We adopted the wet chemical procedure, using various contents of Fe3O4 (magnetite) from 0–100% for making two-dimensional graphene–Fe3O4 nanohybrids. The homogeneous dispersal of Fe3O4 nanoparticles decorated on the graphene surface combined with their biocompatibility and high thermal conductivity make them an excellent material for magnetic hyperthermia. The morphological and magnetic properties of the nanohybrids were studied using scanning electron microscopy (SEM) and a vibrating sample magnetometer (VSM), respectively. The smart magnetic platforms were exposed to an alternating current (AC) magnetic field of 633 kHz and of strength 9.1 mT for studying their hyperthermic performance. The localized antitumor effects were investigated with artificial neural network modeling. A neural net time-series model was developed for the assessment of the best nanohybrid composition to serve the purpose with an accuracy close to 100%. Six Nonlinear Autoregressive with External Input (NARX) models were obtained, one for each of the components. The assessment of the accuracy of the predicted results has been done on the basis of Mean Squared Error (MSE). The highest Mean Squared Error value was obtained for the nanohybrid containing 45% magnetite and 55% graphene (F45G55) in the training phase i.e., 0.44703, which is where the model achieved optimal results after 71 epochs. The F45G55 nanohybrid was found to be the best for hyperthermia applications in low dosage with the highest specific absorption rate (SAR) and mean squared error values.We report the synthesis and characterization of graphene functionalized with iron (Fe 3O4–graphene (FG) nanohybrids and magnetothermal measurements of FxGx100–  nanohybrids (25 mg each) at a 633 kHz alternating magnetic field of strength 9.1 mT.Synthesis of Fe According to the factsheet of February 2018 issued by the world health organization (WHO), cancer is the cause of almost 8.8 million deaths annually.3 It is imperative to develop an effective and accurate treatment method for this malignant disease. The typical therapies in-practice for cancer treatment include radiation therapy, chemotherapy, and surgery. However, apart from being painful, these therapies lead to several side effects such as damage to healthy tissues, fatigue, alopecia and multidrug resistance (MDR). Magnetic hyperthermia (HT) is an alternative and promising non-invasive approach for cancer treatment, where magnetic thermoseeds are injected directly into the tumor area of the patient.4 The cancerous area, containing the implanted thermoseeds, is heated to an elevated temperature through magnetic nanoparticles (MNPs). The heating phenomena is mainly induced in the magnetic nanoparticles under alternating magnetic field due to hysteresis, Néel and Brownian relaxation losses. Brown relaxation losses occurs when the nanoparticle rotate in the fluid and produce heat via a fraction mechanism in the aqueous medium. As there is a reduced blood flow in the tumor area, containing disorganized blood vessels, the heat dissipation to the surrounding area is limited. Therefore, cancerous cells are more susceptible for apoptosis at relatively mild heating up to ∼42 °C as compared to healthy cells. It has been reported that a temperature range of 41.8–44 °C provides the most suitable conditions for entire body hyperthermia.5 This is due to the leaky vasculature of the cancer cells that obstructs dissipation of thermal energy from them, as compared to the well-ordered blood vessels and nerves connected to healthy cells that can stand against heat by a more efficient heat dissipation, and a greater excretion of the heat shock proteins.6,7 The unique feature of nanoparticles to act selectively on the tumor cells sparing the healthy cells makes this therapeutic technique much more accurate as compared to the conventional methods.8,9Cancer treatment with high accuracy is a major concern of the medical community. For instance, hyperthermia or heat mediated therapy has today become of great significance utilizing energy absorbing nanoparticles.3O4 remained a hot contestant in hyperthermia applications due to their biocompatibility, strong intrinsic magnetic properties and their use in bio-medical applications. The use of these materials in magnetic resonance imaging (MRI) contrast enhancement, magnetic hyperthermia cancer therapy, and targeted drug delivery is a manifestation of their biocompatibility.10–15 However, the bare pristine Fe3O4 nanoparticles possess strong anisotropic dipolar interactions and high magnetization that result in agglomeration and precipitation. Due to these factors, their colloidal solubility is lost, and their activity is reduced. Therefore, it is challenging to incorporate Fe3O4 nanoparticles in both in vitro and in vivo experiments. To prevent their agglomeration and precipitation, we have developed a support of reduced graphene oxide sheets, for making Fe3O4 nanoparticles immobilized.Ferrites such as Fe2 bonded carbon atoms. The introduction of the exfoliation technique in 2006 for producing single-layer graphene,16 has gained tremendous attention from application perspective. Large thermal conductivity (κ ∼ 5.3 × 103 W m−1),17,18 high flexibility and strength ,18–20 and excellent biocompatibility of graphene lead to remarkable properties for the development of prototype devices for biological applications such as miniaturized single fat-cell glucose sensors,21 graphene-based single-bacterium bio-device, label-free DNA sensor, and bacterial DNA/protein.22 Controlled tunability of the fabrication and functionalization of graphene is required, to achieve a remarkable performance from graphene-based devices, making it an important topic of contemporary research.Graphene is a single-atomic-thin planar sheet of sp23–25 Among all, Hummer's method is the most famous and reliable for economical large-scale production of graphene. The Graphene Oxide (GO) obtained by Hummer's method is hydrophilic owing to numerous attached functional groups , making the graphene easy to functionalize with other species. Cong et al. reported the synthesis of hydrazine reduced GO sheets (prepared by modified Hummer's method), that were post decorated with Fe3O4 nanoparticles. These Fe3O4 functionalized graphene sheets could be used as a magnetic resonance contrast. Jing Su et al.27 prepared graphene–Fe3O4 nanohybrids via a hydrothermal approach. These materials exhibit superparamagnetic properties for biocompatible controlled drug delivery.Thus far different techniques have been developed to produce graphene stacks of varying thicknesses, a post-functionalization to make hybrids of graphene with other materials. Morphologies and properties of free standing nano-islands of graphene in layered hybrid systems have been found to be closely related to their growth strategies.3O4–graphene nanohybrids. The synthesized nanohybrid materials are applied to investigate magnetic hyperthermia and their functionality is compared with that of pristine Fe3O4 and reduced graphene oxide. Large specific heat capacity of graphene and the charge