Organophosphate insecticides with broad spectrum and high efficiency make a great difference to agricultural production. The correct utilization and residue of pesticides have always been important issues of concern, and residual pesticides can accumulate and pass through the environment and food cycle, resulting in safety and health hazards to humans and animals. In particular, current detection methods are often characterized by complex operations or low sensitivity. Fortunately, using monolayer graphene as the sensing interface, the designed graphene-based metamaterial biosensor working in the 0–1 THz frequency range can achieve highly sensitive detection characterized by spectral amplitude changes. Meanwhile, the proposed biosensor has the advantages of easy operation, low cost, and quick detection. Taking phosalone as an example, its molecules can move the Fermi level of graphene with π–π stacking, and the lowest concentration of detection in this experiment is 0.01 μg/mL. This metamaterial biosensor has great potential in detecting trace pesticides, and its application in food hygiene and medicine can provide better detection services. Currently, there are many types of pesticides in the world, and the application of pesticides has brought considerable economic benefits to agriculture. However, the excessive utilization of pesticides has produced a serious influence on human health and the ecological environment. Taking organophosphate pesticides as an example, they are toxic to insects and mammals, and can easily damage their nervous systems . In addiTraditionally, detection methods for pesticide residues include high-performance liquid chromatography, gas chromatography, and liquid chromatography–tandem mass spectrometry. Although these methods have high sensitivity, their expensive equipment, stringent experimental conditions, and long experimental time limit their applications ,6,7,8,9.Metamaterials, composed of sub-wavelength periodic unit cells, can produce abundant electromagnetic responses in the visible, terahertz, and infrared regions ,11,12. BTraditionally, metamaterial sensors consist of a dielectric layer and a metal layer, and they are sensitive to the refractive index of the top layer of the analyte. For example, a metamaterial sensor can measure 4-methylimidazole at concentrations from 0 to 20 mg/L, and the excited local electric fields effectively enhance the interaction between the incident terahertz light and the matter under testing . By usinIn this work, a graphene-based metamaterial biosensor working in the terahertz band is proposed. The sensor is composed of a polyimide (PI) film as the dielectric layer, an aluminum structure array as the metal layer, and monolayer graphene as the sensing interface. Simulation results indicate that the monolayer graphene, with varying Fermi levels, can modulate the entire electric field and produce the phenomenon of amplitude change. In addition, the adjustable amplitude range of the graphene-based metamaterial biosensor is almost independent of the structural parameters. Using phosalone as an example, the increasing concentration of the analyte results in a rise of the Fermi level of graphene, showing an increase in the transmission amplitude. The limit of detection (LOD) achieved in this experiment is 0.01 μg/mL. These findings demonstrate the potential of graphene-based metamaterial biosensors for trace detection of residual pesticides, and they may also be applied for detection devices in food hygiene and medical fields.x = Py = 150 μm, and a square ring with an outer length of L1 = 122 μm and an inner length of L2 = 90 μm. The conductivity of the aluminum material is σ = 3.56 × 107 Sm−1 [z-axis, with the electric field direction along the positive direction of the x-axis. The unit cell boundary condition was utilized both in the X and Y directions, and the open boundary was utilized in the Z direction to achieve a perfect absorption boundary.The proposed model of the graphene-based metamaterial biosensor is depicted in 107 Sm−1 . Polyimi107 Sm−1 . Prior tF ranging from 0.01 eV to 0.04 eV are analyzed. As the Fermi level of graphene increases, the amplitude at the resonance frequency gradually rises, indicating that the resonance becomes weaker. The effective conductivity of the biosensor also increases as the Fermi level rises, which is the main reason for the weaker resonance. As shown in F from 0.01 eV to 0.04 eV, the change in transmission amplitude ΔT and ΔEF exhibit a good linear relationship with a fitting degree of R2 = 0.99, which is beneficial for the following detection of substances.The designed biosensor operates by detecting changes in the Fermi level of the graphene induced by the analyte, which in turn causes changes in the resonance strength ,33. In tIn addition, the surface electric fields on the top layer of the biosensor at the Fermi level of 0.10–0.14 eV are studied. As shown in 1 and L2, respectively) of the aluminum rings are selected as key parameters, and different spectra are analyzed. The change in transmission amplitude (ΔT) is set as the difference in amplitude when the Fermi energy level (ΔEF) changes. The larger the value of ΔT, the more sensitive the sensor is to changes in the Fermi level of graphene. Therefore, the influence of the structural parameters on the dynamic range of amplitude can show the change in sensitivity.The structural parameters of the graphene-based metamaterial biosensor determine the intensity of the electromagnetic field that can be excited in the aluminum ring array layer. This, in turn, affects the range of transmission amplitude that can be controlled by the monolayer graphene. To investigate this effect, the outer and inner side lengths  in the frequency range of 0.3 to 0.8 THz. As shown in The monolayer graphene used in this experiment was purchased from Jicang-Nano Technology Co., Ltd.  with a size of 10 × 10 mmA standard solution of 10 μg/mL phosalone was purchased from Shanghai Aladdin Biochemical Technology Co., Ltd., Shanghai, China. This standard solution was diluted with acetone to prepare phosalone solutions with concentrations of 0.01 μg/mL, 0.05 μg/mL, 0.1 μg/mL, 1 μg/mL, and 2 μg/mL. These solutions were then applied on the surface of the biosensor for detection. Due to the fast evaporation rate of acetone, heating is not necessary during the drying process.i/T0, where Ti and T0 represent the transmission amplitude at the resonance frequency in the i-th concentration and zero concentration of the analyte, respectively. After extracting the experimental data, ΔTi/T0 for phosalone solution concentrations of 0 μg/mL, 0.01 μg/mL, 0.05 μg/mL, 0.1 μg/mL, 1 μg/mL, and 2 μg/mL corresponds to 0%, 1.92%, 9.37%, 11.32%, 13.73%, and 13.69%, respectively.A pipette was used to drop 20 μL of phosalone solution onto the surface of the biosensor. As the solvent acetone quickly evaporates, the detection time is greatly reduced. Different spectral curves were obtained at different concentrations, as shown in i/T0 increases rapidly and appears to maintain a linear relationship with the concentration of the solution. As concentrations further increase, ΔTi/T0 remains almost unchanged and exhibits a clear saturation effect. Therefore, the relative change in transmission amplitude caused by increasing solution concentrations is not a simple linear trend, which is common in biological sensing. To characterize the binding ability between the monolayer graphene interface and small biological molecules such as phosalone, the Hill model can be employed to fit the experimental data. The description of the Hill equation is as follows [max is the maximum ΔTi/T0 value at the saturation concentration, c is the concentration of the phosalone solution, n is the Hill coefficient, and k is the dissociation constant. The calculation can be performed using the commercial software Origin. The values of the parameters in Equation (1) were obtained by selecting the Hill function for fitting in the software Origin, and then the parameters were determined through the Levenberg–Marquardt iterative algorithm. The calculated Vmax is 0.136, the Hill coefficient n is 1.558, the dissociation constant k is 0.032 μg/mL, and the goodness of fit is 0.999. The consistency between the experimental data and the fitting curve reflects the reliability and accuracy of the results.The relative change in transmission amplitude is presented in  follows :(1)y=VmaThe designed biosensor offers several advantages over traditional pesticide detection methods, such as low cost, ease of operation, and extremely short detection time. Moreover, it is more appropriate for biosensing at trace concentrations than traditional dielectric-metal metamaterials.As shown in A graphene-based metamaterial sensor characterized by spectral amplitude changes is proposed. Taking the phosalone in organophosphorus insecticides as an example, the designed sensor has been simulated and experimentally verified. In the entire sensor structure, the PI film serves as the dielectric layer and the aluminum structure array acts as the metal layer, reducing cost. The monolayer graphene as the sensing interface further enhances the interaction between the surface and the analyte. The structural parameters of the metal layer do not significantly affect the adjustable amplitude range of graphene within a small variation range. Graphene can interact with molecules through π–π stacking, and graphene at different Fermi levels affects the electric field strength that can be excited by the entire metamaterial. Finally, the lowest concentration of detection LOD is 0.01 μg/mL, and the amplitude change in the concentration range of 0–2 μg/mL is in line with the Hill model. The graphene-based metamaterial biosensor has the advantages of a small size, high sensitivity, and label-free detection, making it a promising detection method for food safety industry applications.


