.335 nm, and the thickness of a single layer of graphenemeasured by atomic force microscopy (AFM) varies from 0.4 to 1.0 nm.42 Assuming that three-layergraphene has a thickness of ∼1.0 nm (by AFM),38 the measured distance of ∼4.8 nm well agrees withthree-layer graphene, which is covalently interconnected by two PMDAbridges. Another complementary HRTEM image of the CIG nanoscaffolddisplays US–AuNP-decorated surfaces, and the graphene layersare visibly separated  and (004) reflections was observed after exfoliationand aniline functionalization of graphite flakes; however, the intensityof the (002) peak significantly decreased, attributable to less-layerednanosheets, compared to that of the as-received graphite flakes. ThePMDA-bridged CIG nanoscaffold displayed no (002) and (004) reflections;in contrast, two new peaks appeared at ca. 24.8 and12.5°, attributed to covalently bridged graphene layers withPMDA spacers, which correlates well with an increase in the layer–layerdistance between graphene nanosheets. Such a low-angle shifting phenomenoncompared to the (002) reflection at ∼26.5° was also observedin previous studies, which used rigid linkers  to preparecovalently connected graphenes.41 AfterAuNP incorporation into the CIG nanoscaffold, there was no changein the peaks at ca. 24.8° and 12.5°; however,AuNP-related five peaks at ca. 38.2, 44.4, 64.6,77.6, and 81.7° were attributed to standard Bragg reflectionsof (111), (200), (220), (311), and (222) for the face-centered cubiccrystal structure, respectively.44Complementary X-ray diffraction data of natural graphite,exfoliatedgraphite, aniline-functionalized graphene, CIG nanoscaffold, and AuNP-CIGnanoscaffold are presented in 17 and sacrificedtemplate18 techniques reported in the literature.The linker molecule, PMDA, principally predetermines the layer–layerdistance of graphene nanoplatelets and the size of the sandwichednanoparticles. When ease of access to commercially available dianhydrides andother bifunctional cross-linking agents was combined with well-practicedcarbon nanomaterials covalent surface modification approaches,46 an acceptable size control on sandwiched nanoparticles in betweengraphene nanosheets can be realized for several applications fromenergy storage materials49 to electromagnetic shielding50 and membranes.51 Inaddition to 2D graphene nanoplatelets, given the available covalentsurface modification techniques, a library of 2D materials such asMoS2,53 h-BN,54 TiS2,55 MoSe2,55 and Bi2Te355 can be processed similarly to prepare hierarchicalnanoscaffolds of other 2D-materials. This elegant approach may provideprecise size control while producing sandwiched metal/metal oxidenanoparticles between 2D-layered nanostructures, whose surfaces areprone to covalent chemistries.In summary, a hybrid US–AuNP (mean particle size of 1.94± 0.20 nm)/graphene nanoplatelet sandwich was fabricated usingthe CIG nanoscaffolds with a pretuned graphitic layer–layerdistance of ∼2.04 nm. The precision covalent chemistry strategyto prepare sandwich structures significantly differs from the sequentiallayer-by-layer stacking


We investigate the dynamics of high energy dual regime unidirectional Erbium-doped fiber laser in ring cavity, which is passively Q-switched and mode-locked through the use of an environmentally friendly graphene filament–chitin film-based saturable absorber. The graphene–chitin passive saturable absorber allows the option for different operating regimes of the laser by simple adjustment of the input pump power, yielding, simultaneously, highly stable and high energy Q-switched pulses at 82.08 nJ and 1.08 ps mode-locked pulses. The finding can have applications in a multitude of fields due to its versatility and the regime of operation that is on demand. The 1.5 µm band is best known as the standard telecommunication band, of which the losses in the silica fiber would be minimal compared to shorter or longer wavelength bands. Mutually, mode-locking operations in this region play an irreplaceable role in commercial and non-commercial fields, including in metrology and holography ,3. The aTwo-dimensional (2D) layered materials are among the most well exploited SAs, owing to their high nonlinearity, high saturable absorption, and high damage threshold, with graphene and carbon nanotubes (CNTs) still spearheading the generation of ultrashort pulses due to their performance reproducibility and their facile fabrication. Their sub-picosecond relaxation time is beneficial because it makes them a reliable alternative to the conventional use of a semiconductor saturable absorber mirror (SESAM), which requires a complicated post-fabrication process. Compared to CNT, graphene works as a fast SA owing to its relaxation time of approximately 100–200 fs, and, thus, is more capable of having high a mode-locking pump power threshold. Additionally, there is a limitation on the working wavelength of the diameter-dependent CNTs. Having an independent wavelength absorption also means that graphene resonantly absorbs light [Although graphene has been extensively exploited in terms of its performance parameters in the years that follow its first demonstration as SA in 2009 , such