transfer effect between graphene sheets and immobilized magnetic Fe3O4 nanoparticles enhanced the magnetic hyperthermia effect. The best thermoseed agent was determined by calculating the specific absorption rate (SAR).Although Hummer's method provides an efficient approach for the introduction of new species to a 2D graphene oxide system in the form of hybrids however, reduction of GO is an important stage for a reliable functionalization of defect free graphene. In this work, we adopted thermal reduction method for producing graphene and FeArtificial intelligence (AI) has been dominant in health care and medical sciences since the advent of current century. Researchers and futurists have concluded that the collaboration of this technology with the doctors can make wonders and that there is still a long way for scientists to go to unveil the extraordinary potential of AI to transform health care into a much more modern and efficient medical care system. Keeping this in view, we have also utilized AI to produce improved nanoparticles for hyperthermia treatment.i.e., AI. Deep learning consists of artificial neural networks (ANN) based algorithms. ANNs have been used by scientists to analyse time series data for prediction and forecasting.28,29We have used a deep learning algorithm for the time series modeling of hyperthermia data. Deep learning is a subset of machine learning which is a subfield of a bigger domain We have developed a system of six artificial neural networks to assess the best nanoparticle. ANNs with exogenous input have this capability to extract information from the past values in the data and process it to learn and then predict step ahead values which gives us an insight of the performance of each particle. However, the algorithm requires as much data as possible for improved predictions.2.3O4–graphene nanohybrids, high quality expandable graphite powder of mean size 25 μm was purchased from Aldrich (purity 99.99%), FeCl3·6H2O from Riedel-de Haen (purity 99%), FeCl2·4H2O (purity 99.8%) and HCl from Merck, KMnO4 from BDH (purity 99%), 32% NH3 solution and high grade H2SO4 and H2O2 (30 wt%) were purchased from Panreac. All reactions were carried out using deionized (DI) water.For synthesis of Fe2.1.30 5 g graphite powder was added in 125 mL H2SO4 (99.99% assay) in a flat-bottomed flask at 0 °C followed by vigorous stirring to avoid agglomeration. Once the powder was well dispersed, 15 g KMnO4 was added to the mixture slowly, at a low temperature, below 15 °C. Gradually the mixture was brought to room temperature. After the reaction, mixture became pasty and turned light brown in color. 150 mL of DI water was added slowly to the mixture to dilute it, after which 17 mL H2O2 (30 wt%) was added, that changed the mixture color to yellow. Finally, the mixture was washed with 1 : 10 HCl (1 M) solution to remove residual ions. Grey colored GO powder was obtained after drying the solution in oil bath with continuous stirring (10 rpm) for 8 hours at room temperature (25 °C).GO was synthesized using graphite powder as a starting material by modified Hummer's method.2.2.xGx100– compositions have been synthesized, where x  refers to the weight percentage of magnetite in the nanohybrid. Note the composition with x = 0 specifies pure graphene and x = 100 specifies pure magnetite. Specific amounts of FeCl3·6H2O and FeCl2·4H2O and graphene oxide (GO) were weighed for each composition. Stoichiometric quantities of FeCl3·6H2O and FeCl2·4H2O were dissolved in 25 mL de-ionized (DI) water to obtain their 0.04 and 0.02 molar aqueous solutions, respectively. The required amount of GO was dispersed in DI water (250 mL H2O for 0.9 g GO) for each composition. In order to transform the attached carboxylic acid groups to carboxylate anions, the dispersion was sonicated for 1 h. Then, 0.02 mole of FeCl2·4H2O and 0.04 mol of FeCl3·6H2O were dissolved in DI water and added dropwise to the GO solution at room temperature with vigorous stirring. On completion of ion exchange reaction, 32% NH3 solution was added drop by drop until the pH of solution became 10 that is required for the formation of Fe3O4 NPs.31 All the compositions were dried in oil bath with continuous stirring (10 rpm) for 8 hours at room temperature (25 °C).Magnetite–graphene oxide F2.3.3O4–GO compositions in dried powder forms were thermally reduced in a quartz tube furnace for 1 h at 800 °C (heating rate 10 °C min−1) at 300 cm3 flow rate of forming gas . The samples were named according to the weight % ratio in the compositional formula FxGx100–  as G, F25G75, F45G55, F65G35, F75G25, F85G15 and F, respectively. For example, F45G55 refers to the nanohybrid containing 45 wt% magnetite and 55 wt% graphene. Schematic diagram for synthesis of FG nanohybrids is shown in For the fabrication magnetite–graphene (FG) nanohybrids, Fe2.4.31 method. Fe3O4 NPs were obtained on reacting iron-II (Fe2+) and iron-III (Fe3+) ions in an ammonia solution (pH 10) in the molar ratio 1 : 2 respectively, at room temperature by stirring the solution for 30 min at 4000 rpm, following the reaction in eqn (1)Magnetite nanoparticles were prepared by co-precipitationThe solution was washed several times at room temperature by stirring the dispersion for 30 min at 3000 rpm, to remove the unwanted ions and until it attained a pH of 7. Magnetite nanoparticles were centrifuged from the solution and dried at room temperature.3.15,32,33 Furthermore, several models based on nonlinear autoregressive structure have been proposed.34–36 We have developed a neural net time-series model using Neural Net Time Series app on MATLAB R2018b. It takes one or more time series for prediction; however, our time-series difficulty is to make a prediction by utilizing the l past values of a time series which is being predicted (y(t)), and another time-series (x(t)), in our case the 100% magnetite particle. In this basis, we have used Nonlinear Autoregressive with External Input (NARX) neural network. NARX is a recurrent network capable to model dynamic systems,2 it is not only able to predict output value which is regressed on the previous values but is also used for nonlinear filtering  = 37 However, our choice of Bayesian regularization is influenced by the involvement of Bayesian theorem which incorporates prior data along with maximum likelihood function to give posterior distribution.38Data is fed into the three layered  network consisting of 10 hidden neurons and two-time delays . Size of39 Adopting a probabilistic approach towards machine learning is the most reliable strategy to solve a problem like future prediction which is based on uncertainty.40 It may take a little longer but yields finer results and has emerged to be the most robust and vigorous one in comparison to the standard back propagation NNs. Regularization itself is meant to overcome the overfitting issue, hence the models trained using BR are difficult to overtrain and overfit.41 It utilizes posterior probability which involves utilization of Bayesian theorem for parametric optimization and updating the knowledge from prior to posterior.42 