Phocoena phocoena in set gillnets is routinely monitored in European Union fisheries, but generally relies on data collected at low spatio-temporal resolution or over short periods. In Denmark, a long-term monitoring programme started in 2010 using electronic monitoring to collect data on porpoise bycatch and gillnet fishing effort at a fine spatial and temporal scale, including time and position of each fishing operation, together with every associated bycatch event. We used these observations to model bycatch rates, given the operational and ecological characteristics of each haul observed in Danish waters. Data on fishing effort from the Danish and Swedish gillnet fleets were collected to predict fleet-wide porpoise bycatch in gillnets at regional level. Between 2010 and 2020, yearly total bycatch averaged 2088 animals (95% Cl: 667–6798). For the Western Baltic assessment unit, bycatch levels were above sustainability thresholds. These results demonstrate that fishing characteristics are key determinants of porpoise bycatch and that classical approaches ignoring these features would produce biased estimates. It emphasizes the need for efficient and informative monitoring methods to understand possible conservation impacts of marine mammal bycatch and to implement tailored mitigation techniques.Incidental captures (bycatch) remain a key global conservation threat for cetaceans. Bycatch of harbour porpoise  GillnetEstimating the bycatch of marine mammal species in an area requires fishing effort estimates for the fishing fleet(s) responsible for incidental captures and their respective bycatch rate estimates. Such data need to be representative of both fleet effort and bycatch rates, e.g. using randomized sampling stratification . EstimatBycatch rates in gillnets are dependent on a combination of biotic and abiotic factors such as the spatio-temporal distribution of the sensitive species and the target species, and the characteristics of the fishing gear  . The conPhocoena phocoena) is an abundant cetacean categorized globally as ‘Least Concern’ on the IUCN Red List [The harbour porpoise  (i)a).Bycatch of harbour porpoises in commercial gillnet fisheries in Denmark and Sweden was estimated using EM systems installed onboard 17 Danish gillnet vessels between 2010 and 2020. Individual vessels' monitoring varied from several consecutive months to years. The sampling area covered the most important commercial gillnet fishing grounds around mainland Denmark and Western Sweden, however not including the Baltic Proper a.Figurehttps://www.archipelago.ca/) was used on all participating vessels from 2010 and replaced in 2013 with Black Box Video . Both EM systems consisted of a control unit, associated with a position sensor (GPS) and a set of at least two waterproof CCTV (closed-circuit television) cameras recording the fishing activity. The cameras were positioned to allow (by)catch items to be observable from different angles—where the net appears from the water and at the sorting table—thus maximizing the chance of identifying target and bycatch species with certainty.Monitoring was conducted using two different EM systems. EM Observe .Each EM system came with its own analyser programme—respectively, EM Interpret  and Blackbox Analyzer (Anchorlab). These programmes show a map with the GPS trace of a vessel, alongside the videos. Trained EM analysts manually reviewed a census of the EM data to detect fishing events and bycatches. In short, EM data processing consisted of identifying each fishing event (setting and hauling of nets) and reviewing the entirety of the video data for each haul to spot harbour porpoise bycatches. Each bycatch event was marked with a timestamp recording location and time of day. Both EM analysing softwares could replay a sequence, adjust playback speed or zoom on areas of interest during the review process. Video quality could vary greatly depending on e.g. darkness, weather conditions or the general cleanliness of the camera lenses. Exceptionally, videos' feed quality was too bad to detect a porpoise bycatch event, and on such occasions ‘true’ bycatch rates would have been underestimated. However, based on the small amount of low-quality data, we considered this negligible. All EM data were saved to be reviewed multiple times if needed. A detailed protocol is available online (see  (b)Gadus morhua)—must be registered in daily-reported logbooks [In the European Union (EU), the fishing activity of all vessels above 10 m—the limit is down to 8 m in the Baltic Sea if the main target is cod . Although there are differences in reporting catches and landings between Denmark and Sweden, the gillnet fisheries in these countries share a common fishing ground and are largely similar in their fishing patterns, so we assumed that observations made in the Danish fleet of porpoise bycatch could be applied to Swedish vessels. Importantly, information from German gillnetters who also operate in the western part of the Baltic Sea was not included, as German gillnet fishing effort data are not reported at a comparable spatial and temporal scale [A census of the Danish and Swedish fisher-reported data, including all official logbooks, monthly declarations, sales notes and landing declarations, was collected for all the commercial vessels having registered set nets as their primary or secondary gear between 2010 and 2020 c. Althoual scale .ad hoc functions in the R statistical software [Danish and Swedish effort datasets were cleaned for clerical errors . We summarized the data, so that one row would correspond to a unique fishing day per ICES rectangle per vessel. When more than one mesh-size was reported for the same fishing day in the same ICES rectangle, only the mesh-size of the main target species  was kept. Electronic supplementary material, figure S1 summarizes the changes in fishing effort by mesh-size class. Soak-time was averaged out from the values reported in each stratum and net-length was calculated as the sum of all the net fleet lengths in each stratum. In the Danish data, soak-time and net-length were only readily available for the vessels monitored with EM. Retrieving these data was nevertheless essential to run the subsequent analytical modelling, so we used the vessels in the EM dataset as a reference fleet, estimated the average net-length and soak-times from the validated EM data and applied these values to the portion of the Danish fleet not monitored with EM, using expert judgement in the strata with low sampling effort . Smaller vessels for which only monthly declarations exist do not report their fishing locations, thus their fishing effort was allocated to the ICES rectangle in which their home harbour lies. Depth was estimated for each point in the datasets using a high-resolution bathymetric map and software ; for Dansoftware ,38 were  (c)The total number of harbour porpoise bycaught in the Danish and Swedish commercial gillnet fleets was estimated in two alternative ways. First, we estimated mean observed bycatch rates, stratified spatially and temporally, using EM data collected on Danish vessels  and raised these numbers to the number of fishing days in the fleet to estimate total bycatch. Next, we used the same EM data to build a statistical model to predict bycatch of porpoise from operational variables in the entire fleet and ecological variables in the surveyed areas. The results from these two approaches were then compared in terms of mean estimates and uncertainty. (i)Scaling-up observed bycatch rates to fleet-level is often used in bycatch studies . Here, w (ii)Mixed models allow for departure from independence in ecological data ,32. HereglmmTMB package to create the models and the bblme package to compare them [DHARMa package [predict in R). This was done using a modified version of the glmmTMB package that included an aggregate function (https://github.com/glmmTMB/glmmTMB/tree/aggregate), which allowed both quantification of the uncertainty and the performance of bias correction for sums of predictions on the natural scale . Bias correction is necessary because sums on the natural scale are a nonlinear transformation of the random effects in the model [https://github.com/gildas-glemarec/bias-corrected-estimates-of-harbour-porpoise-bycatch-in-gillnet-fisheries).Alternative models were compared using the Akaike Information Criterion modified for small sample size (AICc). All data treatments and analyses were conducted in R, using the are them ,40. The  package  (electrohe model . Corresp (d)As opposed to the scaling-up approach, a model-based method could account explicitly for variations in operational factors in the gillnet fishery, e.g. differences in vessel-length, mesh-sizes, or soak-time. In turn, we could explore the underlying reasons for possible trends by standardizing bycatch rates and fishing fleet effort. For this, the terms in the model formula were grouped in two categories. One group described the effect of different types of fishing effort on the amount of bycatch, i.e. vessel, mesh-size and soak-time effects, while the other group described changes in porpoise density over time and space. This separation of model terms allowed for calculating not only standardized bycatch numbers, but also standardized effort and porpoise BPUE relative to other years/areas.The standardized effort was calculated by fixing all porpoise density terms in the model to some arbitrary constant value for all years and spatial positions , and predicting the amount of bycatch given the observed effort data. The result was then proportional to the bycatch per individual porpoise for a given set of effort data, since it accounts for different values of e.g. mesh-size, soak-time and net-length having different risks of catching porpoise. Similarly, the standardized BPUE was calculated by fixing all the effort-related variables to constant values and predicting the bycatch for each year in all spatial positions.This separation of effort and porpoise density terms offers a possibility to produce spatially explicit estimates of relative porpoise densities (standardized BPUE) and relative porpoise mortality due to fishing (standardized effort) in addition to total bycatch estimates. The total bycatch is consequently the product of porpoise density and effort in each stratum . Moreover, what we refer to as ‘standardized effort’ is not necessarily indicative of the actual fishing effort but is rather the porpoise mortality induced by the fishing effort. (e)recovering to 80% of their carrying capacity in the long-term [modified PBR (mPBR) [et al. [2, 2281 km2 and 17 862 km2. Then, we estimated four threshold values (two PBR- and two mPBR-based) for each year in which an abundance estimate was available, with recovery factor values associated with uncertain estimation processes for abundance and bycatch, and with more accurate and precise estimates  = 0.5 or 1.0 for PBR, and Fr = 0.15 or 0.35 for mPBR).We estimated the potential biological removal limit (PBR) for harbour porpoise in the Western Baltic , to evaluate the sustainability of the bycatch levels in that assessment unit . There iong-term . This haR (mPBR) . Here, w [et al.  to tune  [et al. . This cl [et al. . However. 