Airlines’ fleet assignment heavily affects the economic and ecological performance of the global air transportation system (ATS). Consequently, it is inevitable to include potential changes of the fleet assignment when modeling and assessing future global ATS scenarios. Therefore, this article presents a novel explanatory approach to modeling the fleet assignment in the global ATS. The presented approach is based on formulating and solving an optimization problem, which describes the fleet assignment in the ATS through a suitable combination of objective function and constraints. While the objective function combines both the airline and the passenger perspective on the fleet assignment, the constraints include additional operational and technological aspects. In comparison to the available global fleet assignment models in the literature, which rely on statistical approaches, the advantages of the presented approach via an optimization problem lie in the overall scenario capability and the consideration of explicit aircraft types instead of simplifying seat categories. To calibrate and validate our model, we use 10 years of historic flight schedule data. The results underline the strengths and weaknesses of the presented approach and indicate potential for future improvement. Despite the currently ongoing COVID-19 pandemic, the aviation industry is expecting long-term growth of passenger air traffic. Different industry forecasts anticipate reaching pre-pandemic revenue passenger kilometers (RPKs) between 2023 and 2027. For the time span of 2018–2050, the average annual RPK growth rate is estimated to lie between 2.9% and 4.2%; only marginally below pre-pandemic growth estimations. [Contrastingly, the aviation industry and political institutions are aiming to reduce emissions and climate impact of air traffic to accomplish the objectives set by the Paris Agreement –8. HenceHowever, as with all changes to the ATS, airlines are expected to adopt their operations to defend and strengthen their market positioning, when facing potential mitigation measures. One way of adopting an airline’s operations is by updating the fleet and the fleet assignment, deciding which aircraft type is operated on which route. Together with the finite number of airlines operating in the ATS, these individual airline fleet assignment decisions aggregate into changes of the overall fleet assignment in the global ATS. Because these changes of the overall fleet assignment affect the economic and ecological performance of the global ATS, e.g., by changing the location and quantity of emissions or by changing the number of departures and arrivals at airports, it is, thus, inevitable to include potential changes of the fleet assignment when modeling and assessing future global ATS scenarios.Available literature on modeling airlines’ fleet assignments can broadly be categorized into approaches for individual airlines, for multiple competing airlines and for the global fleet assignment. Fleet assignment models for individual airlines strive, e.g., for minimal operating cost or maximal operating profit, while considering operational constraints like limited fleet sizes and airport slot restrictions. Methodically, mathematical optimization approaches like linear programming or mixed-integer programming are used. The early work of Abara  and HaneFleet assignment models for multiple competing airlines focus on capturing the competitive nature of flight scheduling, fleet assignment and flight frequencies when multiple airlines operate within the same market. Methodically, game theory approaches are applied, in which the competition is portrayed as a non-cooperative game between multiple airlines, each trying to maximize its operational profit or market share. While the early work of Hansen  as well Global fleet assignment models are sparsely available in the literature and mainly focus on statistic modeling approaches.The Aviation Integrated Model (AIM) –27 utiliKölker et al.  also useThe dependency between aircraft size, flight distance, and passenger volume on a route is also employed by the Future Air Traffic Estimator (FATE) model . UnfortuSimilarly, the publications about how the fleet assignment in the global ATS is modeled within the Aviation environmental Portfolio Management Tool (APMT) , 31 and In this article, we present a new explanatory approach to modeling the fleet assignment in the global ATS. Similar to the previously discussed approaches for modeling the fleet assignment of an individual airline, our approach is based on formulating and solving an optimization problem. However, rather than optimizing the fleet assignment and flight scheduling of an individual airline, we focus on capturing the dynamics of the global, cross-airline fleet assignment in the ATS. Therefore, we apply a different objective function and a different set of constraints. Our objective function includes the airlines’ and passengers’ perspectives, via direct operating cost (DOC), schedule delay and travel time. Our constraints represent logical, operational, and technological aspects of the fleet assignment, like limited fleet availability, airport capacities, and payload-range capabilities of the different aircraft types. Effects of flight scheduling and airline competition, which are key elements in fleet assignment models for individual airlines and multiple competing airlines, are substitutionally included into our optimization problem via a utilization model and market concentration constraints. In doing so, we achieve a suitable level of detail for modeling the global ATS and ease the computational requirements of our optimization model; leading to computation times of less than ten minutes for the yearly fleet assignment of the global ATS. To calibrate and validate the overall model, we use ten years of historic flight schedule data. In contrast to the statistical modeling approaches for the global fleet assignment, which are available in the literature, our new explanatory approach via an optimization problem has the two main advantages of allowing diverse scenario analyses and including explicit aircraft types instead of aircraft seat categories.The remainder of this article is arranged as follows: Section This section presents first the data sets used for this study; second, the developed overall model architecture; and third, the underlying submodels. Last, the mathematical formulation of the model as an optimization problem is introduced.The Sabre Market Intelligence database provides global historic flight schedule data for the years 2010–2019 . This hiThe Cirium Fleets Analyzer database provides information about the global aircraft fleet for the years 2010–2019 . The fleThe available overall data sets regarding route networks and aircraft fleets are split into two subsets. The first, spanning the years 2010–2016, is used for model calibration and submodel development. The second one, spanning the years 2017–2019, is used for model validation.Aircraft type performance data are taken from the Aircraft Characteristic Manuals (ACMs) provided by the aircraft manufacturers , 37 and Data regarding aircraft-type-specific fuel consumption is derived from EUROCONTROL’s Base of Aircraft Data (BADA) data sets .The required airport-specific information consists of IATA codes, geographic coordinates, number of runways, maximum runway lengths, and the runway configurations. The information is collected and aggregated from various publicly available data sources, e.g., Wikipedia and Google’s satellite imagery.While most of the data mentioned above would be available for a wide variety of different aircraft types, this study focuses on a selected group of relevant passenger aircraft types, which are considered to be representative for the global ATS. The selected 35 Airbus and Boeing aircraft types cover 63% of global departures and 91% of the global available seat kilometers (ASKs) in the historic flight schedule data. A complete list of the selected aircraft types is given on the abscissae of Figs. The overall model structure is displayed in Fig. The fleet assignment model for the global ATS is based on the formulation and solving of an optimization problem. The aim of this optimization problem is to determine which proportion of the offered seat capacity on each route is provided by which aircraft type.The objective function of the optimization problem includes both the airline and the passenger perspective, representing the cost and revenue side of aircraft operation, respectively. The costs are included into the objective function via the DOC per ASK. The revenues are modeled indirectly via the schedule delay and the travel time, both affecting the passenger acceptance of a fleet assignment and therefore the revenue on a route. The weighting between the different aspects of the objective function is determined during the model calibration process, minimizing the differences between calibration data and model results.The constraints of the optimization problem consist of logical, operational and technological constraints. Valid solutions of the optimization problem are required to cover the entire seat capacity of each route. Additionally, the limited availability of each aircraft type in the overall aircraft fleet must be considered. Furthermore, the individual relation between payload and range must be met for each aircraft type. From an infrastructure perspective, the available runway length and runway capacity at each airport must be considered. An additional market concentration constraint takes account of the existence of different airlines with individual fleets and route networks.The solution of the optimization problem results in the primary output of the model: the fleet assignment in the global ATS, answering which aircraft type is used to offer which proportion of the seat capacity on which route. This primary output then leads to additional outputs like the route-specific aircraft mix, the route network of each individual aircraft type and the flight frequency on each route.Several submodels are used as part of the overall model structure. In the following these submodels are introduced and briefly described.The DOC submodel uses the TU Berlin method by J. Thorbeck; also known as the CeRAS DOC model. The TU Berlin method describes the DOC as the sum of the five cost components fuel, maintenance, fees, crew and capital. For each component the method provides an analytic formula for calculating the monetary values in 2010 euro values. , 41reduced flight profiles by simulating aircraft trajectories for all discrete combinations of flight distance (in steps of 100 NM) and load factors  for all aircraft models available in BADA 4. For the simulation standard climb and descent procedures are assumed and corresponding speed schedules are used. During cruise, long-range cruise speed is applied and step climbs are modeled to approximate operationally realistic cruise profiles. We compress the trajectory data (= reduction) by saving the relevant aircraft state parameters  at significant profile vertices only, i.e., transitions between flight phases, into the database, as also, e.g., done by Linke et al. [t on the route  between origin airport o and destination airport d via the quadratic expression given by Eq.  is applied , 43. Thie et al. , 46. Simentclass1pt{minima airport .1\documeo, d) with aircraft type t is calculated in dependency of the great circle distance o, d) with aircraft type t is modeled in dependency of the number of available passenger seats per flight o, d) with aircraft type t, including both flights and turnarounds, is modeled in dependency of the block time The utilization submodel describes the yearly service and transport performance of an aircraft unit. The block time the ACMs , 37 and the ACMs , 48, theThe payload-range relation of each aircraft type is directly derived from the ACMs. For each aircraft type a representative weight variant is selected and the boundaries of the payload-range diagram are modeled as straight lines.t with p passengers on route  are modeled with the analytic formulas given in Eqs.  served by t in a time interval p being the available seats per flight. As shown by Eq.  with aircraft type t in comparison to serving it with the fastest available aircraft type The travel time evaluation for the objective function is based on the relative comparison of block times of different feasible fleet assignments. With Eq. , the addThe historic flight schedule data indicate a negative correlation between the offered seat capacity on a route and the market concentration of the used aircraft types. This correlation is assumed to be caused by the existence of different airlines with individual fleets and route networks in the global ATS. Thus, this effect is inherently not included in the presented methodology due to the used cross-airline approach. Therefore, the market concentration submodel calculates an upper limit  see Eq. . The regThe modeled optimization problem is described by the objective function and constraints in Eqs. –16), th, th16), o and destination airport d is operated by aircraft type t.The aim of the optimization model is to determine a global fleet assignment, which minimizes the cost of serving a route and demand network with an aircraft fleet, Eq. . The fleo, d), the fleet assignment must cover at least the required seat capacity, Eq. , the market concentration of the used aircraft types operated on this route, given by the HHI To describe a valid solution of the optimization problem, the fleet assignment must meet the following constraints: For each route in the global route network, given by the directed tuple of origin and destination airport  must allow an operating range Equations  and 18)18) ensurt to provide the required seat capacity o, d) is the weighted sum of the DOC t to provide the required seat capacity o, d), the required seat capacity t to provide the required seat capacity o, d) is calculated by dividing the required operating time for the route by the