asWe have reported the successful implementation of a biodegradable and biocompatible chitin in a graphene-based SA device in the eye-safe region , and thiPleurotus ostreatus) were extracted to produce the chitin and 42 g of nanofibers were successfully extracted from 3 kg of whole mushrooms. Initially, the mushrooms were blended for 5 min using a regular domestic blender before undergoing hot water extraction to remove any water-soluble components. Following that, the Pleurotus ostreatus tissues underwent alkaline deproteinization treatment (1M NaOH) at 65 °C for 3 h. This is effective at removing proteins, lipids, and alkali-soluble glucan. Water was then added to the slurry in order to increase its volume to 1.5 l. The suspension was stirred for 30 min at 85 °C. Through 15 min of centrifugation , at 7000 rpm, the soluble components and excess water were removed to produce precipitate (cake), which was then soaked in alkaline solution. The suspension was heated to 65 °C for 3 h while stirring continuously before it was neutralized by re-centrifugation in excess water. The neutralized cake was then re-suspended in water (1:40 w/v ratio) and dispersed by final blending for another 1 min. The suspension was stored at 4 °C until further use. The produced graphene-THF suspension and chitin was mixed together with a one-to-one (1:1) ratio, totalling up to 5 mL. A 1-h ultrasonication process was then conducted to break down any agglomeration and to stack graphene , producing a well-dispersed graphene in chitin. A thin yet sturdy film was obtained after a 36-h ambient temperature drying in a petri dish, revealing an SA with 50 µm thickness, as measured using a 3D laser microscope. Sturdy film was obtained after a 36 h ambient temperature drying process in a petri dish.The graphene–chitin film used in this study was fabricated following the same procedure as reported in ,23. The The graphene–chitin film was first characterized using field emission scanning electron microscope (FESEM) and Raman spectroscopy for their physical morphologies and signature peak profiles, respectively, as shown in 2 with a saturable loss of around 85%.A twin-balanced detector, as illustrated in The transmission and absorption of the graphene–chitin film was recorded at 39% and 42%, respectively, in the 1500 nm region, as shown in Additionally, the thickness of the graphene–chitin film was investigated and found to be approximately 50 μm, as shown in The experimental setup of the Erbium-doped fiber laser (EDFL) is shown in A wavelength shift of around 7.05 nm and a widening of the spectral band of approximately 1.03 nm was observed upon the insertion of the graphene-chitin SA, as shown in The input pump power was then increased steadily, recording a Q-switched operation as the pump power reached 80.63 mW, and it remained steady up until 163.16 mW, with no damage inflicted on the film, indicating that the film was able to withstand high input power and can self-start a Q-switched lasing at low power.Typical Q-switched laser output train and its single envelope under the maximum input pump power of 163.16 mW is shown in The pulse width obtained was lower than many of the previously reported single-regime Q-switched operation using graphene-PVA based Sas, and it was comparable to the one employing a graphene-silica hybrid waveguide and optically deposited graphene oxide ,33,34. IAs the pump power increased further than 163.16 mW, the pulse train observed in the oscilloscope began to become unstable, and by the point that the pump power reached 167.75 mW, a mode-locking operation can be seen to start taking over, though with a very unstable operation. At a high pumping level, the thermal accumulation and supersaturation of the SA produced a sharp decrease in the transmission of the laser pulse passing through the SA, leading to the generation of short pulses ,40. EvenThe central wavelength slightly shifted from the continuous wave (CW) of the laser cavity which was initially at 1564.48 nm, mainly due to the loss introduced by the insertion of the SA. Several pairs of Kelly sidebands can be observed from the spectrum, indicating a clear characteristic of a traditional negative dispersion soliton wherein the group velocity of the signal in the fiber is higher at a shorter wavelength. This resulted in a stable pulse that maintained its shape and its amplitude as it propagated through the fiber due to the balance between non-linear effects and dispersion. With high peak power as one of its characteristics, this explains why the peak power of several magnitudes is higher than that of Q-switching operation ,45,46, a2 fitting, the pulse duration at its FWHM was estimated to be about 0.70 ps, resulting in a pulse duration of 1.08 ps (τFWHM/1.54). The autocorrelation trace revealed that the result of the experiment follows the sech2 fitting closely. The calculated output power and pulse energy of the mode-locked EDFL was 39.75 W and 0.043 nJ, respectively.The mode-locked pulse was observed to be stable from the threshold pump power of 172.33 mW to the maximum pump power of 181.50 mW. The usual limitation of film-based SA in withstanding high input power ,48 was