3In total, 6139 individual fishing days were recorded and analysed in the Danish EM fleet between 2010 and 2020, during which 525 harbour porpoise bycatches were observed in hauls where no pingers were used. At least one porpoise was registered as bycatch in 8.6% of the recorded fishing days. (a)id) was not retained in most models, suggesting that the bycatch rates depend foremostly on the characteristics of the fishing gears described by the fixed effects rather than on specific fishing vessel behaviour. Interaction terms generally increased model fit, with the final model retaining only the interaction between mesh-size and soak-time.The model selection  favoured a spatial autocorrelation component structure with a random variation of exponential decay in correlation with the distance within year and within quarter. This indicates that bycatch observations tend to cluster both in space and in time or, in other words, that there exist areas of higher risk of bycatch. The random variable coding for the unique vessel identifier  indicated that the most prominent contributors to porpoise bycatch were mesh-size, soak-time and vessel-length. (b)y-axes is arbitrary in the middle and bottom plots in Total porpoise bycatch estimates from model predictions were aggregated by year for two focal areas: the Western Baltic, and the North Sea and Skagerrak. These numbers were compared with the bycatch estimates obtained from scaling-up the mean EM observed bycatch rates to the entire fleet for the same two focal areas —top. UsiMean values of the bycatch estimates differed between the two methods —top. On Standardization demonstrated that, although there are important differences between the Western Baltic and the North Sea and Skagerrak focal areas, with higher mean bycatch rates in the latter —middle, Model-based estimates revealed areas and times of the year with higher risks of porpoise bycatch  and allo (c)Given the uncertain nature of the abundance estimates in 2012 and 2016, we selected the thresholds more robust to potential biases in abundance estimates for these years  reached 862 animals (238–3198) when assuming that pinger usage strictly follows the legislation and reduces bycatch by 100%, and up to 939 (206–3679) when pinger implementation is ignored . The sus. 4To the best of our knowledge, this study is the first to present fleet-level estimates of any cetacean species bycatch in commercial gillnets, correcting for fleet characteristics and using a long time series of fine-scale EM data. This work yields a model able to predict harbour porpoise bycatch from fisheries-dependent data. In the study area—the Eastern North Sea, Skagerrak and Western Baltic—2089 porpoises were predicted to have been bycaught in 2020, provided that acoustic deterrents are used according to the legislation and that they totally mitigate bycatch. Not accounting for pinger usage, we predicted 2617 bycatches in 2020 . Both esFor the Western Baltic, the estimates from model predictions showed very similar results to those using traditional BPUE extrapolation method, likely because the vessels here fish in a very homogeneous way . As the fleet is more homogeneous, the monitoring data are also more representative of the fleet, thus the two ways of predicting total bycatch yield similar outcomes. Conversely, the fishery in the North Sea is more heterogeneous, being characterized by a mixture of large and small vessels with varying fishing trip duration, and varying target species needing different net-lengths, mesh-size and soak-times. As the model accounts for these fishing characteristics, the predicted total bycatch estimates emerging from the modelled bycatch rates account for the fleet variability. The BPUE applied to fishing days assumes that all fishing days are equally likely to bycatch porpoises, regardless of fishing characteristics, which can therefore yield biased estimates when the sampling design cannot account for representativeness . This sh (a)We estimated that 939 porpoises (206–3679) were bycaught in 2020 in the Western Baltic, when no pingers are assumed to have been used . This isBased on our model predictions, 1678 porpoises (576–5074) were bycaught in gillnets in the Eastern North Sea and Skagerrak in 2020, assuming no pinger usage . This isIt is important to recall that, in this paper, German gillnet effort is not included in the estimation process, notably because of the peculiarities of fishing effort reporting in Germany, which is not standardized with other countries . This me (b)c; electronic supplementary material, figures S2 and S3). Instead, predicted high bycatch areas were associated with areas where fishing methods  are more conducive to bycatch. This has important implications for mitigation and shows that simply reducing the number of fishing days in an area of high bycatch may not be sufficient to reduce total bycatch. Our work highlights which fishing techniques should be prioritized for mitigation, once bycatch probability and fishing intensity have been accounted for .Total bycatch is not homogeneously distributed in the study area . Areas ofigure 1Our model prediction approach can help estimate how inferred relative porpoise density, fishing characteristics and fishing intensity interact to yield bycatch levels . Standardized bycatch rates  is the product of porpoise density and the bycatch probability of individuals. Our model allowed us to separate these two terms and give spatially explicit estimates of relative porpoise density, mortality due to bycatch, and total bycatch as the product of the two. Independent measures of porpoise density are therefore not necessary to estimate spatially explicit measures of relative porpoise densities and bycatch probabilities. The estimated bycatch probabilities capture the spatial heterogeneity in the species propensity to become entangled in fishing gear as the measure is standardized for fishing characteristics. This spatial heterogeneity could emerge from behavioural variance  or demographic characteristics . Regardless of causes, a spatially explicit bycatch probability estimate is an extremely useful tool to plan the best-suited mitigation methods. (c)The original EU pinger regulation and its succeeding iteration ,38 have  (d)Bycatch rate estimation depends on two important assumptions. First, the EM observed data need to be representative of the fleet in terms of area, time of year and fishing characteristics such as vessel-length and target species. Our voluntary EM observation covered most areas except the northern and central part of Kattegat and the southern North Sea . The EM All vessels participated voluntarily, and vessel crews were aware that the hauls were being monitored. There is a known tendency for fishers to shift from normal fishing practices when they know they are being observed , implyinWhen estimating bycatch using our modelling approach, assumptions had to be made about mean net-length and mean soak-time. These assumptions clearly come with errors as no fishers have exactly equal fishing patterns. However, it improves vastly on bycatch estimation methods earlier used by WGBYC, which assumes that all vessels have the same kind of ‘fishing day’ regardless of vessel-length and mesh-sizes. In all cases, this paper stresses the crucial need to have more accurate and precise national statistics on fishing effort. Key variables influencing bycatch probability  are required to derive robust bycatch estimates. In our case, although there are differences in reporting catches and landings between Denmark and Sweden, the gillnet fisheries in these countries share common fishing grounds in the Western Baltic and Eastern Greater North Sea and are largely similar in their fishing patterns, so we assumed that observations made in the Danish fleet of porpoise bycatch and resulting mean bycatch rates could be applied to Swedish vessels. (e)Regardless of threshold estimation assumptions, bycatch levels in the Western Baltic are above the threshold values estimated to yield sustainable bycatch limits. Nevertheless, PBR estimates do fall within the confidence intervals of the estimated yearly total bycatch . Yet thestricto sensu. Second, the assessment unit considered here is more open to immigration and/or movement of harbour porpoises from neighbouring regions than previously thought and is therefore ill-defined. Several hundreds of porpoises would need to immigrate yearly into the Western Baltic to maintain a stable population given the discrepancies between the estimated threshold and the level of bycatch predicted from our model. This would mean that the Western Baltic harbour porpoise assessment unit is extremely open to what is currently considered the North Sea assessment unit [This could be for two reasons. First, we could assume that the population can sustain the level of bycatch to which it is exposed, implying that the mPBR- and PBR-based threshold values are over-precautious. Regarding mPBR, this makes sense as the objective is to maintain or restore to carrying capacity a population far away from its maximum net productivity level (MNPL). In that state, the net productivity the population can achieve is much lower than MNPL, forbidding sustaining as many excess mortalities. The mPBR objective is to ensure that the population thrives, not that it cannot become depleted. For PBR, however, the objective is that the population remain at or above MNPL with high certainty. Hence, PBR estimates here are likely cautious and should be treated as reference points over which bycatch levels need greater attention rather than a threshold ent unit  as the Bent unit . In addient unit . The undTwo contrasting conclusions emerge from this work. One option is that we accept the current assessment unit. In this case, PBR is a more appropriate measure of sustainable bycatch limit than mPBR, as PBR falls within the confidence interval of the predicted total bycatch. This would mean that the alternative conservation objective (a restoration objective) first set by ASCOBANS, while well-intentioned, is not useful to guide bycatch management—in this instance at least. Under such a restoration objective, mitigation measures would likely be required from the moment a single porpoise is bycaught. The alternative is that the delineation of the assessment unit is not making sense and we need to consider large influx of porpoises from the North Sea to maintain the population and the bycatch levels observed; in which case the assessment unit needs to be merged with the North Sea one.. 5Globally, we need to prioritize the generation of fisheries statistics at a fine scale of effort. This information is crucial as we try to achieve ambitious biodiversity restoration targets while maintaining the vital food security provided by small-scale coastal fisheries .Coastal fisheries are facing rapid socioecological changes, like many other human activities in the 2020s. In the Western Baltic, gillnet fishing effort decreased significantly over the past decade , and this will most likely continue due to the current limitations on cod fishing. This will in turn yield a reduction in porpoise bycatch. Nonetheless, as cod gillnet fishers will switch to other target species, change gear, or stop fishing, the way in which changes in fishing practices and intensity will affect porpoise bycatch remains largely unknown. Furthermore, we show here that the current pinger deployment regulations are ineffective at reducing bycatch; not because pingers are not an effective tool to reduce bycatch, but because the regulated pinger deployment schedule is not targeting the vessels and instances yielding the greatest bycatch risk.