Bayesian inference is a highly commendable approach for statistical analysis of stochastic processes.43BR happens to be a better choice for quantitative researches, due to its potential to unveil complex data patterns and interrelations.The training process continues till the optimal result is achieved and is terminated as the generalization stops improving, the model can be trained for the maximum number of 1000 epochs.To are the observed values and Te are the estimated ones. Moreover, regression (R) values have been calculated to see the correlation between output and target. Values of R close to 1 are considered to be optimal, it indicates close relationship between output and target.After training, the assessment of the accuracy of the predicted results is carried out on the basis of Mean Squared Error (MSE) 4.i.e., 65.6 μm for sample G. It is evidenced that for low Fe3O4 samples (up to 45%), Fe3O4 nanoparticles are evenly distributed within the graphene matrix with a very little degree of agglomeration. However, with increasing the content of Fe3O4, further agglomeration is observed. It can be concluded that, the average size of Fe3O4 nanoparticles significantly varies with the content of Fe3O4 with nano scale length. The image in 3O4 nanoparticles with non-uniform size distribution due to magnetic anisotropic interactions.JEOL (Japan) JSM-6400F field emission scanning electron microscopy (FE-SEM) equipped with electron diffraction microscopy (EDX) was used for morphological and elemental analysis of Functionalized Graphene (FG) nanohybrids as presented in 3O4 on the graphene surface sample F75G25 was randomly selected for energy dispersive X-ray spectroscopy (EDX) as shown in 3O4 nanoparticles are distributed between the layers of the graphene sheets, which lead to the formation of Fe3O4–graphene nanohybrids.To confirm the presence of the Fe3O4 and FG samples were exposed to DC magnetic field, up to 20 kOe, in Lake Shore 7404 (US) vibrating sample magnetometer (VSM), at room temperature. The field dependent M(H) curves of all the samples (3O4) for a comparative study of their magnetic properties. The inset shows the M(H) loop of pure graphene. Remanence (Mr) and coercivity (Hc) values for all samples are shown in Pristine graphene, Fe samples  were norMr) and coercivity (Hc) values of samples are not increasing monotonically with the Fe3O4 content. As seen in SEM images 3O4 content, further agglomeration is observed. It can be seen from combined analysis of SEM and VSM results that Hc and Mr values are mainly dependent on degree of agglomeration of Fe3O4 rather than its concentration. Moreover, the magnetic behavior of a magnetic component dispersed in a nonmagnetic reduced graphene oxide matrix depends not only on the quantity of the magnetic component but also on how it is dispersed within the matrix.44 The weak magnetization of graphene is due to the presence of local defect states and non-magnetic nature.45 Sample G attains a maximum saturation magnetization of 0.08 emu g−1. The measured saturation magnetization (MS) of pure Fe3O4 (sample-F) ∼42 emu g−1 is much lower than the value reported for bulk counterpart (85–95 emu g−1). This is due to the well-understood size dependence of magnetization in nanoparticles in which surface spin disorder can lead to a magnetically dead surface layer.46 In case of FG samples, the magnetic behavior  is not depending upon the magnetic component. For example, remanence and saturation magnetization of the F45G55 sample is comparable to that of F75G25i.e., ∼6 emu g−1 and 20 emu g−1 respectively. This can be due to the intercalation-dependent magnetic interactions. As shown in the SEM images  was measured using NAN201003 MagneTherm (UK) induction heating unit for 25 mg of each sample powder exposed to a 633 kHz alternating magnetic field of strength 9.1 mT. A significant heating upshot was observed in all the synthesized nanohybrids  of each nanohybrid sample was determined by weight % contribution of Fe3O4 and graphene in the given composition. Specific heat capacity of Fe3O4 was taken as 0.937 J g−1 K−1 (ref. −1 K−1.17Specific absorption rate (SAR) is the rate at which MNPs convert magnetic energy into heat. SAR is considered as a figure of merit in hyperthermia measurements. The specific absorption rate (SAR) of the samples have been calculated from the heating curves, using the following K−1 ref.  and thatvs. magnetite content w.r.t wt% ratio in different FG nanohybrids. SAR value obtained for pure magnetite sample (F) is smaller than that reported by P. Burnham et al. i.e.∼5.813 W g−1 for dry magnetite.50 This difference is possibly due to the magnetic field strength (200 Oe), alternating frequency (282 kHz) and particle size (particle size = 15.3 nm) difference. Interestingly, the SAR value of F45G55 sample is ∼1.5 times larger and appx. same for F25G75 samples, as compared to that of the pure magnetite (F). Such an intriguing feature must be due to the large area matrix provided by the reduced graphene oxide sheets, and its heat capacity which is higher than that of pure magnetite. In details, the heat capacity of graphene is about twice that of magnetite, leading to higher SAR for lower content of Fe3O4. Additionally, the highest SAR obtained for sample F45G55i.e., 6.45 W g−1, is due to an excellent exfoliation of graphene and most uniform intercalation of magnetite nanoparticles, as has been observed in SEM images. These behaviors also led to reduced frustration of moments for highest saturation magnetization  values have been calculated to see the correlation between output and target. Calculated values for all the compounds are given in the following Six NARX models are obtained, one for each of the components in R values for the six composites. These values were obtained after completing the testing phase of the model. The model trained for the assessment of F45G55 performance in comparison to pure magnetite yielded highest MSE value. It turns out to be the most different one. Moreover, the R values depict the accuracy of the results obtained from the model which is also high for this composition. The plots obtained from the trained network for F45G55 are as following:i × target + j; where i is slope and j is the y-intercept. Here, we have three plots for different values of j. Each of them shows a meaningful correlation between target and output since the value of R is very close to 1, which is the ideal case. We have achieved maximum regression values for all the particles. x-axis and the predicted versus observed values on the y-axis. The other smaller graph in i.e., the predicted results. Our training and test outputs lie on the response line. Hence, the error is obtained by comparing the values from the response line to the ones that lie above or below the line. So, if the target value lies above the line, we obtain a positive error and, negative otherwise.The regression/scatter plots on y-axis and errors on the x-axis. The maximum number of instances do not have zero error. This shows the predicted results are not very close to the target values, time