nThe RF spectrum of the generated mode-locked pulse was recorded with a span of 500 MHz and 50 MHz, respectively, as shown in Graphene–chitin film-based SA was used to generate simultaneous Q-switching and mode-locking operations in the telecommunication region. The lasing produced a Q-switched 1.54 μs pulse at a high input pump power of 163.16 mW. This operation was able to produce a high peak power and a pulse energy of 53.3 mW and 82.08 nJ, respectively. Beyond the input pump power of 163.16 mW, the pulse started to become unstable, and upon reaching 172.33 mW, the mode-locked pulse can be observed. The 26.11 MHz pulse correlated to a 1.08 ps pulse width with a peak power of 39.75 W. Although progressive works are still needed to improve the performance of the Q-switched and the mode-locked EDFL––such as by optimizing the cavity configuration through output coupling efficiency, by gaining medium doping concentration, and by improving the SA quality––this finding will be of interest due to their application in material processing, optical spectroscopy, and telecommunication.


UCL Open: Environment is committed to sharing peer-reviewed knowledge about environmental issues, including climate change. In this special series we are encouraging contributions from authors engaged in producing knowledge about climate change in and with local communities. The published articles and case studies will meet our usual standards of quality and originality, whilst fulfilling the need for wider recognition of transdisciplinary, community-based knowledge.This special series will include research articles, case studies and commentaries about community-level responses to climate change. We hope that all submissions will be based on work done in collaboration with or led by non-traditional research communities. The special series will provide a platform to record and disseminate the insights from many community-scale and community-led researchClimate change is arguably the best-known environmental and societal challenge of our time and while calls to respond to the ‘climate emergency’Local knowledge of climate change impacts, adaptation and mitigation are often overlooked by large research and policy institutions, despite vast potential to contribute to sustainable responses . Where lAt the same time as awareness of the issues linked to working with communities on environmental topics has grown, scientific knowledge about future climate impacts has also become progressively granular, with more information now available and accessible that is meaningful for local communities . The chaCommunity-scale and community-based responses to climate change have burgeoned in recent years. In some cases, regions, cities and communities have acted ahead of and in the absence of national and international leadership on key issues . CommuniGlobal governance mechanisms, such as the Paris Agreement , provideThe need to respond to the immediate and future impacts of climate change is now widely accepted, and is becoming more urgent. The world is currently about 1°C above the pre-industrial average temperature . This avThanks to a combination of observations and modelling at local scales  we know that some communities are facing much higher temperatures than they are accustomed to: the UK is subject to a growing chance of experiencing daily maximum temperatures of over 40°C. . The impFor communities to respond they will probably have to move through several stages of development if they are to help mitigate climate change and also adapt to it. One way that communities may become better able to adapt, and support other communities in the process, is by curating and sharing knowledge and experiences. Little of that has been done so far and doing so might make it easier to:overcome barriers to progress that arise from, say, issues linked to laws, governance finance and the psychological frames that people see climate change through and, then, to develop the necessary engineering and behavioural responses, and;assist and enable communities to be resilient and thrive, particularly perhaps, those communities facing compound risks (such as those facing flooding from both sea and rivers or extreme heat and drought).We suspect that some of the most grounded, creative, innovative and transformative work on preparing for and responding to climate change impacts is and will be coming from communities around the world who are themselves foreseeing or currently grappling with the impacts of climate change. Communities of all kinds hold and constantly evolve important knowledge, skills and insights highly relevant to addressing climate change. This special series aims to share knowledge about if and how these are being brought to bear in climate responses in a range of international settings, to understand how communities and researchers might better respond to these coming challenges.We welcome and look forward to submissions on any aspect of how communities might be addressing climate change as it is a core issue for the world and hope to attract the attention of many actors outside the academic research community as we run up to the COP26 meeting in November 2021.