Background and objectiveChatGPT is an artificial intelligence (AI) language model that has been trained to process and respond to questions across a wide range of topics. It is also capable of solving problems in medical educational topics. However, the capability of ChatGPT to accurately answer first- and second-order knowledge questions in the field of microbiology has not been explored so far. Hence, in this study, we aimed to analyze the capability of ChatGPT in answering first- and second-order questions on the subject of microbiology.Materials and methodsBased on the competency-based medical education (CBME) curriculum of the subject of microbiology, we prepared a set of first-order and second-order questions. For the total of eight modules in the CBME curriculum for microbiology, we prepared six first-order and six second-order knowledge questions according to the National Medical Commission-recommended CBME curriculum, amounting to a total of (8 x 12) 96 questions. The questions were checked for content validity by three expert microbiologists. These questions were used to converse with ChatGPT by a single user and responses were recorded for further analysis. The answers were scored by three microbiologists on a rating scale of 0-5. The average of three scores was taken as the final score for analysis. As the data were not normally distributed, we used a non-parametric statistical test. The overall scores were tested by a one-sample median test with hypothetical values of 4 and 5. The scores of answers to first-order and second-order questions were compared by the Mann-Whitney U test. Module-wise responses were tested by the Kruskall-Wallis test followed by the post hoc test for pairwise comparisons.ResultsThe overall score of 96 answers was 4.04 ±0.37  with the mean score of answers to first-order knowledge questions being 4.07 ±0.32  and that of answers to second-order knowledge questions being 3.99 ±0.43  (Mann-Whitney p=0.4). The score was significantly below the score of 5 (one-sample median test p<0.0001) and similar to 4 (one-sample median test p=0.09). Overall, there was a variation in median scores obtained in eight categories of topics in microbiology, indicating inconsistent performance in different topics.ConclusionThe results of the study indicate that ChatGPT is capable of answering both first- and second-order knowledge questions related to the subject of microbiology. The model achieved an accuracy of approximately 80% and there was no difference between the model's capability of answering first-order questions and second-order knowledge questions. The findings of this study suggest that ChatGPT has the potential to be an effective tool for automated question-answering in the field of microbiology. However, continued improvement in the training and development of language models is necessary to enhance their performance and make them suitable for academic use. In recent years, natural language processing (NLP) and machine learning (ML) techniques have revolutionized the field of question-answering systems . ChatGPTMicrobiology is a fundamental subject in medical education and it deals with the study of microorganisms, including bacteria, viruses, fungi, and parasites. Medical students need to have a sound understanding of microbiology as it is directly related to the diagnosis and treatment of infectious diseases . CompeteAutomated question-answering systems have the potential to transform medical education by providing instant and accurate responses to complex questions . The useIn light of this, in this study, we aimed to evaluate the performance of ChatGPT in answering first- and second-order knowledge questions related to microbiology. This could aid in the development of effective educational tools that can help medical students master the subject of microbiology. The results of this study could also shed light on the strengths and weaknesses of ChatGPT as an automated question-answering system with regard to the field of microbiology and pave the way for further research in this field.Study design and settingThis cross-sectional observational study was conducted by the department of Microbiology, Pathology, and Physiology, All India Institute of Medical Sciences, Deoghar, Jharkhand, India in February 2023. We used an online AI language model - ChatGPT - to converse with and the responses were collected for analysis. The program is free for registered users at this point in time.Ethical considerationThis study does not involve any human or animal research participants. Responses generated by the language model were not shared in this paper. Hence, according to current guidelines in the country, this study does not require any ethical clearance.Question preparationAccording to the CBME curriculum for the subject of microbiology, we prepared a set of first- and second-order questions. The researchers prepared a total of 96 questions, with six first-order and six second-order knowledge questions for each of the eight modules, as shown in Table Data collectionThe questions were used to converse with ChatGPT by a single user. The responses were recorded for further analysis. We did not use the “regenerate response” and took the first response to be the final answer.Scoring of answersThe responses generated by ChatGPT were scored by three microbiologists. The scoring was done on a rating scale of 0-5, where 0 indicates an incorrect answer and 5 indicates a fully correct answer. The average of the three scores was taken as the final score for each response. The brief study protocol is depicted in Figure Having multiple evaluators to assess an answer can help reduce biases and increase the accuracy and reliability of the evaluation. Different evaluators may have different biases based on their personal experiences, perspectives, or preferences. Having multiple evaluators can help mitigate these biases. Hence, we employed three evaluators to reduce biases.Statistical analysisAs the data were not normally distributed, non-parametric statistical tests were used for analysis. The overall scores were tested using a one-sample median test with hypothetical values of 4 and 5. The scores of the first-order and second-order knowledge questions were compared using the Mann-Whitney U test. The module-wise responses were tested using the Kruskal-Wallis test, followed by the post hoc test for pairwise comparisons . All theThe overall mean score of 96 answers was 4.04 ±0.37 . The frequency distribution of scores of 96 answers to both first-order and second-order knowledge questions is shown in Figure The mean score of the answers to first-order knowledge questions was 4.07 ±0.32 , and that of answers to second-order questions was 3.99 ±0.43 . The scores for answers to first- and second-order knowledge questions were similar (Mann-Whitney p=0.4), as shown in Figure The score was significantly below the score of 5 (one-sample median test p<0.0001) and similar to 4 (one-sample median test p=0.09), indicating an accuracy rate of 80%.The highest score was obtained in the central nervous system infections category 4.25±0.27) and the lowest score was obtained in the gastrointestinal and hepatobiliary systems category (3.75±0.36). The competency topic-wise distribution of scores is shown in terms of descriptive statistics in Table 5±0.27 anWhen we compared the scores among various categories according to CBME, we found that there was an overall difference (p=0.02) among the scores, indicating that the application's performance may vary in different topics in microbiology. The comparison is shown in Figure Based on our findings, ChatGPT is capable of providing answers to both first- and second-order knowledge questions with approximately 80% accuracy. This corroborates the findings of Sinha et al . There wAcross different competency modules, the score was significantly different. The highest score was found for questions related to central nervous system infections and the lowest score was obtained in the gastrointestinal and hepatobiliary systems. The underlying cause may be attributed to the limitation of training the AI system. Any deviation from the uniformity of difficulty may be another reason for the different levels of performance of the language model. Hence, there is scope for further improvement of the model to make it more suitable for academic usage. The applicability of language models like ChatGPT in academic usage is wide-ranging and constantly expanding, and they can be used to enhance various aspects of academic work, including research, writing, teaching, and learning ,13.This study has certain limitations. We only evaluated the capability of ChatGPT in answering first- and second-order knowledge questions on the subject of microbiology, and the findings may not be generalizable to other subjects or domains. Secondly, the study only used a single user to converse with ChatGPT. Conversations with another user at different time points may generate a different response. Furthermore, paraphrasing the questions showed variations in the answers. The scoring of the responses was subjective; hence, evaluation bias may be present even after taking the average of three scores from three evaluators.The study showed that ChatGPT is a potential tool for answering both first- and second-order knowledge questions with equal vigor in the field of microbiology. The accuracy rate of approximately 80% indicates that ChatGPT has the capability to be a valuable automated question-answering system for students. Although the model showed high accuracy, there is still room for improvement, and it is necessary to fine-tune the model to enhance its performance to achieve nearly 100% accuracy. In the future, with further improvement, ChatGPT-like programs may be used for academic purposes.