available operating time per aircraft unit, Eq.  of origin airport o and destination airport d. Of all possible combinations of airports, only those origin and destination airport tuples  with strict positive flight distance and strict positive offered seat capacity OD, Eq.  of potential combinations of routes and aircraft types. Only those routes and aircraft type combinations are considered, for which the set of the possible number of offered passenger seats o, d), which can be served by the aircraft type t, Eq. , Eq. , an, an15), ptimizer  to solveCurrently, the optimization problem does not include any constraints for the flow of aircraft in the route network, e.g., requiring the number of departures and arrivals being equal per aircraft type and airport. This is because the input data for the route network includes significant sources and sinks regarding the offered seat capacity per airport. Therefore, including constraints on the flow of aircraft would require either manipulating the input data or adding the option of additional empty flights to the optimization problem. Both approaches are considered out of scope for this study.Moreover, the optimization problem also does not require the number of flights on a route to be integer, see Eq. . We rateUsing the previously described methodology and data sets to reproduce the fleet assignment in the global ATS for the years 2010–2019 leads to the following results.Figure Figure To quantify and summarize the previously described differences of the fleet assignments for multiple years and evaluation parameters, a normalized first Wasserstein distance (NWSD) approach is used. For each year the data and model distributions of each aircraft type are compared using the Wasserstein distance and normalized with the mean of the input data. The results of the different aircraft types are then ASK-weighted and averaged for each year. Figure Aggregating and comparing the historic flight schedule fleet assignments and model results for routes with similar distance and capacity characteristics leads to the subsequently described results.Figure Figure Similar to the NWSD analysis performed around Fig. Figure The comparison of the model results with the airport capacity submodel constraints indicates that only seven airports (PEK to LGW) are directly capacity constrained. For all other airports in the overall model the airport capacity submodel calculates a higher runway capacity than required. The capacities of four shown airports are overestimated , two are underestimated (PEK and LHR) and the capacities of the other six shown airports are estimated with only minor differences. For the depicted airports, the largest difference between historic flight schedule data and model results, not caused by too restrictive constraints, occurs for Dubai International (DXB).In the following, the previously described results are discussed and interpreted to draw conclusions for the overall model performance and potential future improvements.First, both the aircraft type results Figs.  and 3 anAdditionally, low ASK categories and aircraft types are more sensitive to potential single outliers, caused by either route or airline characteristics. For instance, all B767-400 passenger aircraft included in the historic flight schedule data for 2016 are operated by two North American network carriers; an information surely affecting the resulting operational scenarios of this aircraft type, but unknown to, and out of scope of the model. Nevertheless, future work should include an in-depth analysis of the low ASK differences.Second, the previously presented results show a relatively low accuracy of the overall model regarding the aircraft type capacity share and the aircraft type market concentration on individual routes, see Figs. Third, the exemplary airport results, see Fig. Additionally, the airport results for DXB underline the incapability of the presented modeling approach to reproduce local characteristics. As the hub of the largest A380 operator, Emirates, an accumulation of A380 flight operations occur at DXB. For the depicted year of 2016 a total of 47% of all A380 operations originate from, or terminate at DXB. As the global cross-airline model has no reason to assume such an accumulation of A380 operations in one airport, other smaller aircraft types are used for flights to and from DXB. Thence, the number of flight operations at DXB is overestimated, as seen in Fig. Finally and despite the above mentioned further improvement potential, the overall accuracy of the results shows that an explanatory approach to modeling the fleet assignment in the global ATS via an optimization problem is very much suitable. When reproducing the average flight frequency and aircraft size for different route categories the NMAEs are smaller than 6%. For the aircraft type distance distributions, the NWSDs between model results and historic flight schedule data are smaller than 12%.In comparison to fleet assignment models in the literature for individual airlines or multiple competing airlines, we made additional assumptions and approximations to allow global fleet assignment modeling. The overall accuracy of the results shows that these assumptions and approximations are reasonable, given the global and more aggregated scope within this study.In comparison to other global fleet assignment models available in the literature, e.g., AIM –27 and FWithin this study, the global aircraft fleet and the global route network with its offered passenger seat capacity per route are constant inputs generated from historic data. Thus, the interactions of passenger demand, fleet assignment, and fleet development are not within the scope of this study. However, for other studies, like the introduction of a new aircraft type in the future , this caWhen using the presented model for future scenarios, this is done under the assumption that the calibration of the submodels and objective function with data from the years 2010–2016 remains valid. This implies that the overall structure of the ATS remains comparable to the calibration time span, e.g., regarding the competition of airlines, the number of available aircraft types and the balance of demand and supply. While there currently is no inherent reason to assume differently, a recalibration to newer data might become necessary in the future. In addition, a sensitivity analysis regarding the calibration time span could give insights into the uncertainty for future scenarios.This article presents a new methodology for modeling the fleet assignment in the global ATS. The new methodology is based on an explanatory cross-airline approach; formulating and solving an optimization problem representing the fleet assignment in the global ATS. The objective function of this optimization problem includes the airline perspective via operating cost and the passenger perspective via schedule delay and travel time. The optimization constraints include logical, operational, and technological aspects of the fleet assignment, like payload-range and airport capacity constraints.The presented results demonstrate the overall suitability of this approach, while also indicating improvement potential for future work. When reproducing the fleet assignment of 35 different aircraft types for the years 2010–2019, the results show errors of less than 6% for the average route flight frequency and aircraft size in comparison to historic flight schedule data. Similarly, the distance distributions of individual aircraft types are reproduced with errors of less than 12%. Larger errors occur for the aircraft type market concentration and capacity share.The accuracy of the overall model can most likely be increased by improving the existing objective function and constraints of the optimization problem. Alternatively, new constraints and additional terms to the objective function should be investigated. Additional aircraft types, in particular regional and commuter aircraft, can improve the applicability of the presented model. Moreover, future work should focus on the application of the presented model. Potential research questions include modeling and analyzing the fleet assignment for future global ATS scenarios as well as deriving operational scenarios for future (green aviation) aircraft designs.


Idiopathic pulmonary fibrosis (IPF) is a serious disease of the lung parenchyma. It has a known polygenetic risk, with at least seventeen regions of the genome implicated to date. Growing evidence suggests linked multimorbidity of IPF with neurodegenerative or affective disorders. However, no study so far has explicitly explored links between IPF, associated genetic risk profiles, and specific brain features.We exploited imaging and genetic data from more than 32,000 participants available through the UK Biobank population-level resource to explore links between IPF genetic risk and imaging-derived brain endophenotypes. We performed a brain-wide imaging-genetics association study between the presence of 17 known IPF risk variants and 1248 multi-modal imaging-derived features, which characterise brain structure and function.17q21.31) and 8 (DEPTOR). Through co-localisation analysis, we confirmed that cortical thickness in the anterior cingulate and more widespread white matter microstructure changes share a single causal variant with IPF at the chromosome 8 locus. Post-hoc preliminary analysis suggested that forced vital capacity may partially mediate the association between the DEPTOR variant and white matter microstructure, but not between the DEPTOR risk variant and cortical thickness.We identified strong associations between cortical morphological features, white matter microstructure and IPF risk loci in chromosomes 17 (DEPTOR expression, unrelated to lung function, and more widespread microstructural white matter changes consistent with hypoxia or neuroinflammation with potential mediation by lung function.Our results reveal the associations between IPF genetic risk and differences in brain structure, for both cortex and white matter. Differences in tissue-specific imaging signatures suggest distinct underlying mechanisms with focal cortical thinning in regions with known high This study was supported by the NIHR Nottingham Biomedical Research Centre and the UK Medical Research Council. Idiopathic pulmonary fibrosis (IPF) is a condition in which the lungs become scarred (fibrosed). Although IPF primarily affects the lungs, co-occurrence with impaired brain function, such as cognitive decline, increased risk of neurodegenerative disorders, cerebrovascular accidents, depression, and anxiety have been reported. The nature of this association is unclear and no post-mortem or in-vivo investigations have been performed to explore this directly. At the genetic level, 17 regions of the genome have been found to drive the genetic risk for IPF.DEPTOR gene locus on chromosome 8, was associated with focal cingulate cortical thinning and more widespread changes in white matter microstructure. The cortical association signature was observed in regions with known DEPTOR expression. The white matter findings may be mediated by forced vital capacity (measure of impaired lung function), hinting to distinct direct (cingulate) and indirect  brain effects of DEPTOR risk alleles.Using previously identified IPF genetic variants and neuroimaging-derived features from 32,431 participants available through the UK Biobank, we performed a brain-wide association study for IPF risk variants. We identified brain endophenotypic associations with two IPF risk associated genetic variants, one in chromosome 17 and one in chromosome 8. In particular, the presence of the IPF risk variant at the The reported brain-wide IPF risk association patterns reveal two potential mechanisms that may explain the known association of IPF and brain disorders: (i) direct and focal, thought to be related to paralimbic mTOR dysregulation and (ii) indirect and widespread, in keeping with secondary effects from impaired lung function, such as hypoxia. Taken together, these data support the hypothesis that genetic risk profiles may explain some of the observed comorbidity of IPF and brain disorders.Idiopathic pulmonary fibrosis (IPF) is a serious chronic lung disease of unknown aetiology affecting around five million people worldwide.,,Although the pathogenesis of IPF is incompletely understood, there is a growing body of evidence that multiple risk factors including ageing, genetic alterations, and environmental factors, such as cigarette smoke exposure, contribute to disease risk. Genome-wide association studies (GWAS) have identified a number of loci associated with increased genetic risk in IPF.,,There has been scattered evidence in the literature for brain-lung relationships, across the spectrum of respiratory disorders.,Neuroimaging can enable unique insight into the nature of the association between IPF and brain dysfunction due to its remarkable sensitivity to subtle structural and functional brain changes. It can reveal topographical and tissue-specific brain signatures linked to genetic risks of primary brain disorders or comorbid brain health in systemic disease. Brain imaging has been successfully used to provide intermediate phenotypes (sometimes referred to as endophenotypes) to assist GWAS studies in brain disorders,In this study we used a brain-wide exploratory approach, capitalising on population-level datasets available through the UK Biobank.,a priori selection criteria. We considered participants that had: i) Neuroimaging (magnetic resonance imaging—MRI) and genetic data; Based on the UK Biobank data released in April 2020, only 41,984 subjects had brain imaging data. ii) Good-quality anatomical MRI (T1-weighted) data; We excluded 2289 subjects because of poor structural imaging data .Neuroimaging and genetic datasets from the population-level UK Biobank resource were used.,1-weighted, T2-weighted, and susceptibility-weighted imaging (SWI)) that provides anatomical and morphological information; ii) Diffusion MRI (dMRI) that reflects white matter microstructural complexity and integrity derived from two models, the diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI); and iii) Functional MRI (fMRI), which indirectly reflects neural activity and cortical connectivity during task-free (resting-state) and task-based conditions. To minimise redundancy in the original set of IDPs, we considered a reduced set of N = 1248 IDPs for each participant, representing all the above modalities and features, but minimising overlap   extracted from all available imaging modalities using a standardised processing pipeline,,As imaging-derived features can be affected by non-biological parameters, we de-confounded them following the recommendations in.,,As suggested in,,−8) with increased genetic risk in IPF .k in IPF , from a We tested the association between each of the 17 IPF variants and each of the 1248 IDPs across 32,431 individuals. For each IPF variant, separate analyses were performed using a general linear model  Body measurements. We used standing height (Data-Field 50), and weight (Data-Field 21002) as covariates. Any missing value in height and weight data was substituted by the imputation  data in the BWAS model to identify brain-wide Imaging-Genetics associations. As suggested in,The BWAS covariates were de-meaned and unit-variance normalised. In total, 25 variables were used as covariates in the BWAS models . When muFor the genetic variants that were found to be associated with brain IDPs, we performed co-localisation analysis using the COLOC methodX), a single mediator (FVC M), and a single dependent variable (brain IDPs Y). This aimed to quantify the direct and indirect relationships between IPF risks, IDPs, and FVC. The direct and indirect standardized effects were calculated using multivariate-adjusted linear regression analysis and a Sobel testTo explore whether associations between neuroimaging features (IDPs) and presence of IPF risk genetic variants, as derived from the BWAS regression model, were indirect effects from subclinical IPF risk manifestation, we undertook mediation analysis focusing on basic lung function that could plausibly affect brain health. We used forced vital capacity , a cumulative measure of lung function ,The statistical software, threshold, sample size, inclusion/exclusion criteria, and methods of each analysis are described in the sections above. All the analysis was conducted using PLINK v2.0,http://www.ukbiobank.ac.uk/ethics/), and these ethical regulations cover the work in this study. Written informed consent was obtained from all participants.No new data were collected from this study. We used publicly-available data from the UK Biobank (under Project 43822). The UK Biobank has approval from the North West Multi-centre Research Ethics Committee (MREC) to obtain and disseminate data and samples from the participants —17q21.31 in chromosome 17) were found to be significantly associated with brain IDPs. The number of associations reaching the Bonferroni-corrected threshold for statistical significance  were 19 and 174 for the chromosome 8 and chromosome 17 IPF variants, respectively  .Fig. 3ThDEPTOR IPF variant that survived Bonferroni correction, along with the sign and magnitude of the association (t-stat). Cortical thickness in the caudal anterior cingulate was negatively associated with IPF risk (lower thickness ↔ presence of IPF risk variant in chromosome 8). A number of white matter (WM) IDPs  and the NODDI intracellular volume fraction (ICVF), and radial diffusivity indices) of major fibre bundles  were also associated with the DEPTOR IPF variant. These IDPs represent WM integrity and microstructure within these bundles. ICVF and FA correlated positively with the presence of IPF risk variant  variant included indices of white matter integrity and microstructure , the NODDI ICVF, and mean diffusivity (MD)), and cortical morphological measures  volumes), with the most significant association corresponding to “cortical surface area of fusiform—right hemisphere (RH)”. No functional measures associated with this IPF variant either. Presence of IPF risk variant in chromosome 17 was associated with increased cortical thinning in certain areas , with lower diffusivities in white matter   variant, where 174 associations with brain IDPs were found . The mode matter , and higl areas) .To explore whether the association of IPF genetic risk with the brain IDPs was likely due to the same causal variant, we performed co-localisation analysis for the IDPs that were found to be significantly associated with IPF variants. Seventeen of the nineteen significant IDP associations for the chromosome 8 IPF locus showed statistical evidence through co-localisation of a likely shared causal variant with the IPF association (posterior ≥ 0.8DEPTOR (chromosome 8) IPF variant and 17 brain IDPs, representing cortical morphology and white matter microstructure. We kept these 17 IDPs for subsequent analysis, indicated by the triangles with bold outlines in Taken together, the above results provide strong evidence—through complementary analyses—of a potentially causal association between the To explore potential mechanisms underlying the identified associations, we conducted a mediation analysis. Given that IPF is a restrictive lung disease characterised by impaired lung capacity, we used forced vital capacity (FVC), which is used for IPF diagnosis and characterisation,DEPTOR IPF variant and the anterior cingulate cortical thickness was not mediated by lung function, as captured by FVC  databaseDEPTOR IPF variant, has a relatively high expression level for the DEPTOR gene. That provides evidence of biological plausibility that the observed association between IPF risk gene variant and IDP changes was detected in a cortical region where the risk gene is indeed strongly expressed.The lack of any mediation through FVC in the case of anterior caudate cortical thickness can hint to a likely direct gene effect that is limited to the anterior caudal cingulate cortex. To corroborate the likely direct nature of this effect, we computed a bulk gene expression map of the DEPTOR variant associations by lung function in keeping with an indirect effect such as systemic hypoxia or inflammation. Conversely, the anterior cingulate thinning was independent of lung function suggesting a direct effect from DEPTOR variant expression in view of the known strong DEPTOR gene expression in the anterior cingulate cortex.We undertook a large brain-wide multi-modal imaging and genetic association study in 32,000 participants of the UK Biobank to establish brain endophenotypes of IPF genetic susceptibility. Strong associations with a likely shared causal variant were found for several of the 1248 studied brain imaging-derived phenotypes (IDPs) for two of the 17 IPF risk associated variants on chromosomes 8 and 17. Co-localisation provided additional evidence that the associated neuroimaging features and IPF share a single causal variant at the chromosome 8 locus. The association pattern consists of focal cingulate cortical thinning and more widespread microstructural alterations in major white matter tracts, such as the corpus callosum and corona radiata. Furthermore, we suggest potential partial mediation of the subcortical IDP–DEPTOR in a very large blood eQTL dataset, a large lung eQTL dataset, and multiple tissues in Genotype-Tissue Expression (GTEx) (including the lung). In addition, it has been also shown that the RP11-760H22.2, TAF2, and KB-1471A8.1 co-localised with the IPF risk signal in GTEx (but not the other larger eQTL databases). Although we would never be able to completely rule out the variants acting through another gene, given the variants were located in DEPTOR, DEPTOR had the strongest evidence for gene expression in relevant tissue and had strong biological supporting evidence,DEPTOR was the most likely gene of interest and was selected as the IPF variant in this locus.Our study builds upon our previous workDEPTOR in chromosome 8 encodes for the Dishevelled, Egl-10 and Pleckstrin (DEP) domain-containing mechanistic target of rapamycin [mTOR]-interacting protein, which is a key modulator  of the mTOR pathways interacting with both of its complexes (mTORC1 and mTORC2),DEPTOR lead to decreased mTOR activity.DEPTOR, major roles have already been established in cancer, metabolism, and immunity explained by its ubiquitous tissue expression and modulation of fundamental cellular processes. In IPF, the recently identified DEPTOR IPF risk allele together with decreased gene expression in lung tissueβ1-mTORC1 axis may hold considerable promise as an anti-fibrotic strategy with the potential to impact multiple IPF pathomechanisms.DEPTOR inhibition was also found in brain tissue in Alzheimer's DiseaseDEPTOR/mTOR ratio is considered to regulate the neuroprotection/neurodegeneration balance in pro-inflammatory states via autophagy regulation. Moreover, DEPTOR central nervous system (CNS) expression in the hypothalamus, circumventricular organs, and autonomic nervous regions support increasing evidence for a further critical role in brain-body homeostatic control.DEPTOR dysregulation through the IPF risk variant is linked with cortical thinning in the anterior cingulate and microstructural changes of major white matter tracts. Moreover, our mediation analysis hints to potential direct DEPTOR and indirect lung phenotypic effects using forced vital capacity (FVC), as an established lung function marker in IPF.DEPTOR bulk gene expression in the Allen Human Brain Atlas.,DEPTOR variant will also reduce DEPTOR expression in areas of high expression, such as the anterior cingulate, resulting in the observed cortical thinning. In turn, the affection of the anterior cingulate may well explain the development of neuropsychiatric comorbidities as evidenced by a large body of neuroimaging studies.Our analysis provides exciting insights that https://open.win.ox.ac.uk/ukbiobank/big40/pheweb33k/variant/8:120934126-A-G). Taken together, hypoxia and chronic neuroinflammation are plausible mechanisms for the observed pattern of subcortical changes in diffusivity.In addition, we propose a second indirect mechanism for the associations found between the chromosome 8 IPF risk variant and more widespread changes in major white matter tracts with potential mediation through FVC. Even if the mediation results were weak, the plausibility of such mechanism cannot be ruled out. Particularly, when considering that the specific characteristics of these imaging associations point to a neuroinflammatory signature that may relate to chronic systemic pro-inflammatory state or tissue hypoxia. High ICVF and reduced diffusivity measures that were correlated with the presence of IPF variant in chromosome 8 correspond to findings in acute neuroinflammation and injuries,DEPTOR risk variant and also overlaps spatially suggesting a similar indirect pathomechanism warranting further mechanistic and mediation studies. Interestingly, the cortical chromosome 17 risk variant signature showed a distinct spatial profile and displayed a dichotomy with cortical thinning but increased cortical surface area. Such a pattern has been reported in a GWAS study, which confirmed the ontogenic dichotomy between thickness and surface area.DEPTOR risk variant). Direct comparisons between our findings and the ones reported inWe detected conspicuously strong effects of IPF genetic risk on brain structure for chromosome 17 that are noteworthy, despite the inconclusive co-localisation analysis. The subcortical endophenotypic pattern of the chromosome 17 IPF risk variant was broadly similar to the microstructural changes seen for the ,,Our results showed no association between IPF genetic risk variants and functional imaging features . This is not surprising and agrees with previous studies,DEPTOR and showed co-localisation with IPF), meaning that the effective degrees of freedom are much lower than 17 .A limitation of our study is that the potential partial mediations did not survive Bonferroni multiple comparison correction, they were only nominally significant. In the mediation analysis, we used an absolute FVC measure (in litres) and accounted for age, sex, height, and ethnicity (our study cohort is restricted to white European ethnic background). We found strong covariance structure between the 17 IDPs used  and chromosome 17 (17q21.31). Through co-localisation a shared causal gene locus was identified for the DEPTOR variant and associated brain signature, and for cingulate cortical thinning no mediation effect from lung function was found. Taken together, these data support the hypothesis that genetic risk profiles may explain some of the observed comorbidity of IPF and brain disorders with further mechanistic studies warranted to characterise additional indirect effects.In summary, we exploited imaging and genetic data from more than 32,000 participants available through the UK Biobank population-level resource to explore links between IPF genetic risk and imaging-derived brain endophenotypes. We identified strong associations between cortical thickness (in the anterior cingulate) and white matter microstructure  with IPF risk loci in chromosome 8 . Our study was performed under UK Biobank Project 43822.All data used in this study are publicly available from the UK Biobank (github.com/Alirezamnk/BWAS_IPF) at the time of publication.Analysis code will be available on GitHub (RGJ reports research funding from AstraZeneca, Biogen, Galecto, GlaxoSmithKline, RedX, Pliant, and Genetech; personal fees from Bristol Myers Squibb, Daewoong, Veracyte, Resolution Therapeutics, RedX, Pliant, Chiesi, Roche, PatientMPower, AstraZeneca, GSK, Boehringer Ingelheim, Galapagos, and Vicore; non-financial support from NuMedii and Action for Pulmonary Fibrosis, outside the submitted work. LVW reports research funding from GSK and Orion Pharma and have conducted consultancy for Galapagos. DPA reports research funding from Biogen. The other authors have no conflict of interest to disclose.