PurposeThe purpose of this study was to evaluate the changes in capabilities between the Generative Pre-trained Transformer (GPT)-3.5 and GPT-4 versions of the large-scale language model ChatGPT within a Japanese medical context.MethodsThe study involved ChatGPT versions 3.5 and 4 responding to questions from the 112th Japanese National Nursing Examination (JNNE). The study comprised three analyses: correct answer rate and score rate calculations, comparisons between GPT-3.5 and GPT-4, and comparisons of correct answer rates for conversation questions.ResultsChatGPT versions 3.5 and 4 responded to 237 out of 238 Japanese questions from the 112th JNNE. While GPT-3.5 achieved an overall accuracy rate of 59.9%, failing to meet the passing standards in compulsory and general/scenario-based questions, scoring 58.0% and 58.3%, respectively, GPT-4 had an accuracy rate of 79.7%, satisfying the passing standards by scoring 90.0% and 77.7%, respectively. For each problem type, GPT-4 showed a higher accuracy rate than GPT-3.5. Specifically, the accuracy rates for compulsory questions improved from 58.0% with GPT-3.5 to 90.0% with GPT-4. For general questions, the rates went from 64.6% with GPT-3.5 to 75.6% with GPT-4. In scenario-based questions, the accuracy rates improved substantially from 51.7% with GPT-3.5 to 80.0% with GPT-4. For conversation questions, GPT-3.5 had an accuracy rate of 73.3% and GPT-4 had an accuracy rate of 93.3%.ConclusionsThe GPT-4 version of ChatGPT displayed performance sufficient to pass the JNNE, significantly improving from GPT-3.5. This suggests specialized medical training could make such models beneficial in Japanese clinical settings, aiding decision-making. However, user awareness and training are crucial, given potential inaccuracies in ChatGPT's responses. Hence, responsible usage with an understanding of its capabilities and limitations is vital to best support healthcare professionals and patients. Artificial intelligence (AI) represents rapid growth, transforming many aspects of our lives, including the medical field. Utilizing advanced algorithms and machine learning, AI potentially aids physicians in making more accurate diagnoses, identifying potential health risks, and providing patients with personalized treatment plans -5. NotabMoreover, OpenAI released GPT-4, the latest version of the large-scale language model that powers ChatGPT, on March 14, 2023. Internal evaluations reported that GPT-4 saw an 82% decrease in the likelihood of responding to unauthorized content requests and a 40% increase in the probability of producing fact-based answers compared to GPT-3.5 . One sigIn order to examine the changes in capabilities between GPT-3.5 and GPT-4 in the context of a Japanese-speaking medical setting, we used the 112th Japanese National Nursing Examination (JNNE) . AccordiChatGPTChatGPT  is an artificial intelligence language model that became widely available in November 2022. It is an AI that generates instant, natural conversation-style responses to queries . The numJapanese National Nursing Examination (JNNE)The JNNE has a total examination time of five hours and 20 minutes, divided into morning and afternoon sessions with 120 questions each, totaling 240. Eleven subjects are covered in the examination, including the structure and function of the human body, understanding disease and promoting recovery, health support and social security systems, basic nursing, adult nursing, geriatric nursing, pediatric nursing, maternal nursing, psychiatric nursing, home care nursing theory, and integrated and practical nursing.The examination comprises 50 compulsory questions that probe fundamental medical knowledge, 130 general questions in a question-and-answer format covering all 11 subjects established in the test criteria, and 60 scenario-based questions. The scenario-based questions set up scenarios that one could face in a nursing setting for seven subjects  and probe the examinee's understanding and judgment in response to these situations.The basic format of JNNE is multiple-choice with four options, but a few questions with five options (select one or select two) are also presented. A few calculation problems are included, and some conversation questions, which simulate dialogues with patients in the actual clinical setting, are also part of the exam. Questions are deemed 'inappropriate' if they have multiple correct answers, are excessively difficult, or the situation is not adequately set up to obtain a correct answer despite being a multiple-choice question. Depending on the reason for being labeled as inappropriate, measures include excluding all examinees from scoring, only including correct responses for scoring (incorrect responses are excluded), or treating multiple options as the correct answer. According to the official announcement, one question was excluded from scoring in the 112th JNNE, and another question was considered to have multiple answers . TherefoCompulsory and general questions are scored one point per question, while scenario-based questions are scored two points. The passing criteria are set as answering correctly to over 80% of compulsory questions and meeting