Using the self-consistent charge density functional tight-binding (SCC-DFTB) method, we study the behavior of graphene-carbon nanotube hybrid films with island topology under axial deformation. Hybrid films are formed by AB-stacked bilayer graphene and horizontally aligned chiral single-walled carbon nanotubes (SWCNTs) with chirality indices  and 1.2 nm in diameter. In hybrid films, bilayer graphene is located above the nanotube, forming the so-called “islands” of increased carbon density, which correspond to known experimental data on the synthesis of graphene-nanotube composites. Two types of axial deformation are considered: stretching and compression. It has been established that bilayer graphene-SWCNT  hybrid films are characterized by elastic deformation both in the case of axial stretching and axial compression. At the same time, the resistance of the atomic network of bilayer graphene-SWCNT  hybrid films to failure is higher in the case of axial compression. Within the framework of the Landauer-Buttiker formalism, the current-voltage characteristics of bilayer graphene-SWCNT  hybrid films are calculated. It is shown that the slope of the current-voltage characteristic and the maximum values of the current are sensitive to the topological features of the bilayer graphene in the composition of graphene-SWCNT  hybrid film. Based on the obtained results, the prospects for the use of island-type graphene-nanotube films in flexible and stretchable electronic devices are predicted. Over the past decades, carbon nanotubes (CNTs) and graphene have been the most discussed members of the nanocarbon allotropes family ,2. The uUsing technologies such as CVD, solution processing, layer-by-layer self-assembly, and vacuum filtration, graphene-CNT hybrid structures with various topological configurations are synthesized ,9,10,11.Quite a lot of papers are devoted to the study of mechanical, thermal, and electronic properties of graphene-CNT hybrid structures with vertically aligned CNTs using computational modeling methods ,33,34,35At the same time, in the above-mentioned articles, only non-chiral SWCNTs were used in the construction of topological models of graphene-CNT hybrid structures, while the majority of SWCNTs obtained during real synthesis are chiral nanotubes with a diameter of 0.7–1.2 nm. In addition, in a real experiment on the synthesis of graphene-CNT hybrids, structures with areas of increased density of carbon atoms are often obtained. This article discusses atomic models of graphene-nanotube hybrid structures with chiral SWCNTs  of subnanometer diameter. We carry out predictive studies of the behavior of such structures under the stretching/compression deformations and evaluate how the electrically conductive properties change in this case.To calculate the atomic structure and energy parameters of graphene-SWCNT  hybrid films, we used the self-consistent charge density functional tight-binding (SCC-DFTB) method  implemenG was calculated within the framework of the Landauer-Buttiker formalism [T(E) is the electron transmission function, TF is the function that determines the temperature broadening of the energy levels, FE is the Fermi energy of the contacts, e is the electron charge, h is Planck’s constant, 2e2/h is the value of the conductance quantum doubled to consider the spin. The electron transmission function T(E) is given by: The electrical conductivity ormalism  accordinT(E) [T(E) for a small number of k-points of the first Brillouin zone, and then interpolate it for any k-point of the first Brillouin zone and restore the full transmission function T(E). All calculations were carried out at a temperature of 300 K.Since the supercells of the studied graphene-nanotube structures contain 250–300 atoms, an original method was used to accelerate the calculation of the transmission function T(E) . This med along the Y axis  also changed: d = 0.27 nm for model V1 and d = 0.06 nm for model V2. The distance between graphene layers along the Z axis (perpendicular to the nanotube axis) was the same for both topological models and amounted to 0.34 nm.Within the framework of this study, two topological models of graphene-SWCNT hybrid films were considered. The supercell of each model was a 2D structure formed by an AB-stacked bilayer zigzag graphene nanoribbon (ZGNR) and SWCNT with chirality indices . The bilayer graphene was located above the surface of the carbon nanotube perpendicular to its axis, forming the so-called “islands” of increased carbon density in the composition of the hybrid structure. xL = 1.707 nm and yL = 1.110 nm for model V1, xL = 1.723 nm, and yL = 1.134 nm for model V2. Previously, based on the results of calculating the binding energy, it was found that the supercells of the considered topological models are energetically stable [The supercells of bilayer graphene-SWCNT  hybrid films with an island topology after optimization of the atomic structure (finding of the equilibrium configuration) are shown in y stable .The main objective of our study was to reveal the regularities in the behavior of bilayer graphene-SWCNT  hybrid films with island topology under mechanical deformations and to evaluate how their electrically conductive properties would change in this case. We considered two types of deformation: axial stretching and axial compression. Their choice is due to the fact that in the design of flexible and stretchable electronic devices, functional nanomaterials are tested precisely for these types of deformation.xL of supercells was increased by 1%. Then, the search for the equilibrium configuration of the deformed structure was carried out by optimizing the geometric parameters of the supercell. Based on the results of the numerical simulations, the stretching strength limits of the topological models were determined. strE on the stretching value ΔL in relative units. The strain energy strE was calculated according to the formula:totE1 is the total energy of the supercell before stretching, totE2 is the total energy of the supercell after stretching, and N is the number of atoms in the supercell.Let us turn to the consideration of the case of axial stretching deformation. In the course of the numerical experiments, we simulated the stretching of supercells of bilayer graphene-SWCNT  hybrid films along the X-axis, i.e., along the zigzag edge of bilayer graphene. This variant of deformation application is due to the following reason. The supercell of the 2D hybrid structure of bilayer graphene-SWCNT  includes a fragment of bilayer graphene with different side lengths in the X and Y directions. The difference between the graphene sizes along the X and Y axes is about 3 times. Therefore, we can say that the bilayer graphene in a supercell has the shape of a nanoribbon with an extended side along the X-axis. In this regard, it is logical to assume that the stresses of the atomic network will differ greatly when a load was applied to the graphene side with a smaller extension  and when a load was applied to the graphene side with a larger extension . Step-by-step deformation was carried out. At each step, the length of the translation vector The calculated dependencies are shown in I–V) characteristics of bilayer graphene-SWCNT  hybrid films for topological models V1 and V2 under stretching by different percentages. For model V1, the I–V characteristic practically does not change in the strain range of 1–10%. A decrease in the slope of the I–V characteristic and the maximum current values is observed only at 14% stretching, i.e., immediately before breaking the covalent bonds between atoms in the graphene bilayer. For model V2, the slope of the I–V characteristic increases with increasing stretching, and the maximum current value is almost two times higher than the maximum current value of model V1.Let us turn to reveal the patterns of current flow in bilayer graphene-SWCNT  hybrid films with island topology under deformation. Since axial stretching along the X axis  was considered in the framework of the numerical experiments, electron transport calculations were also carried out only for the case of current transfer along the X axis. For the operation of stretchable electronic devices, the current characteristics of materials are of interest. To explain this difference in the behavior of the topological models V1 and V2, we performed a Mulliken Population Analysis. The calculated distributions of the electron charge density over the atoms of supercells of models V1 and V2 for the cases of the minimum and maximum allowable strain are shown in Thus, model V1 demonstrates a higher resistance to failure when stretched along the X-axis while maintaining electrically conductive properties.xL) decreased by 1%. The pattern of changes in the atomic network of supercells at different values of axial compression is shown in compE on the axial compression in relative units for the models V1 and V2 are shown in The study of the effect of axial compression on the structural and electrically conductive properties of bilayer graphene-SWCNT  hybrid films was carried out according to the same plan as the study of the effect of axial stretching. Stepwise compression of supercells of models V1 and V2 along the zigzag edge of the graphene bilayer  was simulated. At each deformation step, the supercell length along the X-axis  hybrid films to conduct electric current. The calculated current-voltage characteristics of supercells of models V1 and V2 for the cases of minimum and maximum axial compression, as well as in the absence of deformation, are shown in Comparing the maximum current values at a voltage of 2.2 V for supercells of models V1 and V2, it can be seen that for model V2, the maximum current is almost two times higher than for model V1. The same trend was found for the V2 model in the case of axial stretching. To clarify this result, we calculated the distributions of the electron charge density over the atoms of the supercells of models V1 and V2 in the cases of minimal and ultimate axial compression. They are shown in Thus, the results of simulating axial compression showed that the model V2 of bilayer graphene-SWCNT  hybrid films is characterized by the highest resistance to failure on compression, as well as maximum current values and negligibly small changes in the current-voltage characteristics with increasing strain.Discussing what could cause such differences between the behavior of models V1 and V2 both under axial compression and under axial stretching, we analyzed the topology of the extended fragments of the atomic structure of each model obtained by translating their supercells .To confirm our assumption, let us compare the current-voltage characteristics of a bilayer graphene-SWCNT  hybrid film and AB-staked bilayer graphene calculated for the limiting cases of axial stretching/compression. Hence, the key topological parameters from the standpoint of controlling the electrically conductive properties of bilayer graphene-SWCNT films  hybrid structures are the width of the bilayer graphene nanoribbon and the magnitude of the shift of the graphene layers in the supercell.Thus, we have studied the deformation behavior and electrically conductive properties of bilayer graphene-SWCNT  hybrid films with an island topology under axial stretching and compression. It has been found that the topology of a graphene bilayer in a supercell is a key factor in determining the response of a bilayer graphene-SWCNT  hybrid structure to axial stretching/compression. The topological model of a bilayer graphene-SWCNT  hybrid film with a ZGNR width of 3 hexagons as part of a supercell is characterized by the highest resistance to failure on axial stretching and stability of electrically conductive properties. The topological model of a bilayer graphene-SWCNT  hybrid film with a bilayer nanoribbon width of 4 hexagons as part of a supercell has the highest resistance to failure on axial compression, a stable current-voltage characteristic slope, and maximum current values. By controlling the topology of bilayer graphene in the composition of island-type hybrid graphene-nanotube structures, it is possible to achieve optimal values of their electrically conductive characteristics for subsequent use in flexible and stretchable electronic devices, in particular, as conductive electrodes.