However, whether and how PD-L1+ TEVs mediate αPD-L1 therapy resistance is unknown. Here, we show that PD-L1+ TEVs substantially decoy αPD-L1 and that TEV-bound αPD-L1 is more rapidly cleared by macrophages, causing insufficient blockade of tumor PD-L1 and subsequent αPD-L1 therapy resistance. Inhibition of endogenous production of TEVs by Rab27a or Coro1a knockout reverses αPD-L1 therapy resistance. Either an increased αPD-L1 dose or macrophage depletion mediated by the clinical drug pexidartinib abolishes αPD-L1 therapy resistance. Moreover, in the treatment cycle with the same total treatment dose of αPD-L1, high-dose and low-frequency treatment had better antitumor effects than low-dose and high-frequency treatment, induced stronger antitumor immune memory, and eliminated αPD-L1 therapy resistance. Notably, in humanized immune system mice with human xenograft tumors, both increased αPD-L1 dose and high-dose and low-frequency treatment enhanced the antitumor effects of αPD-L1. Furthermore, increased doses of αPD-L1 and αPD-1 had comparable antitumor effects, but αPD-L1 amplified fewer PD-1+ Treg cells, which are responsible for tumor hyperprogression. Altogether, our results reveal a TEV-mediated mechanism of αPD-L1-specific therapy resistance, thus providing promising strategies to improve αPD-L1 efficacy.PD-L1 The application of immune checkpoint blockade, including anti-PD-1 and anti-PD-L1 antibodies (αPD-1 and αPD-L1), has led to a major revolution in tumor immunotherapy. Although αPD-1 and αPD-L1 show excellent efficacy in various tumor types, even in patients with advanced tumors , only 10+ T cells and facilitates tumor growth [Extracellular vesicles (EVs) are mainly divided into two categories: ectosomes and exosomes. Ectosomes (50–1000 nm in diameter) are vesicles produced by direct outward budding of the plasma membrane. Exosomes (30–150 nm in diameter) are generated from the endosomal pathway. EVs contain large numbers of proteins, nucleic acids, lipids and metabolites from their parent cells and are essential for communication between cells . PD-L1 hr growth . TEV PD-r growth . TEV PD-r growth . These fr growth . SimilarPD-1 has two naturally occurring ligands, PD-L1 and PD-L2, that provide inhibitory signals to T cells via PD-1 . αPD-1 bHere, we found that TEVs can efficiently decoy αPD-L1 via PD-L1. TEV-bound αPD-L1 is more readily phagocytized by macrophages and then more rapidly degraded by lysosomes. In this way, TEVs consume large amounts of αPD-L1, leading to insufficient αPD-L1 to block PD-L1 on tumor cells, thereby mediating αPD-L1 therapy resistance.CV) that occupied all PD-L1 on 1 × 105 MC38 and PC3 cells was approximately 250 ng  did not affect the binding of αPD-L1 and PD-L1 to MC38 cells  was used to block PD-L1 on MC38 cells, the addition of MC38-EVs no longer increased the αPD-L1-free PD-L1 on MC38 cells  and human PC3 prostate cancer cells (PC3-EVs) that have been reported to contain high levels of PD-L1. These EVs showed typical exosome-like morphology  of αPD-L1, respectively. However, EVs from tumor tissues (EVs-TT) of these mice bound approximately 0.95 ± 0.20, 1.69 ± 0.16 and 2.51 ± 0.14 μg  of αPD-L1, respectively, which was remarkably high. In addition, the PD-L1 levels of Circ-EVs were positively correlated with those of EVs-TT  along with the decreased IFN-γ+CD8+ and Ki-67+CD8+ T cells in the TTs of the mice that received MC38-EVs but not MC38 Pdl1−/−-EVs  from mice with 1-, 2- and 4-week MC38 tumors bound approximately 0.04 ± 0.02, 0.12 ± 0.01 and 0.16 ± 0.01 μg  with impaired TEV secretion , which release reduced TEVs [Rab27a−/− tumor-bearing mice, αPD-L1 notably inhibited MC38 Coro1a−/− but not MC38 tumor growth at the same dose  was used, the difference in growth between MC38 tumors and MC38 Rab27a−/− or MC38 Coro1a−/− tumors was completely eliminated , peripheral blood and spleen were observed in the tumor-bearing mice receiving high-dose and low-frequency αPD-L1 treatment , an inhibitor of colony-stimulating factor 1 receptor (CSF-1R), markedly reduced the numbers of peripheral monocytes and liver macrophages  of αPD-L1. We also detected PD-L1 on the EVs-TT of another 3 lung cancer patients  of αPD-L1. Then, we established a PC3 tumor model in nonobese diabetes/severe combined immune deficiency (NOD/SCID) mice, and in these tumor-carrying mice, EVs-TT #3 obviously decreased the binding of αPD-L1 and tumor PD-L1  cells, thereby leading to hyperprogression of cancer [+ Treg cells. We indeed found in the TRAMP-C2 tumor-bearing mice that αPD-1Exe treatment induced more Ki-67+PD-1+ Treg cells than αPD-L1Exe treatment . The mice were housed in a specific pathogen-free facility, and the experimental protocols were approved by the Animal Care and Use Committee of the First Affiliated Hospital of Zhejiang University.C57BL/6 J and NOD/SCID female mice aged 6-8 weeks were purchased from Joint Ventures Sipper BK Experimental Animal Co. . 2.PC3 cells, MC38 cells and TRAMP-C2 cells were purchased from the Chinese Academy of Sciences Institute . PC3, MC38 and TRAMP-C2 cells were cultured in DMEM with 10% exosome-depleted fetal bovine serum (FBS)  and 1% penicillin/streptomycin . PMs were collected 3 days after the intraperitoneal injection of C57BL/6 J mice with thioglycolate . PMs were cultured in RPMI-1640 with 10% FBS and 1% penicillin/streptomycin. All cells were cultured at 37 °C with 5% COg for 10 min, 2000 × g for 20 min and 10,000 × g for 30 min at 4 °C. Then, the MV pellets were resuspended in sterile PBS, and the supernatants were passed through 0.22 μm syringe filters  and collected in 35 ml ultracentrifuge tubes . The EVs were concentrated using ultracentrifugation with a SW32Ti rotor  at 100,000 × g for 70 min at 4 °C. Subsequently, the EV pellets were resuspended in sterile PBS. The protein contents of the EVs were quantified by using a BCA protein assay kit in the absence of detergent (Thermo Fisher Scientific).PC3, MC38 and TRAMP-C2 cells were plated at a density of 3 million cells per 15-cm plate (Corning 430599) and cultured for 48 h, and the media from 10 plates were collected. For PC3-EV, MC38-EV and B16-EV separation, cell culture supernatants were centrifuged at 300 × A total of 5 μg of PC3-EVs or MC38-EVs was diluted in PBS and placed on 200-mesh carbon-coated copper grids at room temperature (RT) for 2 min. The excess suspension was removed using filter paper. Then, the PC3-EVs or MC38-EVs were negatively stained with uranyl acetate at RT for 5 min, washed twice with PBS, dried and examined under an FEI Tecnai T10 EM  operating at 100 kV.g for 10 min. Samples were separated by 10% SDS-polyacrylamide gel electrophoresis and transferred to PVDF membranes (Millipore), which were blocked with 5% milk for 1.5 h, incubated with the corresponding primary antibodies at 4 °C overnight, and then incubated with secondary antibodies at RT for 2 h. An Enhanced Chemiluminescence Kit  was used to detect the bands. The antibodies used and the corresponding dilutions are listed in Supplementary Table Equal amounts of cell or tissue lysate or EV proteins were resuspended in 5 × SDS loading buffer, incubated at 100 °C for 5 min, and centrifuged at 12,000 × The number and size distribution of EVs were analyzed using a NanoSight NS300 . EVs were resuspended in PBS for analysis. For recordings, samples were pumped automatically into a chamber at a constant flow rate using the Malvern NanoSight syringe pump system. The camera level was adjusted to 14, and three 30’ captures per sample were recorded. For analysis of the recordings, the detection threshold was set to 5, and the NTA3.3 Suite Software was used for analysis.g or 3500 × g for 5 min at 4 °C, and then incubated with the corresponding fluorescence-conjugated primary antibodies in 100 μl of PBS at predetermined saturating concentrations for 20 min at RT. After two washes in PBS, the cells or beads were analyzed on an ACEA NovoCyte flow cytometer . For intracellular staining, cells were stimulated with PMA , ionomycin , and brefeldin A solution  at 37 °C for 4 h and then subjected to intracellular staining. The data were analyzed using FlowJo software , and the antibodies used and the corresponding dilutions are listed in Supplementary Table Cells or EVs incubated with 4-μm aldehyde sulfate beads (Thermo Fisher Scientific) were washed in PBS with 1% BSA, collected by centrifugation at 400 × −1 puromycin. Live cells were sorted using a Beckman Coulter DxFLEX flow cytometer (Beckman Coulter). After sorting, single cells were cultured in 96-well plates. The Rab27a or PD-L1 knockout efficiency was confirmed by western blotting or flow cytometry. Selected MC38 cells with unchanged Rab27a or PD-L1 expression were used as controls.For depletion of Rab27a or PD-L1 in MC38 cells, the guide RNA plasmid . Human CD8+ T cells were isolated from PBMCs of healthy donors with a Human CD8+ T-Cell Enrichment Kit (StemCell). A total of 1 × 106 CD8+ T cells were labeled with CFSE (Thermo Fisher Scientific) at 5 μM. The cells were then incubated at 37 °C for 5 min, and the reaction was stopped by adding an equal volume of RPMI-1640 with 10% FBS. Unstimulated CFSE-labeled cells served as a nondividing control. Both mouse and human CD8+ T cells (1 × 106 ml−1) were stimulated with αCD3 and αCD28  for 24 h and then incubated with MC38 and PC3 cells alone (2.5 × 105 ml−1) or MC38 and PC3 cells plus the corresponding TEVs with or without αPD-L1  for 48 h.Mouse CD8+ Treg cells were isolated from splenocytes and peripheral lymph nodes in Foxp3GFP transgenic mice and sorted by a Beckman Coulter DxFLEX flow cytometer (Beckman Coulter). A total of 1 × 106 PD-1+ Treg cells were labeled with 1 μM CellTraceTM Far Red (Thermo Fisher Scientific). Then, the reaction was stopped by adding an equal volume of RPMI-1640 with 10% FBS, and the cells (3 × 105 ml−1) were stimulated with complete RPMI 1640 medium containing 1 ng ml−1 PMA, 200 ng ml−1 ionomycin , and 4000 U ml−1 murine IL-2  in the presence of 5 μg ml−1 recombinant mouse PD-L2 (BioLegend) for 72 h.PD-12SO4, followed by measurement of the absorbance at 450 nm. The concentration of αPD-L1 on the surface of EVs was calculated based on the linear