series and performance plots further validate this interpretation. versus epoch count. As stated earlier, MSE is the difference between the observed and the simulated, therefore it should be lower. According to our motivation, the particle that obtained the highest error is the most favorite one, and for F45G55, MSE obtained in the training phase is 0.44703, where the model achieved optimal results after 71 epochs. However, it is seen that Bayesian regularization does not require validation, this is due to this fact that the sole purpose to perform validation check is to make sure either error increases or decreases while training and avoid overfitting in the testing phase, making Bayesian method encounters successfully while training the model. Most importantly, our experimental results also have shown that highest SAR value is obtained for sample F45G55  were fabricated. All the compositions were characterized and analyzed morphologically and magnetically at microscopic level. Hyperthermia measurements showed a high specific absorption rate (SAR). Sample containing 45% magnetite and 55% graphene i.e., F45G55 is found to have the largest SAR value of 6.45 W g−1 with saturation magnetization ∼20 emu g−1, that is ∼1.5 times greater than that of pure magnetite (4.32 W g−1) with saturation magnetization ∼42 emu g−1. The role of graphene is to reduce frustration of magnetic moments by monitoring the dipolar interactions with better exfoliation and low agglomeration of Fe3O4 nanoparticles. Moreover, large value of specific heat capacity of graphene contributes an increase in SAR of nanohybrids to even 1.5 times larger than that of the pure magnetite. Nonlinear Autoregressive with External Input (NARX) models are obtained for each of the nanohybrid composition. The accuracy of the predicted results has been evaluated on the basis of Mean Squared Error (MSE). The highest MSE value was obtained for the composition containing 45% magnetite and 55% graphene (F45G55) nanohybrid in the training phase i.e., 0.44703 which is where the model achieved optimal results after 71 epochs. Hence, F45G55 sample was found the best nanohybrid with highest SAR and MSE values for hyperthermia applications in low dosage. With suitable surface functionalization, biocompatible Fe3O4-graphene nanohybrids can be useful candidates for localized magnetic hyperthermia applications.The goal of this work was to develop biocompatible two dimensional (2D) magnetic system of high thermal conductivity for the treatment of malignant tumors The authors report no conflicts of interest.


Capsicum cultivar samples. The chemical, structural, and morphological propertiesof the graphene-type materials were evaluated. The materials exhibitedgood adsorption capability of matrix interferents without compromisingthe extraction efficiency of target analytes when compared with othercleanups using commercial sorbents. Under optimal conditions, excellentrecoveries were obtained, ranging from 90 to 108% with relative standarddeviations of <14%. The developed method showed good linearitywith a correlation coefficient above 0.9927, and the limits of quantificationwere in the range of 0.35–0.82 μg/kg. The developed QuEChERSprocedure using reduced graphite oxide (rGO) combined with GC/MS wassuccessfully applied in 20 samples, and the pentabromotoluene residueswere quantified in two samples.A new applicationof graphene-type materials as an alternativecleanup sorbent in a quick, easy, cheap, effective, rugged, and safe(QuEChERS) procedure combined with GC–ECD/GC–MS/GC–MS/MSdetection was successfully used for the simultaneous analysis of 12brominated flame retardants in  Although the use of certain BFRs was banned and restrictedby the European Union due to their persistence and bioaccumulationin the environment, they were detected in biota, food, and feed samples.7Although BFR levels found in food samples are mostly associatedwith environmental contamination, the potential influence from packagingcannot be excluded. A wide range of plastic products may contain BFRs.Material recycling may potentially reintroduce the BFR into new plasticproduct cycles and lead to increased exposure levels, for example,through the use of plastic packaging materials.14 The toxicity of these plastic additives to humans demonstrated thatBFRs have the potential to adversely affect endocrine functions andthe central nervous and reproductive systems. The different brominateddiphenyl ethers (BDE) appear to have a similar toxicological profile,with the liver, kidney, and thyroid as the main target organs.15 Several analytical techniques are used in monitoringstudies of pesticides and other contaminants in food samples.18 These include mainly liquid chromatography and gas chromatographyfor multiple contaminant analysis and electrochemical techniques usuallyfor one target.20Consumption of BFR-contaminated food products may result in serioushealth problems in human beings.Capsicum cultivars are samples that have a complexcomposition since they contain numerous chemicals including steam-volatileoils, fatty oils, capsaicinoids, carotenoids, vitamins, proteins,fibers, and mineral elements.21 Samplepreparation techniques for BFR extraction include QuEChERS,1 dispersive liquid–liquid micro-extraction,solid-phase extraction (SPE), solid-phase micro-extraction, and otherapproaches use adsorbent-based techniques for testing new synthesizedmaterials such as QuEChERS with magnetic nanoparticles.22 These new trends of procedures, using magneticnanoparticles, have evolved, gained visibility, and grown due to thesuccessful results. In addition, the main attributes were organicsolvent-free/reduction, small amounts of adsorbents, fewer steps,and less time and energy.22 The authorsalso showed that these magnetic nanoparticle adsorbents, mainly theones with particles with a 200 nm average size, presented an adsorptionability toward pigment matrix and other co-extracts from strawberrysamples. This type of adsorbent has also shown a set of successfulapplications when applied in the pre-concentration of BFRs in otherred fruit samples (blueberry and raspberry).2 Some authors reported the use of graphene materials in several extractionmethods for pesticide analysis in food samples.25 Recently, Zhenget al. reported the synthesized and successful use of ZIF-8@nitrogen-dopedreduced graphene oxide as a coating material for the extraction ofhalogenated flame retardants in crayfish.26 Another application using graphene oxide-based surface molecularlyimprinted polymers was reported for organophosphate flame retardantsin environmental water.27 Khetagoudar etal. reported the excellent performance of a graphene nanocompositein the elimination of pigments in green chili matrices.28 To the best of our knowledge, studies in whichgraphene-type materials [reduced graphite oxide (rGO) and sulfur-dopedgraphene (S-GF)] are applied for QuEChERS extraction of BFRs fromfruits/vegetables are not reported in the literature. It is, therefore,imperative to innovate and develop new methods to meet the needs forsample integrity, rapid