Dynamically controlling friction in micro- and nanoscale devices is possible using applied electrical bias between contacting surfaces, but this can also induce unwanted reactions which can affect device performance. External electric fields provide a way around this limitation by removing the need to apply bias directly between the contacting surfaces. 2D materials are promising candidates for this approach as their properties can be easily tuned by electric fields and they can be straightforwardly used as surface coatings. This work investigates the friction between single layer graphene and an atomic force microscope tip under the influence of external electric fields. While the primary effect in most systems is electrostatically controllable adhesion, graphene in contact with semiconducting tips exhibits a regime of unexpectedly enhanced and highly tunable friction. The origins of this phenomenon are discussed in the context of fundamental frictional dissipation mechanisms considering stick slip behavior, electron-phonon coupling and viscous electronic flow. Electric fields achieved by applying electrical bias directly between the sliding surfaces are commonly used to tune friction. Here, the authors reveal that friction between graphene and a semiconductor is sensitive to the charge density of graphene while influenced by an indirect electric field. The ability to control the friction force at the sliding contact relies on understanding the underlying mechanisms. Beyond the passive control of friction through the selection of the design components , a more recent trend has been to investigate systems whose frictional response can be dynamically tuned in situ, especially as micro- and nanoscale devices become more common. One of the more promising avenues to achieve friction control is through the use of external electric fields that can modulate the properties of lubricants and material surfaces as well as the interactions between them4.Novel approaches to the design of interacting surfaces are necessary to move past the current state of the art, and 2D materials are a new and excellent material choice based on their high mechanical strength and chemical and thermal stability. Many 2D materials exhibit intrinsically low friction and are additionally interesting as they can be tuned or doped. This doping effect can be achieved in the traditional sense of atomic substitution, through interactions with the material’s environment , or through the application of external fields. Graphene, the 2D allotrope of carbon, has been widely studied in this context. Specifically, the graphene field effect transistor (FET) is a common device used to dynamically alter the sheet’s properties in situ. FET devices are constructed such that a voltage is applied to a gate that is electrically insulated from the material of interest. This leads to an indirect effect controlled by the resulting electric field, instead of the direct application of bias to the surface. This has the additional advantage that short circuits can be avoided, which protects machine components and lubricants. It is important to point out that while traditional FETs are designed to be turned on and off, graphene FETs exhibit a gradual response due to graphene’s unique band structure7. Prior research has also investigated the electronic contribution to friction for semi- and superconductors as their carrier density was modified11. Friction between a tip and doped semiconductor substrates was shown to vary with the local carrier concentration; here, a charge depletion (accumulation) resulted in a substantial decrease (increase) in friction11. While the contribution of electron wind, charge carrier dragging, and fluctuating electric fields was estimated to be negligible for gallium arsenide covered with an oxide layer11, the charges trapped in the near-surface layers were responsible for the electrostatically-induced adhesion that justified the variation of the friction force11. These studies demonstrated the relative importance of factors such as charge density and carrier concentration as well as the electrostatic interaction between the two surfaces. Measurements of nanoscale friction on 2D materials with varying in-plane bias have also shown that applying an increasing bias can decrease the measured friction. This has been primarily attributed to a change in the atomic stick-slip behavior and therefore to the frictional dissipation process12. A recent investigation of indirect out-of-plane bias to control carrier concentration via an electric field in a semiconducting MoS2 system associated changes in friction to effects on the electron-phonon coupling13. These previous studies were performed under ambient conditions, and hence, the effects of water traces on the results cannot be excluded. Similar effects have not been investigated for graphene either under out-of-plane or indirect bias yet, particularly through the lens of controlling graphene’s charge density.Previous works have reported the effects of an electric field between an atomic force microscopy (AFM) tip and semiconducting or graphene surfaces—between which a varying bias was directly applied—on adhesion and friction but have focused on revealing the role of water in humid environmentsIn this work, we study the friction at a single asperity nanoscale contact between the graphene surface of graphene FETs and an AFM tip in a dry nitrogen atmosphere, while the doping level of graphene is modulated in situ by changing the potential applied to the device’s back gate. In contrast to conducting or insulating contacts, graphene in contact with semiconducting tips exhibits an enhanced and tunable friction sensitive to the charge density in graphene.2 dielectric layer, and degenerate silicon backgate. Details of sample preparation are described in the Methods section. Raman spectroscopy on the annealed graphene FETs revealed monolayer characteristic peaks (IG/I2D < 0.5) and marginal D peak intensity  microspheres or thermally oxidized Si tips with a thick oxide layer. Insulating tips do not transfer any charge upon contact with graphene. Reference AFM measurements were carried out on 300 nm SiO2 on Si wafer and 50 nm Au-coated Si wafers, which are labeled as insulating and conductive surfaces, respectively. More information about the experiments is found in the Methods section.Three different tip types were used in AFM measurements on the graphene FET devices. Sharp Si tips used directly as supplied by the vendor are referred to as either unmodified or Si tips. Si tips are covered by a thin oxide layer upon exposure to ambient air2) surface  are shown in Fig. ips Fig.  increaseFigure 20, potentially justifying the difference between the local minimum of The electrostatic attraction between graphene and Si tips Fig.  exhibitscf. to reference silica and gold surfaces in Supplementary Fig. X axis shows the potential difference 2 surfaces , the variation of friction with entclass1pt{minimange Fig. , inset. Friction measurements on graphene with Si tips are summarized in Fig. When friction is plotted against ial Fig. . If the Reference measurements on silica surfaces with Si and conducting tips show the same trends as with insulating tips  is roughly half of the CoF, indicating that friction at contacts with minimal charge transfer is more influenced by a varying normal load than a change of electric field. Moreover, this eCoF0 is overestimated based on the magnitude of electrostatic attraction at the distance it is necessarily measured vs. the magnitude in contact . The average value of eCoF1 is comparable to, if not slightly larger than, eCoF0, which indicates that the Si tips act similarly to purely insulating tips in this range. Above the Dirac point, there is a sharp increase in the coefficient of friction (eCoF2 > eCoF1). This value is on average larger than the CoF or eCoF for any other system, suggesting an outsized dependence of friction on the electric field. This frictional response happens when the deviation of the electrostatic attraction before and after contact is observed  and might be partially responsible for the irregular slip features.Figure 25. In this context, the AFM tip is connected by a spring to a moving support that drags the tip with constant velocity over a periodically varying potential landscape 26. Careful analysis of the stick slip reveals a mixture of single and double slips, with more double slips with increase in 27, and hence, they should contribute to the increase of the frictional force with The Prandtl-Tomlinson (PT) model for atomic-scale friction of crystalline surfaces attributes the microscopic origin of the energy dissipation to the stick-slip motion of the tip, where the slip length depends on the dimensions of the crystal lattice28. The results show the monotonic increase in G band energy with −1), while the linewidth is reduced (−1). The smaller linewidth above the Dirac point implies a longer phonon lifetime, which is linked to the increased electron density in n-type doped graphene. While the linewidth quickly plateaus at its reduced value after crossing the Dirac point the monotonic increase of the G band energy continues after crossing the Dirac point; this indicates that a stronger electron-phonon coupling exists in n-doped graphene compared to p-doped graphene and continues to become stronger further past the Dirac point. Note that a previous work showed a symmetric variation of the G band energy and linewidth around the Dirac point, but this was at much lower temperature. At room temperature, asymmetric responses of the G band, similar to our results, have been reported29.A graphene microFET sample with a Dirac point 9. To exclude the influence of thermal effects arising from the applied backgate potential, time-dependent Raman spectroscopy