the annual standard for the total score of general or scenario-based questions. Specifically, about 65% of the points are required for the latter. In the 112th National Nursing Examination, which we used for our analysis and was conducted in February 2023, the passing criteria were 40 points or more for compulsory questions and a total score of 152 points or more for general and scenario-based questions. The exam had 64,051 candidates, 58,152 of whom passed, making the pass rate 90.8% .AnalysisThe test questions from the 112th JNNE . All tesEthical approvalThis study solely utilized data previously published online and did not involve any human subjects. Instead, an analysis of the JNNE was conducted. Therefore, ethical considerations were not applied to this study.Out of 238 questions posed in Japanese, both versions of ChatGPT, GPT-3.5, and GPT-4 were able to generate some form of answers for the same 237 questions. The question for which an answer could not be generated came from a general question that required image information to be answered and was excluded from the analysis. Thus, the assessment was carried out with a total of 237 questions. Among them, one question was a mathematical problem, and 15 were conversation questions.The overall accuracy rate was 59.9% (142/237) for GPT-3.5 and 79.7% (189/237) for GPT-4, showing a statistically significant difference (p<0.01). In terms of scoring, for both compulsory and general/scenario-based questions, GPT-3.5 achieved 58.0% (29/50) and 58.3% (144/247), respectively, failing to meet the passing standard in both categories. In contrast, GPT-4 scored 90.0% (45/50) and 77.7% (192/247), respectively, satisfying the passing standard in both categories.Table For compulsory questions, the accuracy rates were 58.0% (29/50) for GPT-3.5 and 90.0% (45/50) for GPT-4 (p<0.01).For general questions, they were 64.6% (82/127) for GPT-3.5 and 75.6% (96/127) for GPT-4 (p=0.014). For scenario-based questions, they were 51.7% (31/60) for GPT-3.5 and 80.0% (48/60) for GPT-4 (p<0.01). In all categories, GPT-4 demonstrated a higher accuracy rate than GPT-3.5.Table Both GPT-3.5 and GPT-4 successfully generated correct answers for a total of 131 questions (46.4%). Additionally, there were 58 questions (24.5%) where GPT-3.5 gave incorrect answers that GPT-4 provided correct answers for, and there were 37 questions (15.6%) where both versions provided incorrect answers. On the other hand, there were 11 (4.6%) questions where GPT-3.5 provided correct answers while GPT-4 returned incorrect ones. In the category of compulsory questions, there were 17 instances (34.0%); in general questions, 21 instances (16.5%); and in scenario-based questions, 20 instances (33.3%), where questions that were incorrectly answered by GPT-3.5 were correctly answered by GPT-4.In terms of conversation questions, the accuracy rate was 73.3% (11/15) for GPT-3.5 and 93.3% (14/15) for GPT-4 (p=0.248). Neither achieved a correct answer to the mathematical problem. The mathematical problem was not exported with the correct answer in both versions.In this study, we assessed the accuracy of ChatGPT versions GPT-3.5 and GPT-4 using the multiple-choice format of the JNNE administered in February 2023. Although the accuracy of randomly chosen answers for this exam stands at approximately 20%-25%, the overall accuracies for GPT-3.5 and GPT-4 were recorded at 59.9% (142/237) and 80.2% (190/237), respectively, both significantly exceeding the likelihood of random selection. While GPT-3.5 did not meet the passing requirements of the JNNE, GPT-4 exceeded the passing criteria. This suggests the potential utility of the latter in assisting with clinical diagnoses and treatment decisions in real-world Japanese medical settings, provided its use is approached with caution and an understanding of its inherent characteristics.The documented 80% accuracy rate in scenario-based questions, in conjunction with a 93.3% accuracy rate in conversation questions, bolsters the proposition that ChatGPT could potentially augment medical professionals in delivering personalized treatment plans and facilitating remote patient care , 16. MorThe accuracy of ChatGPT has been reported to improve with a certain degree of training . Indeed,Additionally, while direct comparisons are challenging, it is worth noting that GPT-4 recorded an 80.2% accuracy rate on the JNNE in this study, while its accuracy rate on the United States Medical Licensing Examination Educational (USMLE) entrance examination was reported to exceed 90% . This imFurther, the discrepancy in accuracy rates might be attributable to cultural nuances inherent in the Japanese examination system. The Japanese medical examination may require significant degrees of implicit understanding from the examiners to attain high scores, given that the exam may not always explicitly indicate correct or incorrect responses. Japan, recognized as a high-context communication society, often involves interpreting the atmosphere and anticipating unexpressed intentions due to shared cultural norms . This cuThis study has several limitations. First, we did not scrutinize the basis of the answers in this analysis. Since