analysis, cost-effective, and environmentallyfriendly methods, precision, and reproducibility. Also, no MRLs havebeen established for BFR, but the European Commission recommendedtheir monitoring in food.29Some BFRs are present incomplex matrices in vestigial amounts,such that they cannot be simply detected. Complex samples are challengingand require more attention, and many limitations need to be exploredto mitigate matrix effects that analytical chemists must overcome.Thus, challenges can be met by skillful sample pre-concentration ofseveral compounds simultaneously prior to target analyses. The approachesshould be tailored to study matrix interferences and produce effective,reliable, and accurate methods for extraction and analysis. The Capsicum cultivars) witha high content of pigments (carotenoids), spicy compounds (capsaicinoids),fatty oils, protein, mineral elements, and so forth. Moreover, thepossible contamination of the target analytes [polyBDE (PBDE) congenersand novel BFR] conditioned in plastic packaging was also explored.Here,for the first time, an analytical approach developed to meetthe demands using different dispersive SPE (d-SPE) sorbents, namely,synthesized graphene-type materials (rGO and S-GF), was evaluatedto meet the challenge of removing the interferents from a complexfood sample , and the acetonitrile was obtained from CarloErba . The QuEChERS EN method and bulk sorbents,namely, primary secondary amine (PSA), C18-silica, and carbon, wereobtained from Agilent Technologies . The carbon bulk specificationsdescribed that the average particle size was 40–120 μmand irregular. A working standard mixture of the contaminants containinga total of 12 BFRs was prepared at 150 μg/L in n-hexane. This mixture was used to prepare spiked and calibrated standardsolutions in n-hexane. Data and statistical analysiswere carried out using GraphPad Prism 6.0 and Excel software.The standards of BFRs (sevenPBDE congeners and five novel BFRs) with high purity (≥97%)were purchased from Isostandards Material, S.L.  andstored in a freezer. The 12 compounds were 1,2-dibromo-4--cyclohexane(TBECH), pentabromotoluene (PBT), 2,4,4′-tribromodiphenyl ether(BDE28), pentabromoethylbenzene (PBEB), 2,2′,4,4′-tetrabromodiphenylether (BDE47), 2-ethylhexyl 2,3,4,5-tetrabromobenzoate (TBB), 2,2′,4,4′,5-pentabromodiphenylether (BDE99), 2,2′,4,4′,6-pentabromodiphenyl ether(BDE100), 2,2′,4,4′,5,5′-hexabromodiphenyl ether(BDE153), 2,2′,4,4′,5,6′-hexabromodiphenyl ether(BDE154), 2,2′,3,4,4′,5′,6-heptabromodiphenylether (BDE183), and 1,2-bisethane (BTBPE).The internal standard (IS) 5′-fluoro-2,3′,4,4′,5-pentabromodiphenylether was also obtained from Isostandards Material, S.L. . The 2.231 Briefly, the rGO preparationconsisted, in the first step, the addition of 10 g of graphite andover 200 mL of fuming HNO3, keeping the mixture at 0 °C.Then, 80 g of KClO3 was gradually added over 2 h; afterward,the mixture was stirred for 21 h, maintaining the temperature of 0°C. The graphene (GO) material obtained was filtered and washedwith water until a neutral pH was reached and then dried under vacuumat room temperature. In the second step, the GO was exfoliated ina vertical quartz reactor under a nitrogen atmosphere by heating at10 °C/min to 250 °C and keeping this temperature for 30min; then, the temperature was increased from 250 to 700 °C usingthe same heating rate, and then this temperature was maintained for30 min. At the end of the process, the rGO material was obtained.31 The S-GF was prepared by milling 0.5 g of commercialgraphene flakes (GF) together with 0.15 g of elemental sulfur in ballmilling equipment  for 5 h at a frequency of 15 s–1 using two zirconium oxide balls (1.5 cm in diameter).The obtained material was then pyrolyzed in a nitrogen atmosphere,heated up to 600 °C at a heating rate of 2 °C/min (Nabertherm),and maintained at this temperature for 1 h.30The graphene-type materials were prepared by proceduresalready reported.Supporting Information file.The prepared materials were characterized by several techniques,such as Raman, XPS, XRD, SBET, and TEM. All details  regardin2.3Capsicum(C.) annuum L., Capsicum frutescens L., and Capsicum chinense (packagedand stored in plastic containers) were purchased in Porto, Portugal,at local supermarkets. The samples were separately frozen at −20°C. Each sample was coldly homogenized using a high-performanceblender . The grinding of the sample was performedat the maximum speed of the equipment (speed 10) for 10 s; then, thesample was removed from the walls of the container, and the samplewas ground again at a speed of 10 for 10 s. After this sample preparation,the samples were frozen and stored in the same package until extractionand analysis.A total of 20 samples of 2.4n-hexane. The IS was added at a constant concentration foranalytical quality control immediately before injection. After vortexing,the sample was ready for gas chromatographic analysis.Samples(10 g) were mixed with 10 mL of acetonitrile in a 50 mL Falcon tube.Subsequently, EN QuEChERS salts were added, and the sample was vortexedfor 1 min. The tube was centrifuged for 5 min at 4500 rpm. 1.5 mLof the upper layer was transferred to a 2 mL microtube with the additionof different sets of sorbents 1. The mi2.4.132 and EURACHEMguidelines:33 linearity with a matrix-matchedcalibration curve, matrix effect, recoveries, the limit of detection(LOD), the limit of quantification (LOQ), and repeatability and reproducibility.The LOD and LOQ were calculated based on the standard deviation ofthe signal and the slope of the calibration curve. The matrix effectsand intra-day and inter-day precision were calculated according toFernandes et al. 2020.2 The C. annuum L. extract spiked with each of the BFRsat seven concentration levels (μg/kg) (ranging between 0.10and 1.88 μg/kg) was prepared for the establishment of the calibrationcurve. For each level, the determinations were performed in triplicateunder optimal conditions. The recovery and repeatability of 12 BFRswere determined to evaluate the method’s performance in C. annuum L. The repeatability and trueness of themethod were studied by carrying out six consecutive extractions atthree concentration levels .The analyticalperformance of the gas chromatography system with an electron capturedetector (GC-ECD) was carried out through the following parametersbased on SANTE/11813/20172.51 Briefly, the gas chromatography conditions were as follows:the injector temperature was 250 °C; the initial oven temperaturewas 90 °C (held for 1 min), which was then increased to 150 °Cat 16 °C/min (held for 1 min) and then to 290 °C at 16 °C/min(held for 30 min); and the gas chromatography detector was maintainedat 300 °C. The splitless injection was adopted throughout thewhole experiment. The method validation and all the analyses wereperformed in GC-ECD. GC/MS analysis with a Trace-Ultra GC  coupled to an ion trap mass detector,Thermo Polaris, was performed at the same conditions as GC-ECD inall the positive samples observed in GC-ECD in order to have confirmation.Data acquisition was performed first in the full