was conducted; experimental details and results are described in the Supplementary Discussion of Supplementary Fig. Both phononic and electronic contributions to friction have been also shown to be temperature dependentIn summary, AFM measurements were carried out on graphene FET devices, while the electric field modulated the charge carrier density in graphene. These results demonstrate the prominent influence of the charge transfer across graphene-tip single asperity contacts on the frictional response. When using insulating tips, charge transfer is minimized; the electric field leads to a large electrostatic attraction, and friction scales linearly with this attraction, with a hysteresis due to the effect of trapped charges. Insulating tips thus enable to tune friction via the electric field, in the same way as directly controlling the tip-sample voltage. The electrostatic attraction is eliminated in gold-coated tips due to their conductive nature, allowing charge transfer upon contact, which counteracts the polarization and significantly reduces the dependence of friction on 31. Raman spectroscopy  sea or flow, while sliding. The carrier density within graphene is modulated by 2 s−1 at finite carrier density, can be speculated to be analogous to the viscosity of a fluid, hence, to impose a resistance to the motion of the AFM tip, which increases friction, and more so with the increase in sliding velocity. The friction of nanoscale systems in fluids is often interpreted in terms of sliding through a rate-dependent potential energy surface, a model adapted by Bell and Eyring37. The analysis of An alternative or complementary explanation is based on the description of the charge carrier flow in high-mobility graphene via a hydrodynamic approachmentclasspt{minima38. A study of metal-supported graphene in a conductive AFM demonstrated that current transfer while sliding is associated with higher friction than sliding with no current across the interface, both under an electric field39. These authors also observed an enhanced friction not fully explained by changes in the electrostatic attraction. In their fully conductive system, they invoke an electronic property fluctuation model that assumes changes in friction are influenced by both an altered electron density at the interface and the promotion of charge transfer between the sliding surfaces due to the electric field. Similar to the present work, these changes are invoked in the context of the PT model, with both the electron density redistribution and charge transfer leading to an enhanced energy barrier for sliding. Our stick slip measurements do suggest a change of the potential energy landscape when charge transfer occurs, but unfortunately the current across the sliding interface cannot be measured in situ due to the electronically floating semiconducting (Si) tip. So, while we cannot definitively say this for our system, another potential explanation is that the measured excess friction is also influenced by the magnitude and rate of charge transferred between the tip and graphene, which change depending on the tip material, graphene’s doping state, and the quantity of induced trapped charges at the interface. The Si tips are more sensitive to these factors than the other two tip types representing the extreme possibilities of charge transfer, and charge transfer can be encouraged, discouraged, and rate-controlled depending on the charge density in graphene and the material properties of the countersurface, an AFM tip in our case. The interplay between triboelectric currents and the field effect, particularly in semiconducting contacts, thus presents a route for further experimental investigation into electronic contributions to nanoscale friction.Looking at electronic effects of a slightly larger scale, recent work also performed using AFM has associated increased friction with large tribocurrents across the sliding interface of chemically modified semiconductorsFrom a device and application perspective, the use of electric field in comparison to electric bias eliminates the need for any direct current flow between the two contacting surfaces and allows energy-efficient operation of friction tuning. Furthermore, our results with graphene also open up opportunities for tunable friction in 2D semiconductor materials-based FET devices where distinct on/off states can be achieved with single polarity charge carriers.2, Sigma Aldrich) on 35 μm-thick copper foil at 6000 rpm for 30 seconds, followed by baking on a hot plate at 110 °C for 2 minutes. Backside graphene was removed by oxygen plasma treatment (500 W for 10 seconds). Then, PMMA-coated graphene/Cu was floated on a sodium persulfate solution  for 3 hours to completely etch away copper film. The PMMA/graphene was rinsed by immersing into a DI water bath several times and transferred to target substrates, including 300 nm-thick SiO2/Si wafers (UniversityWafer) or metal-coated silicon wafers . After overnight drying, the PMMA layer was removed by soaking in acetone bath. Graphene was then annealed in a quartz tube furnace at 350 °C for 4 hours under Ar/H2 flowing atmosphere to enhance graphene/substrate adhesion and to eliminate polymer residue on graphene surface. After graphene annealing, source/drain electrodes were prepared using silver paste on both sides of the graphene channel. The fabricated graphene FETs were stored in a vacuum desiccator to prevent degradation and adsorption of water and airborne contamination. The quality of the graphene FET device was confirmed by Raman spectroscopy  using a 532 nm excitation laser and 2400 l/mm grating. For the electrical characterization, we used a 2-channel sourcemeter . Constant source-drain bias was maintained at 50 mV (for continuous sweeping) or 1 mV (for discrete sweeps), while the gate bias was swept from 0 V to 30 V (in continuous sweeps) or to 100 V (discrete sweeps). The effect of different atmospheres (dry nitrogen and air) on I-V curves of the graphene FET devices was separately examined and compared to vacuum conditions using a vacuum-controlled probe station .Graphene FET devices were fabricated by transferring chemical vapor deposition (CVD)-grown graphene to the target substrate. Poly(methyl methacrylate)  was spin-coated on graphene (grain size ~5 μm40. Three different tip categories were used in AFM measurements. The first is referred to as Si (unmodified) tips. These are sharp tips as-provided by the manufacturer . The tips are made of n-doped silicon with a typical normal spring constant of ~0.3 N/m and nominal tip radius of 8 nm. The second category of tips is referred to as conducting tips. These are Si tips coated with 5 nm Cr followed by 50 nm Au using an e-beam evaporator . The third tip type is referenced as insulating tip. These are either colloidal silica microspheres  with nominal radius of 5 µm that have been manually glued to tipless cantilevers  or Si tips that were thermally annealed in ambient atmosphere to induce the growth of a thick oxide layer. There was no significant difference between the AFM results conducted with these two types of insulating tips . All measurements were performed in a constantly refreshed dry nitrogen atmosphere  in order to exclude the effects of air humidity and water adsorption. AFM tips had a nominal normal spring constant of 0.3 N/m. Normal spring constants for each cantilever were measured using the Sader’s method42. Friction is averaged along each scan line. Ten scan lines are taken at the same conditions and each single data point results from the average of the ten scan lines. The lateral spring constant is estimated using the thermal noise method43.Friction measurements were performed as a function of the applied load at constant sliding velocity of 1 µm/s and as a function of sliding velocity at constant load (5 or 10 nN), while the backgate bias was maintained constant at a selected value. The gate bias was varied in steps of 10 V from 0 to 100 V, first increasing and then decreasing. All friction scans were performed along a 100 nm line on a flat area of the graphene free from visible contamination. The friction force was calculated from the lateral deflection signal during sliding. A single scan line consists of a trace and retrace line, and friction is calculated by subtracting the retrace from the trace lateral deflection and dividing by two40. The adhesion between tip and graphene is given by the measured pull-off force (the difference between the out-of-contact baseline and the lowest point on the retraction curve), and the values and error shown are the average and standard deviation for five repeated measurements at each held backgate voltage. Adhesion measurements were performed across multiple tips, samples, and locations on samples to ensure qualitative and quantitative reproducibility.The measured vertical deflection of the cantilever at a separation from the surface of 3.5 µm yields the non-contact interaction between tip and surface, and as described in Results is of electrostatic origin. The vertical deflection is recorded before the power source applying the gate voltage is turned on. The deflection towards the surface from this baseline is considered a positive value for the electrostatic attraction. Each value is the average of approximately five seconds of measurement of deflection, and the small error bars are the standard deviation over these 5 seconds  images of an unused tips and of the same tip used only for initial calibration procedures (approach to surface in nitrogen atmosphere and ~5 force curves at a 20 nN or less setpoint) demonstrated that this tip damage, indeed, happened during calibration. Supplementary Fig. The wear of both the surface and AFM tips needs to be avoided and/or accounted for to ensure reliable analysis of the