range of the ELISA data. Serial dilutions of biotinylated αPD-L1 (Thermo Fisher Scientific) were used to generate a standard curve. The results of the standard curve demonstrated that the established ELISA exhibited a reliable linear detection range from 3 to 800 ng ml−1.For determination of the αPD-L1-binding ability of EVs, 96-well ELISA plates were coated with αCD63, αCD81 and αCD9 at 4 °C overnight . Free binding sites were blocked with 100 μl of blocking buffer for 1 h at RT. Then, serum samples or EVs-TT (50 μl per sample) were added to duplicate wells, followed by incubation overnight at 4 °C. The plates were washed, and biotinylated αPD-L1 (Thermo Fisher Scientific) or biotin αRat IgG (BioLegend) was added to each well and incubated for 1 h at RT. Then, streptavidin-HRP (BioLegend) diluted in 100 μl of PBS was added and incubated for 1 h at RT. The reaction was developed with TMB and blocked with 2 M HRab27a−/−, MC38 Coro1a−/−, TRAMP-C2 and TRAMP-C2 Rab27a−/− cells (2 × 106) were resuspended in 200 μl of PBS and subcutaneously implanted into the right flank of C57BL/6 female mice on Day 0. PC3 cancer cells (5 × 106) were injected subcutaneously into NOD-SCID mice on Day 0. When tumors reached an average of 100–200 mm3, as calculated with the formula volume = (width2 × length) 2−1, the mice were randomized into different treatment groups. For analysis of the treatment difference between αPDL1 and αPD-1, 10 μg of αPD-1 (BioLegend) or αPD-L1 was injected intravenously into mice every 2 days. For determination of the effect on TEVs binding αPD-L1 in vivo, the mice with TRAMP-C2, TRAMP-C2 Rab27a−/−, MC38, MC38 Rab27a−/− or MC38 Coro1a−/− tumors were intravenously injected with 10 or 30 μg of αPD-L1 every 2 days. For determination of whether TEV-bound αPD-L1 is eliminated by macrophages, the mice with MC38 or MC38 Rab27a−/− tumors were intravenously injected with 10 μg of αPD-L1 with or without intraperitoneal injection of 20 μg of PLX3397  or with or without intravenous injection of 50 μl Clodrosomes  every 2 days when the tumor size reached 100–200 mm3. For the humanized tumor model, NOD-SCID mice were intratumorally injected with preactivated human PBMCs (1 × 106) when the tumor size reached 80–100 mm3. Two days later, the mice were intravenously injected with αPD-L1, with or without 20 μg PC3-EVs. In some experiments, the mice with TRAMP-C2 tumors were intravenously injected with 20 μg of αCTLA-4 (BioLegend) and 30 μg of αPD-L1 or αPD-1 every 2 days when the tumor size reached 80–100 mm3. At the experimental end point, livers, spleens and tumors were excised for subsequent histologic analysis or processed immediately for flow cytometry analyses, and serum was collected for ELISAs.For construction of subcutaneous tumor models, MC38, MC38 Murine tumor tissue sections were routinely deparaffinized and rehydrated, followed by antigen retrieval using 10 mM sodium citrate buffer (pH 6.0). After the samples were blocked with 1× blocking solution at 37 °C for 1 h, they were incubated with mouse αRat IgG2a (BioLegend) and rabbit αPD-L1  overnight at 4 °C. Then, PLA was performed with Duolink In situ reagents  according to the manufacturer’s instructions. Then, the samples were imaged using Olympus FluoView version 1.4a software . Images of cells and sections were acquired, and positively stained areas were analyzed by ImageJ software .αPD-L1 was labeled with Protein Labeling Kits (Thermo Fisher Scientific) according to the manufacturer’s instructions. Then, 10 μg of labeled αPD-L1 or 10 μg of labeled αPD-L1 and 20 μg of TEV mixture were intravenously injected into mice. 12 h later, the mice were sacrificed, and the brain, heart, lungs, liver, spleen, kidneys, gut and tumor were collected. The labeled αPD-L1 was imaged by an IVIS . The background and autofluorescence were defined according to the supernatant negative controls and subtracted from the images using the Image-Math function. In addition, the exposure conditions  were identical for all measurements within each experiment. Total measurements were obtained under the same conditions for all experimental groups.The murine PMs were cultured overnight on glass coverslips and then treated with lysosome inhibitors for 24 h. Then, the cells were coincubated with labeled αPD-L1 or TEV-bound αPD-L1 for another 2 h. After being washed three times with PBS, the cells were fixed with precooled methyl alcohol for 10 min at −20 °C and then permeabilized with 0.1% Triton X-100 for 10 min at RT. After the cells were blocked with 5% BSA and 3% goat serum in PBS, they were incubated with LAMP1 antibodies  overnight at 4 °C in blocking buffer. The following day, after three washes in PBS, the cells were incubated with DyLight 488-labeled secondary antibodies  for 30 min at RT and washed in PBS. Finally, nuclei were stained with DAPI (Thermo Fisher Scientific). Liver and spleen tissues were embedded in Tissue-Tek™ CRYO-O.C.T. (Thermo Fisher Scientific) and processed to obtain 5 μm sections. Then, the tissue sections were stained with mouse F4/80 antibodies (Abcam) at 4 °C overnight followed by staining with DyLight 488-labeled secondary antibodies (Multi Sciences Biotech) for 1 h at 4 °C. The nuclei were stained with DAPI (Thermo Fisher Scientific) for 20 min at RT. The stained sections were imaged using an Olympus IX83-FV3000 confocal microscope (Olympus). Images were analyzed with ImageJ software (NIH).t test was used to compare the differences between two groups, and one-way ANOVA followed by Tukey’s test was used to compare the differences among multiple groups. The Spearman rank-order correlation test was used for correlation analysis. A difference was considered significant if the P value was < 0.05.All statistical analyses were performed using GraphPad Prism 8.0 . All data are expressed as the mean ± s.d. An unpaired two-tailed Student’s Supplementary Information


SO42– anions were found to preferentially accumulate nearthe interface at an open circuit potential (OCP), creating an electricalfield that orients water molecules below the interface, as revealedby the increased intensity of the O–H stretching peak of H-bondedwater. No such increase is observed with NH4Cl at the OCP.The intensity of the dangling O–H bond stretching peak howeverremains largely unchanged. The degree of orientation of the watermolecules as well as the electrical double layer strength increasedfurther when positive voltages are applied. Negative voltages on theother hand produced only small changes in the intensity of the H-bondedwater peaks but affected the intensity and frequency of dangling O–Hbond peaks. The TOC figure is an oversimplified representation ofthe system in this work.The structure of interfacial water near suspended grapheneelectrodesin contact with aqueous solutions of Na To improve our understanding of the electricaldouble layer (EDL) at the graphene–electrolyte interface withoutinterference from the substrate, we used suspended graphene, whichalso separates the solution from the ambient air.The graphene–electrolyte interfaceplays an important rolein many applications and technological fields, such as electrocatalysis,energy storage, water desalination,16 witha specific affinity for cations due to their interactionswith graphene defects and π-orbitals.18 In recent work, an affinity for anions, including OH–, Cl–, and SO42–,has been proposed.21 The ions are the main factors in the formation ofthe EDL, which is strongly influenced by the differential segregationof the electrolyte ions to the interface, driven by various physicaland chemical forces. One arises from the image charge, where the abruptchange of dielectric properties across the solution–electrodeinterface leads to polarization effects that can be described by imagecharges outside the solution. This effect however does not lead todifferential segregation here, as both cations and ions are affectedalike. However, image charge interactions may assist the ion segregationat the interface in the presence of other mechanisms that break thesymmetry between anions and cations. Another is the segregation ofions out of the solution due to the interplay of enthalpic and entropicforces in ion solvation23 and by the differentdisruption of the H-bonding structure of water near ions in the bulkand interface. A third driver is the formation of bonds between ionsand the electrode, of covalent, ionic, or van der Waals character.Finally, external forces such as the applied bias also play a crucialrole. The purpose of this work is to obtain a molecular level understandingof these driving forces and the structure of the electric double layerthey create. As we show below, the combination of Raman spectroscopy,sum frequency vibrational spectroscopy (SFVS), and Kelvin probe forcemicroscopy (KPFM), together with the use of suspended graphene electrodes,provides a unique perspective and insight for such studies.Graphene isnormally assumed to be hydrophobic,2SO4, (NH4)2SO4, and NH4Cl, were chosen becauseof their distinctive adsorption behavior at the air/water interface.24 Through Raman measurements, we found that thedoping of graphene by electrolyte species is very small at bulk concentrationsbelow 10 mM and thus can be ignored. Under an open circuit potentialand for positive voltages, SO42– ionsare the dominant species at the interface. Spectra acquired at biasvoltages on each side of the charge neutral point (CNP), also calledthe point of zero charge, showed an asymmetric change with voltagefor H-bonded bulk water molecules and for molecules with danglingOH groups at the interface. These changes are also evident in KPFMmeasurements of the contact potential difference (CPD) between thetip and graphene. Such results are partially consistent with previousobservations.21 The dangling O–H stretchvibrational peak of water is largely unaffected by changes in concentrationand applied bias, except at voltages below −0.3 V, where itshifts to a lower frequency and gets buried into the peaks of H-bondedwater, implying a change of hydrophilicity at a negatively biasedgraphene electrode.Three salts, Na28 To check the effects of contamination, we prepared graphene in threedifferent ways