most of the exam questions are multiple-choice, there is a possibility that the correct answer could be reached by chance. In the future, it will be important to verify the validity of the basis of ChatGPT's correct answers and reveal its performance in more detail. Second, in this study, the trial was conducted only once, based on other studies conducted in a similar manner, and the results were analyzed. However, ChatGPT may output different answers each time a question is asked due to the language model behind the main architecture. Therefore, in order to reduce errors caused by this, it may be possible to measure ChatGPT performance more rigorously by attempting the JNNE several times and comparing the percentage of correct answer rates in each of these attempts. Third, a detailed evaluation of questions based on correct or incorrect responses, or a detailed analysis by field, was not performed. Investigating the characteristics of problems where GPT-3.5 was incorrect and GPT-4 was correct, as well as differences in accuracy rates by domain, through trials based on larger datasets, could potentially reveal areas in need of further enhancement. Therefore, it is important to focus on these aspects as areas of consideration for future studies. Fourth, it is important to acknowledge that ChatGPT versions are constantly evolving. With subsequent versions beyond GPT-3.5 and GPT-4, modifications to the LLMs could potentially lead to significant changes in the time required for generating responses and their quality . DespiteOur study demonstrated that while the GPT-3.5 version of ChatGPT did not meet the criteria, the GPT-4 version already exhibits the performance required to pass the JNNE. The fact that the accuracy rate dramatically improved in less than half a year suggests that by executing training specialized in the medical field in Japanese, such large language models could potentially become beneficial sources of information in clinical decision-making in Japanese clinical settings, assisting medical professionals in diagnosing and determining treatment plans, and helping patients decide whether to visit a hospital or not. However, given that accuracy is not assured and the hallucinations generated by ChatGPT can be convincingly naturalistic, there is a potential for erroneous judgments to be made unless the messages are meticulously examined by an experienced healthcare professional. Therefore, when implementing in a clinical environment, not only the training of the language model but also the training and awareness-raising of the users will be important. It is crucial to ensure that these tools are used responsibly and with a clear understanding of their capabilities and limitations to provide the best support to healthcare professionals and patients alike.


Toxocariasis has been listed among the most neglected parasitic diseases worldwide, with approximately one fifth of the global population exposed, particularly those living under poverty. In Brazil, communities of descendants of enslaved blacks (quilombola) have historically had some of the highest rates of vulnerability and poverty, characterized by lack of health assistance, poor quality of life, and nutritional insecurity.Toxocara spp. IgG antibody detection was carried out on dog feces and hair, and soil samples were surveyed for presence of Toxocara spp. eggs.A cross-sectional sampling of quilombola individuals living in four communities of southern Brazil, as well as their dogs and the soil, was carried out from December 2021 to March 2022. Sociodemographic and other information such as water source, alimentary habits, and dog and cat ownership were gathered using a semi-structured questionnaire for assessing toxocariasis risk factors. Human serum samples were tested by ELISA for anti-(P = 0.029), educational level (P = 0.026), and drinking water source (P = 0.043) were associated with seropositivity by univariate analysis. Final logistic regression revealed increased odds  to have seropositivity in individuals > 50 years old (< 10 years old). As expected, individuals with soil contact were more likely seropositive . Although retrieved in only 5/96 (5.2%) dog feces, Toxocara spp. eggs were found in 18/60 (30.0%) soil samples.Overall, 172/208 individuals  were seropositive, the highest seroprevalence rate to date in Brazil. Male gender Toxocara spp. in both human and dog populations.The high vulnerability and seroprevalence observed in quilombola communities clearly demand a One Health approach for detection, monitoring, and prevention of infection by The online version contains supplementary material available at 10.1186/s13071-023-06010-w. Toxocariasis has been a prevalent zoonosis with a significant socioeconomic impact, particularly on impoverished communities worldwide , and hasToxocara canis or Toxocara cati infective eggs has been considered the primary transmission route of human toxocariasis  to predict seropositivity for anti-Toxocara spp. antibodies in individuals of quilombola communities of Paraná State, southern BrazilAdditional file 2:  Raw data of associated risk factors of toxocariasis in quilombola communities of southern Brazil..