measured friction. To exclude the influence of surface wear on AFM results, the investigated areas were imaged both before and after sliding friction; any measurements with changes after measurement in topography, adhesion (images collected in QI mode), or lateral deflection (in contact mode) were not considered. Tip wear (and/or breakage) is also of concern, particularly for sharp Si tips, as unexpected changes in tip geometry and contact area can have noticeable effects on tip-sample interactions. An increase in radius has been previously reported for sharp AFM tips2 films leading to RMS roughness ~200–300 pm and peak-to-valley roughness of 1–2 nm for most 100 nm2 areas, which may interfere with the measurement of the atomic stick slip. Hence, graphene/hBN/SiO2 FET devices were fabricated to provide smoother surfaces  for stick-slip measurements. First exfoliated hexagonal boron nitride (HQ Graphene) flakes were exfoliated on a clean SiO2/Si wafer using scotch tape. Thin flakes of hBN were found in optical microscope based on the color contrast to the substrate. Target thickness of hBN ranged from 10 to 30 nm, as measured by AFM . Pre-annealing of exfoliated hBN/SiO2 samples was carried out at 400 °C for 3 hours in 50 sccm Ar flow in order to remove the tape residue and improve the adhesion between hBN and SiO2. Then, CVD-grown graphene was transferred on hBN/SiO2 substrate similar to the macrochannel graphene FET fabrication process, described earlier. The presence of hBN underneath graphene resulted in increased number of nanobubbles/blisters trapped at the interface of graphene and hBN, which was observed by dark-field optical microscopy. Subsequent annealing at 350 °C for 4 hours in Ar/H2 atmosphere  substantially reduced the density of trapped bubbles in graphene/hBN. Because of the limited areal fraction of hBN flakes to SiO2 substrate, no substantial changes in charge transport characteristics were observed compared to graphene/SiO2 FET devices.Graphene conforms to SiOStick-slip data shown in the main text were collected using the same JPK AFM previously described, while the data shown in the SI were collected using an Asylum Cypher system  in lateral force mode, both with purged nitrogen atmosphere. All experimental conditions  were the same as previously described. The same unmodified sharp tips as previously described were also used for all stick-slip measurements. New tips were used for every new measurement attempt to ensure a radius of 10-20 nm. Suitable hBN flakes were found using each system’s optical microscope, followed by minimal AFM imaging of the graphene on hBN  to find suitably flat and clean areas. For these measurements in both systems, a 10 nN load was applied while the tip was dragged over a 100 nm scan line at 20 nm/s for 3–5 repeated scans. Stick-slip was found to be more reliably measured at this scan speed using relatively low gain values, the software default for both instruments.Supplementary InformationPeer Review FileSource Data


Patient education has been transformed using digital media and online repositories which disseminate information with greater efficiency. In dermatology, this transformation has allowed for patients to gain education on common cutaneous conditions and improve health literacy. Xanthelasma palpebrarum is one of the most common cutaneous conditions, yet there is a poor understanding of how digital materials affect health literacy on this condition. Our study aimed to address this paucity of literature utilizing Brief DISCERN, Rothwell’s Classification of Questions, and six readability calculations. The findings of this study indicate a poor-quality profile (Brief DISCERN < 16) regarding digital materials and readability scores which do not meet grade-level recommendations in the United States. This indicates a need to improve the current body of educational materials used by clinicians for diagnosing and managing xanthelasma palpebrarum. The utilization of digital applications in healthcare serves an imperative role in the physician–patient relationship . Mobile Xanthomas are common well-circumscribed deposits of lipids that may be found in skin, tendons, or fasciae . They prThis study did not require Institutional Review Board approval as all data utilized in this study is available for public use and did not involve human or animal subjects.Inclusion criteria for extracted articles required them to provide pertinent information regarding xanthelasma palpebrarum. This was determined by the screeners after a joint education session on medically accurate and relevant information for xanthelasma. Additionally, articles were evaluated for the following: (1) Written in the English language; (2) Contained over 200 words; (3) Content was publicly available without the need for a content subscription; (4) Content was pertinent to providing information on the search query of interest. Articles were excluded if the associated access link was nonfunctional, a duplicate of a previous link, or did not meet further inclusion criteria .Additionally, frequently asked questions and associated online articles regarding xanthelasma palpebrarum were investigated using publicly available data from May 2023 using the Google RankBrain  machine learning algorithm after the application of inclusion and exclusion criteria. This algorithm was specifically utilized via the search engine function of Google ,17. The ® .After questions and articles were extracted, two independent raters evaluated questions for Rothwell’s Classification of Questions. Regarding extracted articles, their content was further reformatted into plain text on Microsoft Word ,21,22,23n = 24). Upon subclassification, questions regarding “Technical Details” were most common at 70.0% (n = 21) followed by questions regarding cost at 6.7% (n = 2) and timeline of recovery at 3.3% (n = 1). Following this category, questions were next classified as “Policy” at 13.3% (n = 4). Upon subclassification of the “Policy” category, all questions pertained to the risks and complications of xanthelasma palpebrarum. The least common classification of questions was “Value” at 6.7% (n = 2). Upon subclassification of the “Value” category, questions regarding “Evaluation” and “Pain” were found at 3.3% each . The source of questions and articles was most commonly a “Commercial” source at 40% (n = 12). This was followed by the source “Medical Practice” at 26.7% (n = 8), then “Government Website” at 20.0% (n = 6). Furthermore, sources from “Academic Institution” and “Media Outlet” were the least common at 6.7% each   . The intp = 0.17).Regarding assessment of the quality of websites, the Brief DISCERN instrument implemented a maximum score of 30, and the average Brief DISCERN of this analysis was 9.3  . The graThis study aimed to describe the content, readability, and quality of publicly available digital educational resources on xanthelasma palpebrarum ,27. UtilAdditionally, the findings of our readability calculations provide further explanation for the findings of Rothwell’s Classification of Questions in this study. The use of Rothwell’s Classification of Questions was implemented to categorize the frequently asked questions regarding xanthelasma palpebrarum. Specifically, this tool served to understand the specific components of xanthelasma palpebrae that patients’ inquiries focused on. Rothwell’s classification was implemented in previous literature which used internet-based public resources in clinical medicine, but to the best of our knowledge, this has not been utilized for xanthelasma palpebrae before ,38. The Brief DISCERN was implemented as the third tool  to address the aim of this study. Specifically, this tool has been validated to serve as a threshold marker for quality, evidence-based information. However, this dataset of educational materials of xanthelasma palpebrarum was classified as low quality for most resources, regardless of source . Only two resources were classified as good quality. Specifically, the Brief DISCERN tools observe deficits in the clarity of references used, addressal of the associated question, and sources of support. This tool categorizes these materials to be of low quality, in addition to the low readability found in this study ,42,43,44To our knowledge, this was the first cross-sectional study that analyzed the quality, readability, and content of patient educational materials of xanthelasma palpebrarum. Given this study is the first in addressing this aim, it is not without its deficits that can be addressed in future studies. For example, this study implemented the use of the RankBrain algorithm. This tool has been utilized in previous literature, and Google’s search engine share of the internet is greater than other engines by over 90% ,46. HoweXanthelasma palpebrarum may indicate underlying chronic diseases which significantly contribute to general morbidity and mortality, such as dyslipidemia. Patients with xanthelasma are turning to the internet for education on this condition. This study implemented tools established in the literature to profile the quality, readability, and content of patient educational materials on xanthelasma palpebrarum. The overall profile of these materials is poor and requires dissemination of higher-quality materials. This finding is important for clinicians when aiming to educate their patients on this condition. Moreover, we recommend that clinicians involved in the diagnosis and management of xanthelasma palpebrarum educate their own patients directly in the clinical setting to circumvent the currently poor profile of online educational materials. Thereby, patients may rely on their providers for healthcare literacy as opposed to unreliable online sources. Furthermore, we aim to repeat this analysis for another serious or high-burden disease.