before transferring to the water surface: (1) ultraviolet(UV) irradiation for 5 min, (2) washing in a mixture of acetone andisopropyl alcohol (IPA) (1:3) for 30 min, and (3) as received, i.e.,no treatment (see Section S3). To assessthe amount of hydrocarbon contamination on graphene, we measured theintensity of the C–C and C–H stretch and CH2 twist peaks with Raman spectroscopy, as described in Section S3. With reference to the peaks froma saturation layer of polyethylene formed by immersing graphene ina 1.6 μM polyethylene/CCl4 solution, we found thatthe level of contamination was 0.05 ML after a UV treatment, 0.13ML after IPA washing, and 0.14 ML in the untreated sample. However,the “cleanliness” of graphene after these treatmentswas found to be short lived. As shown in Section S5, after 30–60 min in air in our laboratory conditionsfollowing the cleaning procedure, the sample became contaminated againto their precleaning level. Therefore, all experiments reported herewere performed on samples with a hydrocarbon coverage of about 0.14ML.It has been reported that graphene may suffer from air-borne hydrocarboncontamination.As shown in –1. This change is the result of the symmetry rules governing the sumfrequency generation (SFG) process, which requires a lack of inversionsymmetry. At the interface, there is an intrinsic lack of inversionsymmetry manifested by the intense peak around 3620 cm–1 due to the dangling O–H stretch vibration mode of the watermolecules at the interface next to graphene, which is not affectedby the salt concentration. However, most of the H-bonded water moleculesbelow the first layer are randomly oriented; i.e., they are symmetricas an ensemble within a volume of wavelength dimensions and thereforecannot generate an SFG output. The orientation ordering is not abruptbut decays rapidly from the interface to the bulk interior. As a result,the O–H stretch vibration peaks of the molecules have smallintensities.The differential segregation of cations and anions near the graphene–waterinterface creates an electric field in the double layer with an intensitythat depends on ion concentration. The field affects the vibrationspectrum of the water molecules and changes the intensity of the peaksin the O–H stretch region, between 3000 and 3600 cmWe should note here that the probing depth of SFGis not determined by the penetration depth of the photons, which ismacroscopic for the visible beam and of micrometers for the IR beam,much larger than the Debye length (of an order of 1 nm), or the Gouy-Chapmanlength (angstroms). Instead, it is determined by the depth of theilluminated region lacking inversion symmetry. The presence of electricfields tends to orient the molecular dipoles along the field direction,i.e., perpendicular to the interface, which break the inversion symmetryand cause the intensity increase observed in the SFVS signal seenin S,eff2 (ω), of interfacial water, which depends on the electric field EDC following the expression:29S(2) denotes the contribution from water molecules right atthe interface, while the integral describes the contribution fromfield-induced polarization of water molecules in the diffuse layer. EDC(z) is the distance-dependentfield along the surface normal, χB(3) isthe third order nonlinear susceptibility of bulk water, and Δkz is the phase mismatch in the SFVS process.The change of χS,eff2 (ω) directlyreflects the change of EDC(z) in both magnitude and direction.The intensity of the SFVS peaks is determined bythe effectivesurface nonlinear susceptibility, χ4Cl interface shows onlya small change with the salt concentration 2SO4, respectively, with the 2.9 kJ/mol variance likely being due tothe effect of the different positive ions in the salts.ntration 2b, indic32 Finally, distortions of the solvation shell of anions near graphenemay introduce asymmetries in the vibration modes that could increasethe intensity of the SFVS peaks. Since the peak intensities were usedto calculate the segregation energy, this could conduce to an overestimationof the energy. These are all important questions that point the wayfor further investigation in theory and experiments.Thenature of the driving force responsible for the preferentialsegregation of sulfate anions is not clear at present. Our experimentalmeasurements indicate that it is not of electrostatic origin sincethere is no doping charge on graphene at the OCP, as shown by theRaman results in Figure S7). The results at 10 mM concentration where graphenedoping is negligible reveal that the positive bias (2SO4 and (NH4)2SO4 . The ionic charge near the graphene electrode that orientsthe hydrogen bonded water vs bias deduced from the SFVS measurements,using formulas S1–S6 in the SI,is shown in the left Y-axis in Y-axis,normalized to match the SFVS charge value at the CNP. As can be seen,the ion charge density curves measured from SFVS and from CPD showthe same behavior, with a smaller slope for negative biases (pinkbackground) than for positive biases (green background). Such an asymmetricbehavior was also observed on supported graphene electrode/pure waterinterfaces.36 The nonlinear response ofthe water orientation to different gating potentials and ion speciesindicates again that the Stern–Gouy Chapman model does notdescribe properly the EDL at the microscopic scale37 and that more advanced EDL models that include effectsof solvent dipoles,39 ion solvation structure,40 ion finite size,41 and nonelectrostatic forces between molecular species and electrodesurfaces42 should be used. Since the alignment of thewater dipoles by the electric field is the result of competition betweenthe torque on the water molecules by the field–dipole interactionand the hydrogen bonding network near the interface, the electricfield EDC in The differential segregation of ions to thegraphene electrode is also manifested in the increasing ionic chargenear the graphene calculated from contact potential change measuredby KPFM. For the KPFM measurements, we used a different cell wheregraphene covered a gold-coated 100 nm thick SiNx membraneer holes 4. Graphe2SO4, (NH4)2SO4, and NH4Cl, and their effect in creating a double layerstructure that orients the interfacial water. The first water layerin contact with graphene has a dangling O–H bond that pointsto graphene and remains unchanged both with salt concentration andwith increasing positive potentials but undergoes a chemical interactionwith graphene at negative values that decreases its peak intensityand redshifts its frequency. This indicates that graphene has a hydrophobiccharacter for zero or positive bias, as manifested by the strong danglingO−H bond intensity of the interfacial water molecules, anda hydrophilic character at negative bias, as manifested by the peakfrequency shift and the decrease of its intensity. We showed thata preferential anion accumulation at the interface is driven by segregationfrom the solution bulk, which increased with ion concentration. Wehave shown that the differential segregation at the OCP is not drivenby electrostatic effects nor by formation of specific chemical bonds,which is impeded by the large energy required to desolvate sulfateanions. While the origin of this phenomenon remains unclear, we speculatethat it could be due to reorganization of water in the solvation shellthat breaks the symmetry of the bonded O–H stretch modes. Thistherefore requires further experimental or theoretical study of thestructure of the EDL. We showed also how externally applied fieldsfurther enhance the segregation effects and lead to an increased orientationof interfacial water. The asymmetric change of the field-induced H-bondedwater orientation is proved by both SFVS and KPFM and brings to thefore the need for further studies, particularly theory to better understandit. Finally, we found that the dangling O–H peak of the watermolecules next to graphene remains largely unchanged as a functionof concentration and also under a positive bias but redshifts anddecreases in intensity at a negative bias, pointing to orbital hybridizationbetween dangling H and graphene.In summary, through the combined use of Ramanspectroscopy, SFVS,and KPFM, we determined the effects of the differential ion segregationat the graphene–electrolyte interface in three salt solutions,Na2S2O8 solution leaving suspended graphene with a PMMA/copper frame,floating on the water solution. A potentiostat was connected to graphenethrough the supporting copper frame. To make the sample more robust,two layers of graphene were transferred sequentially, as describedin the SI.We used suspended graphene electrodesmade from CVD-grown grapheneon copper foil (Graphenea Inc). PMMA was first pasted around the edgesof the copper foil on both sides. The copper in the middle regionwas then etched away with a 0.1 M Na43 A Laser Vision opticalparametric generator and amplifier system converts the 1064 nm lightto a visible 532 nm beam and a mid-infrared beam ranging between 2200and 4000 cm–1. SFG is achieved when the visibleand infrared beams overlap spatially and temporally on the sample.The intensity of the sum frequency light as a function of IR frequencyis a vibrational spectrum of the surface species measured in the visibleregion. All the spectra reported in this work were acquired with anSSP polarization combination, where the letters indicate the polarizationof the sum frequency, visible, and IR beams. The CPD was measuredby KPFM with an MFP-3D Asylum Research system, using conductive Pt/Irtips located in the air side of the graphene electrode. The cantileverholding the tip was mechanically oscillated at its 75 kHz resonancefrequency and simultaneous modulated with a 2 kHz AC bias of 3.5 Vamplitude. Tip-sample CPD mapping was obtained in single-pass modewith side-band detection. For Raman spectroscopy, a laser beam withλ = 532 nm was focused on the graphene sample through a longworking distance objective  with a spatialresolution of 0.2 μm. The sample holder used for the Raman experimentsis the same as that in reference (In our SFVS-electrochemicalcell set-up, we used a picosecond lasersystem to generate a 1064 nm near-infrared light with a repetitionrate of 20 Hz.eference .