scanning mode from50 to 500 m/z to confirm the retentiontimes of the analytes. All standards and sample extracts were analyzedin the selective ion monitoring (SIM) mode and tandem mass spectrometry(MS/MS). The SIM ions selected for the PBT confirmation were 486,328, and 326, and the precursor ions for MS/MS were 486 and 326.Analysiswas performed on GC-ECD equipped with a Zebron-5MS fused silica capillary column (30 m ×0.25 mm i.d. × 0.25 μm film thickness)  used for separation and ultrapure helium (purity ≥ 99.999%)used as the carrier gas at a flow rate of 1.0 mL/min. Chromatographicdata were recorded and processed with Shimadzu’s GC Solutionsoftware. The parameters were according to those.2.6ANOVA statisticalanalysis was performed to estimate significant differences among differentanalytical procedures using GraphPad software.33.131 here, a summary of the main aspects of their characterization isgiven for the sake of the reader to avoid labyrinthic access to data.The rGO was characterized by several techniques, namely, N2-adsorption isotherms, XRD, TEM, XPS, and Raman spectroscopy.31 The surface area determined by the BET methodto N2 adsorption isotherms  was 867 m2 g–1, which suggests the formation of a few-layer graphene structurefor rGO. The average pore diameter was found to be 7.9 nm by the BJHanalysis . The XRD corroboratedthe formation of a few-layer graphene structure (average stackingnumber of 12 layers) with an interlayer distance of 0.34 nm. The rGOTEM micrograph  showed thepresence of winkled structures of graphene consisting of 5–12graphene layers. The characterization of the rGO composition by XPSanalysis  indicated a surfacecomposed of carbon and oxygen . Raman spectroscopyis widely used for the characterization of carbon materials , especially because conjugated and double carbon–carbonbonds lead to high Raman intensities.34 The stretching of the C–C bond in graphitic materials givesrise to the so-called G-band Raman feature, which is common to allsp2 carbon systems (∼1580 cm–1). The G-band is highly sensitive to strain effects in sp2 nanocarbons and can be used to probe any modification to the flatgeometric structure of graphene. The D band (∼1350 cm–1) is significant in providing information about the electronic andgeometrical structures through the double resonance process.34 The intensity ratio between the D and G bands, ID/IG, is often usedto estimate the disorder degree of graphitic materials.36Even though these two graphene-type materials werepreviously preparedand their characterization was reported in the literature,ID/IG) asa quantitative indicator of the amount of disorder or edges of therGO structure, which was calculated as 0.63.By Raman spectroscopy, the degree of disorder in the structureof graphene was studied using the intensity ratio of the D and G bands revealeda BET surface area of 284 m2 g–1, whichwas appointed to the piling up of some graphene layers in the S-GFstructure. The average pore diameter was determined by BJH analysisgiving a value of 3.8 nm .The reported XRD pattern of S-GF indicated an interlayer distanceof 0.34 nm and a stacking number of graphene layers of 32, suggestingthe formation of a graphenic material with some aggregation of thegraphitic layers, following the outputs of the N2-adsorptionisotherms analyses. The TEM images of S-GF  showed graphene sheets with some folding mainly at theborders and with sizes in the range of hundreds of nanometers to afew micrometers. The surface composition obtained by XPS  revealed that S-GF contains 97.5%of C, 1.7% of O, and 0.8% of S, confirming S-doping. The Raman spectroscopyrevealed an increase in defect density and disorder due to the S-dopingprocess , as indicatedby the significant increase in the ID/IG ratio obtained for S-GF (1.72 vs 0.61 forundoped GF).Regarding the S-GFmaterial, the NSBET than rGO(284 vs 867 m2 g–1). Although both materialsshowed mesoporous properties, the rGO has a lower stacking of graphiticlayers (NL = 12 vs 32 for S-GF), whichresults in a higher surface area. This difference is most likely dueto the completely different starting materials used in their preparation—commercialGF for S-GF and graphite for rGO. The commercial carbon bulk alsotested was not characterized. Table S1 summarizesthe graphene-type material’s properties.Comparing the rGO and S-GF materials, beyond theS-doping, theS-GF presents a smaller 3.222 Cleanup sorbentslike PSA, C18, and commercial carbon bulk were commonly applied inthe second step of the QuEChERS procedure.1 As the matrices are diverse and complex, some work has emerged,namely in the application of new sorbents.38 Some studies reported that commercial d-SPE, with the use of carbon,presented lower efficiency for planar compounds.39Duringthe QuEChERS procedure, different sets of dispersive sorbentswere used in several applications.4, C18, and PSA. The chromatogramsobtained with different cleanup sets are shown in 4 + PSA + C18 + carbon) is insufficientat removing interferences and during the extraction, the efficiencyof the extraction was influenced and reduced. It was observed thatchromatogram B compared with C and D shows fewer interferences, andeven most of them have disappeared, suggesting that MgSO4, PSA, C18, and a small amount of graphene-sorbents effectively removeco-extractive interferences from the matrix. As previously demonstratedby other authors, graphene and other graphene-type materials showedvery promising results, even though only studies with pesticides havebeen reported.42In the present work, the ability to remove the co-extractiveinterferences was evaluated using lower amounts of graphene-type materialsorbents together with MgSO3.2.1The use of different graphene-dispersivesorbent materials (rGOand S-GF) was evaluated during the sample preparation process. Tobetter evaluate the performance of the two types of graphene materials,the extraction efficiency was evaluated through recovery studies.The recovery values obtained for the procedure using graphene materialswere compared with those obtained using commercial carbon bulk. 28 the present graphene-type materialspresented not only good removal efficiencies of pigments (extractbecame colorless after the cleaning step with graphene compounds)in Capsicum samples but also excellentcleanup ability for other compounds of the matrix, as you can seein 3O4) as a sorbent in tobaccosamples.42 The G/PSA/Fe3O4 presented a transparent, few-layer structure by TEM and SEMwith a lower BET surface area.42 Otherauthors reported the excellent performance of a graphene nanocompositein the elimination of pigments in green chili matrices.28 However, these authors have not explored allthe morphological characteristics of the nanocomposite, so the studiescannot be compared.The graphene-dispersive sorbents showed a great improvement inthe efficiency of the extraction procedure of the target 12 BFRs.We found that graphene-dispersive sorbents did not adsorb the BFR,while commercial carbon presented some adsorption that was verifiedby the reduction in recovery. The layered structure of graphene-typematerials can explain