The energy-dependent angular distribution of the photoelectrons is compared to the simulated electron reflection pattern of a low-energy electron diffraction experiment in the kinetic energy range up to about 55 eV, and the implications for the structure determination are discussed. Constant energy contours due to scattering resonances are well visible in photoelectron diffraction, and their experimental shape is well reproduced. The example of the bilayer graphene is used to reveal the effect of the scattering by the subsurface layer. The photoemission and LEED patterns are shown to contain essentially the same information about the long-range order. The diffraction patterns of C Diffraction of photoelectrons emitted from the core 1 Photoelectron diffraction (PED) ,2,3 has echnique . A conclechnique ,8,9. Recechnique ,11 has aenergies . On the This calls for theoretical methods capable of treating periodic systems with minimum adjustable parameters and providing a straightforward connection to the underlying band structure. The basis for such approaches is the one-step photoemission theory, where the photoemission intensity equals the probability of the transition to the time-reversed LEED state Φk||* ,15,16,17Φk||*  or real-Φk||* . The preΦk||* , where ton Refs. .Nevertheless, the application of the one-step theory is instructive because it uses the same function E and surface parallel wave vector The LEED wave function  in Ref. .z-periodic crystal ∼ of Ref. . Note thAB-stacked bilayer graphene for the normal incidence (Two examples of scattering solutions for the The energy e T(E)=1 ,11,24, ssee Ref. . At the The red-shaded areas in Equation . SomewhaR for the monolayer graphene is compared in s carbon core band and from the 7 eV-wide lowermost valence band (VB)  for thrR and film1-MLRCV.mov provided in the s emission produce very similar patterns, and the VB one is different.The three maps in each row manifest similar gross features, which reflect the structure of the  in Ref.  and firs in Ref. . The pre in Ref. , regardi of Ref. . At E=29The question of interpreting the PED patterns obtained from an extended initial-state band has been first raised in Ref. , where aAB bilayer are presented in t and r, for the incident plane waves with opposite The electron diffraction maps from the rove see  that thene layer . Generalfrom the detector in s states, we can make use of the fact that the coherence of the 1s orbitals at different sites is physically unimportant and consider separately the emission from the front layer and from the back layer, see film3-ABBFT.mov for AB and film4-AABFT.mov for AA stacking). In the example shown in Let us now consider the role of backscattering in the bilayer graphene. One should keep in mind that the time-reversed LEED state is not the true final state of the photoemission process  dAB and AA stacking: Note the rapid growth in reflectivity above 50 eV around the normal incidence for AB stacking, which does not occur for the AA stacking. It is accompanied by an even stronger increase in C AB bilayer that is not seen in the AA one. . The calculations confirm the important role of in-layer scattering in graphene multilayers: many characteristic features are already present in the graphene monolayer. The layer-resolved C The present proof-of-concept calculation demonstrates the prospect of employing the PED technique at low energies in application to 2D crystals and interpreting energy-dependent intensity maps within the one-step photoemission theory. In particular, in the example of graphene monolayer and bilayers, the LEED and PED patterns are shown to contain essentially the same information about the long-range order, the main difference being that for the


A graphene-based transmission line with independent amplitude and phase variation capability is proposed. Variation of graphene’s tunable conductivity by an applied DC bias is exploited in designing an attenuator and a phase shifter. The attenuator and phase shifter are separated from each other by an interdigitated capacitor to ensure independent control of each section through an applied DC bias. The phase shifter is designed by optimizing lengths of a tapered line and an open stub for a maximum variation of input reactance with a change in graphene resistance. The attenuator is designed by two pairs of grounded vias connected to the transmission line through graphene. Variation of graphene resistance controls the signal passing through graphene pads into the ground causing attenuation. An independent variation of 5 dB of attenuation is measured along with an independent phase variation of 23 degrees in the frequency range of 4 GHz to 4.5 GHz. Graphene has been one of the most used innovative materials in recent years ,2. GraphThis variable Fermi energy level causes a variable conductivity, which is very useful for tunable electrical devices . ConventThe synthesis of monolayer graphene requires high technological complexity. Therefore, for the mass scale production of microwave circuitry it is desirable to use multilayered graphene since it exhibits similar tunable conductive behavior at microwave frequencies . It is eSeveral attempts have been made to exploit the variable conductivity for designing tunable microwave components. Attenuators, phase shifters and antennas based on graphene have been proposed in a number of works ,9,10,11.In this paper, for the first time, both the amplitude and phase variations have been introduced on the same transmission line by exploiting graphene’s tunable conductive behavior, unlike only amplitude ,9,10 or The amplitude-phase variation transmission line is composed of a phase shifter, an attenuator and an IDC, as shown in the design of the prototype, shown in The amplitude-phase variation transmission line was designed on an FR4 dielectric substrate that has a dielectric constant, Ɛr = 4.38, and a loss tangent, tanδ = 0.02. The thickness of the dielectric substrate is 1.52 mm. The width of the 50 Ω transmission line is 2.9 mm. The IDC is designed to block the DC bias voltage supplied to each section, permitting the RF signal in the frequency band of 3 GHz to 5 GHz.The IDC is composed of seven identical fingers on each side. The width of the fingers is wd = 0.75 mm and length is Ld = 19 mm. The distance between the fingers is g = 0.25 mm. On the attenuator side, the length of the transmission line is Lat = 30 mm and the distance between the graphene pads is Lc = 5 mm. On the phase shifter side, the length of the transmission line is Lps = 30 mm and the tapered line is Lt = 6 mm. The length of the stub is Ls = 22.5 mm. The stub is 86 Ω, corresponding to a width of 1 mm. The optimized values of the lengths of the stub and taper can be found in [In order to check the functionality of the IDC and the amplitude-phase variation transmission line, the simulations were performed with a commercial FEM tool Ansys HFSS. The simulation results show that the IDC introduces less than 2 dB of the insertion loss in the system, as shown in For the simulations involving the graphene, the graphene pads were modelled as resistive sheets with assigned resistance values in the range of the estimated resistance of the graphene nanoplatelets. The values of Rg and Rg1 are varied, one at a time, keeping the other at the maximum in order to evaluate the individual impact of the attenuator and phase shifter.The maximum and minimum values of the graphene resistance are expected to be 3000 Ω/□ and 800 Ω/□ respectively. It can be clearly seen in The printed circuit board on which the transmission line is present was fabricated with the help of a photolithographic process. The pattern of the transmission line structure is printed with a photoresist on a dual copper-plated dielectric substrate. The unwanted areas lying outside the structure are etched away by ferrous chloride solution. The graphene nanoplatelets used in the transmission line were provided by Alfa Aesar GmbH. The nanoplatelets were characterized by scanning electron microscopy (Tescan Lyra3 FEG) and atomic force microscopy (JPK nanowizard), to investigate their quality and thickness. The measurement setup of the amplitude-phase variation transmission line is shown in The DUT is placed between the two bias tees. The S-parameters are measured for each of the DC bias voltage applied through the bias tees on each side of the transmission line. In this way, the impact of amplitude and phase is individually measured. This causes a large portion of the transmission signal to pass through the graphene into the ground, resulting in attenuation. The maximum variation in |S21| is almost 5 dB, with negligible variation in ∠S21 for the given variation in Rg. For practical applications regarding the antennas and quadrature amplitude modulation schemes, the demonstrated amplitude and phase variation still need improvement. However, this is a proof of concept, and a first step in acquiring a variable amplitude and phase variation on a single transmission line, exploiting graphene’s tunable resistance. A further increase in the number of stubs can provide a higher phase variation. In a similar manner, an increase in the number of vias on the attenuator side can increase the amplitude variation. The unwanted phase variation (in the attenuator) and amplitude variation (in the phase shifter) can be reduced by optimizing the various lengths and widths of the line sections.The benefit of graphene-based amplitude and phase variation is that any number of points can be generated between the extremes by varying the applied DC voltage bias. A small increment is sufficient to vary the graphene resistance, thus varying the amplitude/phase of the signal by a small amount. This could theoretically create infinite number of points on a constellation diagram. In this case of the preliminary modulator, there is no phase shift intended when the attenuation is activated, and no attenuation intended when the phase shifting mode is activated. As shown in Graphene is a scalable material as it retains the same resistance if the aspect ratio is kept the same with a reduced size. This makes it possible to reduce the power consumption as the variation of amplitude and phase is, in principle tied to the resistance. The maximum power requirement of the phase shifter is almost 255 mW and that of the attenuator is 700 mW. This is on the higher side, but if the device is scaled down, this can be reduced while keeping the aspect ratio and resistance constant. The examples of graphene-based devices on a small scale are reported in ,5 where A transmission line based on graphene with the capability of amplitude and phase variation is proposed. The transmission line consists of an attenuator side and a phase shifter side, separated through an interdigitated capacitor. This design ensures independent control of the amplitude and phase. The optimized lengths of lines, stubs and vias in the different sections of the line cause attenuation and phase shift by varying graphene’s resistance through an applied DC bias voltage.


Although polymers are very important and vastly used materials, their physical properties are limited. Therefore, they are reinforced with fillers to relieve diverse restrictions and expand their application areas. The exceptional properties of graphene make it an interesting material with huge potential for application in various industries and devices. The interfacial interaction between graphene and the polymer matrix improved the uniform graphene dispersion in the polymer matrix, enhancing the general nanocomposite performance. Therefore, graphene functionalization is essential to enhance the interfacial interaction, maintain excellent properties, and obstruct graphene agglomeration. Many studies have reported that graphene/polymer nanocomposites have exceptional properties that enable diverse applications. The use of graphene/polymer nanocomposites is expected to increase sustainably and to transform from a basic to an advanced material to offer optimum solutions to industry and consumers. Polymers are important materials in modern society as they are relatively inexpensive and easy to process compared to other materials . However, their inadequate physical properties restrict their application in diverse areas. Therefore, polymers are usually enhanced with fillers of diverse sizes to relieve various restrictions and expand their application areas ,2,3. NanGraphene has proven to be the most powerful material in the world, and has increasingly attracted attention as a promising candidate to substitute the position of carbon-based materials in the enhancement of the mechanical, electrical and thermal properties of polymers. Graphene is a one-atom-thick carbon layer arranged in a honeycomb structure. Additionally, several reports have introduced graphene into polymers for graphene/polymer nanocomposites ,16,17, aSeveral studies have reported a variety of graphene synthesis techniques for pragmatic applications  25]. Ea. Ea25]. Mechanical exfoliation is the simplest process for preparing standalone graphene . In thisCVD is the most beneficial process for fabricating high-quality single-layer graphene for use in various devices . Graphen3, KMnO4, and NaNO3) and concentrated H2SO4, HNO3, or their mixture . Hi. Hi31]. 2 2 2 [2 [2 [[2 [2 [8 , and Sb [2 [2 [8 ), or the8 [2/SO3  and SO3/O3 [3/I2 ). EFGnPs2 adsorption–desorption isotherms [The unique properties of graphene have been verified by diverse analytical techniques. The atomic structure of graphene was verified by transmission electron microscopy (TEM) a, which sotherms . X-ray dsotherms . X-ray psotherms . Raman ssotherms  which casotherms .2/g), intrinsic mobility , tensile strength (130 GPa), Young’s modulus (~1.0 TPa), thermal conductivity (~5000 W/m·K), optical transmittance (~97.7%), and electrical conductivity (~106 S/cm)) have drawn lots of attention [The tremendous attention on graphene has resulted from its exceptional properties, which makes it an attractive material with enormous potential for applications in diverse industries and devices . Its unittention ,53,54,55The intrinsic graphene properties are reduced dramatically as GO includes oxygen-containing functional groups and diverse defects on the basal plane and at the edges, and aggregation, which is derived from the high specific surface area and the strong van der Waals force between graphene sheets ,57,58. P2sp to 3sp, resulting in π-conjugation bond damage.Covalent functionalization introduces new functional groups to enhance the performance of graphene and graphene-like nanomaterials. GO comprises a variety of oxygen-containing functional groups  that can be applied to general chemical reactions  ,61. Addiπ–π interactions, hydrophobic forces, hydrogen bonding, van der Waals forces, ionic bonding, and electrostatic effects [Non-covalent functionalization can be generated through supramolecular chemistry including  effects ,66. The The solution method is the most vastly used technique to prepare polymer nanocomposites on the laboratory scale due to its high utility through the use of diverse solvents and graphene along with a fast and simple process ,68,69. TThe melting method is an industrial-friendly process to predominantly produce thermoplastic-based nanocomposites owing to its rapid and economic process ,70. ThisIn situ polymerization enables the filler to be grafted onto the polymer, which improves the compatibility and interface between the constituents ,72,73,74Electrospinning is used for preparing fine nanofibers with average diameters ranging from nanometers to micrometers ,75. ElecElectrodeposition is a simple and fast method for producing nanocomposites via electrochemical reactions . It invoCarbon-based polymer nanocomposites have been used to improve the mechanical performance of pure polymers by increasing the interaction between polymers and carbon materials. Among them, graphene/polymer nanocomposites exhibit significantly improved mechanical properties compared to those of pure polymers ,78,79. TIn the case of ultrahigh-molecular-weight polyethylene (UHMWPE)/thermally reduced graphene oxide (TrGO), the tensile strength and Young’s modulus improved with an increase in the TrGO content due to the exceptional inherent TrGO strength and stiffness . In the High-density polyethylene (HDPE)/graphene nanocomposites were prepared directly from HDPE and graphene using a twin-screw extruder. HDPE stiffness increased significantly with increasing graphene content, with a maximum Young’s modulus (187% increase) . TherefoHeptene-functionalized graphitic nanoplatelets (HGN) produced by ball milling were incorporated as reinforcing fillers for LLDPE (linear low-density polyethylene) . The tenPolypropylene (PP)/reduced graphene oxide (PP/rGO_X) nanocomposites with low rGO concentrations  were created via twin-screw extrusion . The tenPP/graphene (polypropylene coated graphene) nanocomposites with exfoliated PP-coated graphene, which can prevent the restacking of graphene sheets during melt blending, demonstrated exceptional interaction and effective load transfer between the polymer and graphene . In the Edge-propylene graphitic nanoplatelets (EPGnP/PP)/PP_X nanocomposites were prepared utilizing a solution method . EPGnP/PThe epoxy/GNP_X nanocomposites manufactured by melt mixing demonstrated an increase in the GNP content, tensile strength, and Young’s modulus of the nanocomposite . In epoxDiverse epoxy nanocomposites with pristine- and Triton-graphene  have been prepared [PVA/GO-X (X = GO content) nanocomposites were prepared utilizing the solution method . ComparePVA/GO-X (X = GO content) nanocomposites were prepared via in situ polymerization . The tenPU-GX (X = graphene content) nanocomposites were prepared with waterborne biodegradable PU and graphene at diverse graphene contents  . GraphenPU/graphene nanofibers were obtained by electrospinning a PU/graphene solution comprising GO, f-GO , and r-GO at diverse concentrations  . The modHyperbranched aromatic polyamide-functionalized graphene sheets (GS-HBA) were prepared with 3,5-diaminobenzoic acid (DABA) as a monomer . GS-HBA As the GS-HBA content increased, the stress gradually increased, and the strain steadily decreased. The S–S curve of the 15 wt.% GS-HBA nanocomposite was similar to pure TPU. The highest Young’s modulus of the nanocomposite with 15 wt.% GS-HBA was 1.2 GPa, that is one order of magnitude higher than pure TPU (0.09 GPa). The yield tensile strengths of nanocomposites with 15 wt.% GS-HBA (25 MPa) increased about seven-fold compared with pure TPU (3.4 MPa), that is comparable to UHMWPE (about 22 MPa). Initially, the ultimate tensile strength increased from 18 to 37 MPa, and then decreased slightly to approximately 30 MPa when the GS-HBA content increased to over 2.5 wt.%. GS-HBA may advance strain hardening and strain-induced crystallization of soft segments (SS) owing to hydrogen bonding. As the GS-HBA content increased, the strain at the break of the nanocomposites decreased. PS/FLG-X (X = FLG content) nanocomposites were prepared by combining graphene produced via liquid-phase exfoliation with polystyrene . The YouEdge-styrene graphitic nanoplatelets (StGnPs) were prepared directly using a mechanochemical reaction with graphite and styrene, and StGnP/PS-X (X = StGnP content) nanocomposites were prepared utilizing solution methods . The mecMulti-layer graphene (MLG) PVC nanocomposites  were prepared by in situ polymerization and melt mixing . The yiePVC nanocomposites embedded with graphene nanoplatelets (GNP/PVC) were prepared utilizing a solution method . The GNPGO/nylon 6 was prepared via a solution-mixing method . The incNylon 6/sulfonated graphene nanocomposites  were prepared via in situ ring-opening polymerization of ε-caprolactam and sulfonated graphene (SG) with polar sulfonic acid . Comparem, Tg, Tc of nanocomposites can be found from the measurement data [g, is one of the important factors in polymer treatment and production applications, and it depends on the molecular weight of the polymer chain and the spatial structure of the polymer [The mechanical properties and thermal stability of the graphene/polymer nanocomposite are influenced by the internal microstructure and crystallinity. Thermal properties of polymers can be confirmed with various measuring equipment  and the Tent data . In part polymer .g of pure PVA with the Tg of graphene/PVA nanocomposites. As a result, it can be observed that Tg of the graphene/PVA nanocomposite increased compared to pure PVA. The increase in Tg indicates that the polymer chain was limited by the H-bonding interaction. Load transfer is largely dependent on the interfacial interaction between the filler and the wrapped polymer matrix [c) may also be obtained through DSC measurement. As the content of GNP in the PP/GNP nanocomposites increases, Tc shifted to a higher temperature, indicating that the presence of GNP as a nucleating agent in nanocomposites promotes the crystallization of PP [Differential scanning calorimetry (DSC) is used to study the thermal behavior of the nanocomposites and to follow their melting and crystallization behavior . Liang er matrix . In addion of PP ,146. Theon of PP .The thermal stability and composition of the nanocomposites are investigated using thermogravimetric analysis (TGA). Additionally, TGA was used to confirm filler loading and evaluate the effect of filler loading on the thermal stability in nanocomposites. The degradation performance was improved as filler loading increased, due to the hindered effect of the filler upon the diffusion of oxygen and volatile products throughout the nanocomposite material. That is, it can be seen that the thermal stability of the polymer was improved by adding graphene .g of the composites gradually transfers to higher temperatures [g of the PU nanocomposite by constraining the mobility of PU chains segment [Tensile test and dynamic mechanical analysis (DMA) have been conducted to evaluate the enhancement properties of nanofillers . Accordieratures . The higeratures ,149. The segment .An optimized graphene content in a polymer matrix is typically used for structural reinforcement areas ,152,153.Owing to the outstanding structural strength and conductivity of graphene, polymer nanocomposites have become a potential candidate for aerospace applications . ThermosGraphene/polymer nanocomposites have advantages in diverse environmental applications including water treatment, energy production, and contaminant sensing ,151,155.Research is being conducted on the use of graphene as a new and renewable energy source. In solar cells, graphene either acts as the active medium or as a transparent/distributed electrode material ,156. It Graphene possesses properties that are distinct from those used in biotechnological and bio-based applications. Owing to its large surface area, chemical stability, and functionalization feasibility, it is a promising candidate for tissue engineering, drug delivery, and DNA sequencing . FurtherAmong the various reinforcement materials, graphene has excellent mechanical, thermal, and electrical properties, and is regarded as a better alternative to general nanofillers. Thus, graphene/polymer nanocomposites have properties that are extraordinary for diverse applications. Several studies on the exceptional graphene properties have revealed that it plays an imperative role in enhancing the specific properties of polymer nanocomposites, which can be utilized in diverse applications ,150,158.Current polymer nanocomposites are useful in various aspects. Many studies have been reported demonstrating that graphene/polymer nanocomposites can be applied in various fields through the chemical functionalization of graphene used as fillers. The uniform dispersion of graphene is one of the factors that can improve performance. Additionally, the interaction between the polymer and the filler, the surface area, filler loading, and dispersion are various factors that determine the quality of the nanocomposite. Despite numerous challenges related to graphene, a suitable interaction between graphene and polymers is yet to be developed. However, the manufacturing industry cannot neglect the use of graphene  in commercial products. It is expected that it will be transformed from a typical to a high-performance material that can offer the best solutions for industry and consumers. Therefore, the business influence of graphene/polymer nanocomposites is growing continuously.