the advantage of these materials concerningthe irregular and disordered structure of commercial carbon. As alreadyreported for graphene,3.2.2Capsicum extracts when compared with d-SPE with commercialcarbon. Significant differences were confirmed. However, no significantdifferences between the recoveries obtained using 2 or 5 mg of graphenesorbents were observed. The chromatograms obtained using 5 mg of graphenesorbents showed better purification, and that is why this was theselected amount in the optimization study (In addition to the differences in the results obtainedbetween different graphene-dispersive sorbents (S-GF and rGO), theamount of each sorbent (2 and 5 mg) was also studied. on study 1.NL = 12 vs 32 forS-GF). These properties revealed the main role in improving the purificationcapacity. Therefore, cleanup set 5 (CL5) with 5 mg rGO, 150 mg MgSO4,50 mg C18, and 50 mg PSA was chosen for further studies.In addition, rGO showed slightly better results than S-GF,whichcan be explained by its higher BET surface area and a lower stackingof graphitic layers was prepared for the establishment of the matrix-matched calibrationcurve. The analytical parameters such as linearity, LOD and LOQ, recoveries,matrix effects, precision, and uncertainty were studied (n = 3)at three spiked levels  chosen consideringthe LOQ values , thuscovering the range of LOQ values. For each level, the extraction ofBFRs in the Capsicum sample and thedeterminations were performed in triplicate under optimal experimentalconditions. The mean recoveries between 90 and 108% were achieved.The precision was evaluated through intra- and inter-day studies,and the results were less than 14% RSD. The uncertainty was also evaluatedfor the 12 BFRs and was in the range between 4.9 and 18.4%, followingthe requirement (50%) of the EU guidance document SANTE/11813/2017.3.The details of the uncertainty studies are summarized in Table S2 in Supporting Information. In termsof the matrix effect, most of the compounds show a positive effect,resulting in a signal enhancement. Signal suppression was only observedfor three compounds , with values ≤−20%, which showed a very low suppression effect. The enhancementeffect was observed for the remaining compounds and between values(19–35%). The use of these graphene-type materials in the cleaningstep allowed for moderate matrix effect values. The opposite was demonstratedby Fernandes et al.,1 who reported a muchhigher matrix effect using a classical QuEChERS extraction methodusing commercial sorbents. The method demonstrated good accuracy,precision, and robustness and can entirely comply with the detectionrequirements for BFR.The  studied 2. Good l3.4Capsicum samples that were purchasedat the local market and which were packaged in 100 g plastic containers.The samples were extracted and analyzed in triplicate. The attainedresults demonstrate the absence of PBDE in all the samples. However,PBT was quantified in two samples at a concentration of 0.71 and 0.79μg/kg. The results showed RSD between 5 and 11%. Confirmationof the presence of PBT in the two samples was achieved by GC–MSand GC/MS/MS.The optimized and validated QuEChERS-graphenedispersive sorbent-GC method was applied to the analysis of the BFRin 20 3.5Capsicum annum L. matrices, and that is why comparisonwith other works is more difficult. Table S3 presented in the Supporting Information summarizes some studieson BFR analysis in food. A comparison of the present study with theonly analytical method based on QuEChERS methodology for the extractionand determination of BFRs in Capsicum cultivars was performed,1 and the presentmethod showed significantly better recoveries for all the BFRs (90–105%)in comparison with 66–104% for the published work.1 Furthermore, the LOD (0.10–0.23 μg/kg)of the developed method was significantly lower than that of the abovementioned method (1.4 and 9.3 μg/kg).1 We can also add a comparison of this method with others performedin BFRs and red fruit samples,2 and wealso concluded that the analytical parameters  were improved in this method. Other works on different food matricesusing other extraction techniques also showed lower extraction efficiencyfor BFRs than the present method and higher RSD.43 Other studies, mostly in fish samples, are also reportedin the literature.47 Comparisons are complicated because fish and related samples presentdifferent compositions when compared with our sample (Capsicum cultivars), namely in the amount of fat.However, comparing the analytical parameters, this new approach presentedbetter values of recoveries, LOD, and LOQ.To the best of our knowledge, this is the first methodreported for the simultaneous analysis of 12 BFRs in Capsicum cultivars using a QuEChERS approach coupled with a cleanup stepadding lower amounts of graphene-type material to the dispersive sorbents.Due to the combination of the classic sorbent’s propertieswith graphene-type materials, they were capable of removing complicatedinterferences present in the Capsicum cultivars with higher effectiveness. The major potential of thismethodology is that, when compared with the classical extraction method,the studied graphene materials added an extra cleaning to the extract,showing excellent purification performance with lower amounts of sorbentsnot influencing the high extraction efficiency. We also conclude thata complete characterization of the materials used as sorbents evidencedin this work is crucial and will allow advances in the developmentof methods. rGO provided the best results and presented the lowestnumber of layers and a high BET surface area, which led to the conclusionthat these properties are the ones that allowed better performances.In this study, wepresented a novel way to cleanup the Capsicum cultivar samples. As apromising cleanup material, graphene-type materials have future applicationpotential to overcome hurdles associated with challenging matricesin detecting multiple contaminants in different samples.The results revealed that this is a simple, sensitive, quick, robust,and effective modified QuEChERS method for the detection of BFR residuesin


In this study, some features of molecular dynamics simulation for evaluating the mechanical properties of a Ni/graphene composite and analyzing the effect of incremental and dynamic tensile loading on its deformation are discussed. A new structural type of the composites is considered: graphene network (matrix) with metal nanoparticles inside. Two important factors affecting the process of uniaxial tension are studied: tension strain rate (5  The remarkable properties of crumpled graphene make it an ideal candidate for high-performance structures ,2,3 sincImprovement of the strength of metals is of high importance for the development of structural materials and carbon polymorphs are shown to be a prospective candidates for such enhancement . The firIt was shown that several important factors for superior mechanical properties of metal/graphene composites can be taken into account. For example, if the graphene flakes are agglomerates, it can decrease the mechanical characteristics of the composite ,25,26. TMolecular dynamics (MD) simulation is one of the most widely-used numerical tools in the studies of physical and mechanical properties of nanomaterials since it provides information not readily accessible experimentally. A number of different processes have been explored in carbon-metal systems by MD, including deformation and fracture ,32,33,34Different methods can be used to apply active deformation to the investigated atomic system. One of the key points is to ensure that the applied deformation scheme follows the basic concepts of continuum mechanics such as energy conservation. The chosen deformation scheme can affect the final results, especially when tension and fracture of the sample are considered, and the search for a better deformation technique is the motivation of this paper.In the present work, the methodology for MD simulation of plastic deformation of a Ni/graphene composite is presented. Two deformation schemes  and two very important parameters (strain rate and simulation temperature) are taken into account.In the present work, the CG/Ni composite system comprises 19,136 atoms, including 2 parts: the carbon matrix and metal nanoparticle fillers, which are brought together, as shown in A. Note that in previous studies [A, the precursor structure is not totally random, and very ideal with round nanoparticles and perfectly rolled graphene flakes, which is far from realistic crumpled graphene. However, the further deformation-temperature treatment allows for finding much more real configurations. The dimensions along x, y, and z directions are 100 Å, 101 Å, and 85 Å, respectively. A more detailed description of the formation of the starting configuration of the Ni/graphene composite is presented in [The starting configuration, which is not yet a composite, but just the precursor—a system of graphene flakes with Ni nanoparticles inside each flake—is shown in  studies ,40,41 itented in ,43.B. At first (A), flakes are rotated for the same angle, but after annealing GFs are crumpled in a different way, structural elements (flake with nanoparticle inside) have different shapes and sizes, and the distance between different GFs was changed during temperature treatment as well as the pore size in the structure. This process should provide the fabrication of the composite with low anisotropy. Moreover, it is known that at temperatures lower than 600 K, covalent bonds in the basal plane of graphene are very strong. Thus, even during annealing, only edge atoms of graphene flakes can interact with the edge atoms of neighboring graphene flakes. During energy minimization or annealing, only changes in the shape of graphene flakes can be observed as well as changes in the equilibrium distance between neighboring flakes.After creating the initial configuration, three different treatment routes are applied, as shown in At first , stage Ax, y, and z) is the same and equal to 61 Å. At this stage, the deformation of the structure occurs due to a decrease of the free volume (pores and voids) between the structural elements and also a slight compression of graphene flakes and Ni nanoparticles (see C). However, nickel nanoparticles do not lose their crystal structure during the pre-compression process and remain in the internal cavities of graphene flakes. During pre-compression, the sample was compressed to about 40% in the x and y directions, and to 28 % in the z direction in comparison with the starting configuration.After exposure of the sample at 300 K, an approximation to quasistatic compressive loading in each deformation increment is conducted in two steps, including (i) compression during 27 ps to eliminate the large pores in the structure and (ii) compression to the composite state. The time of compression depends on the porosity of crumpled graphene: pre-compression can be finished when neighboring GFs start to interact and stress in the system becomes non-zero. Compression to the composite state took place before maximum density is achieved in the system. The time of compression depends on the structural peculiarities. The compression rate in the loading process is 5 cles see , stage CD). This temperature is enough to facilitate the formation of new valent bonds between graphene flakes, but not enough to melt Ni nanoparticles [The second step of deformation—high-temperature compression–is applied at 1000 K to obtain the final composite state see , stage Darticles ,41. The After high-temperature compression, the structure is relaxed to minimize the internal elastic stresses that appear during the preparation of the Ni/graphene composite. It should be noted that in early works ,41,42,43To apply the load to the system, boundaries of the simulation cell are moved with the given strain rate, which is the displacement-control method. In this work, two types of a tensile load of a Ni/graphene composite are considered: dynamic and incremental uniaxial tension . For theease see .x-direction is performed to apply two deformation mechanisms. A constant engineering strain rate of two values, 5 n leads to the same increase in the time of deformation of the composite. For example, the incremental deformation time The strain-controlled uniaxial tension in the The simulation model was developed based on the Adaptive Intermolecular Reactive Empirical Bond Order (AIREBO) for the carbon system  and the The functions that describe the interactions between Ni and C atoms are referred to the Morse potential with the parameters C system ,55. The ained in . The potained in ,41,54. Hained in ,58.MD simulations are conducted using the isothermal-isobaric (NPT) ensemble via a Nose–Hoover thermostat. The equations of motion were solved using a velocity-Verlet algorithm with a time step of 1.0 fs. Periodic boundary conditions are applied along all three directions. During uniaxial tension the volume of the simulation cell remains constant, i.e., as the length of the composite increases in the one direction, its size is correspondingly and anisotropically decreased in the other two directions to maintain the total volume of the system.An increase in the exposure time during the creation of the composite (from 20 ps to 200 ps) did not change the obtained results. Stress–strain curves are obtained by carrying out at least two modeling processes for each composite’s deformation mode and structural state. The simulation time for dynamic tension at a strain rate The snapshots of the MD results are processed by the Visual Molecular Dynamics package . This isFirst, the effect of the tension strain rate on the mechanical properties of the fabricated Ni/graphene composite is considered. The system temperature for these simulations is maintained at 0 K. Before uniaxial tension is applied, the structure is relaxed to minimize internal stresses.ates see . For ε>0gher see , but the0.74 see c. At thasite see b,c. HoweIIn εUTS see . The eloture see b,c. The The effect of temperature on the mechanical properties of Ni/graphene composite is studied for both methods of uniaxial tension at a strain rate of In ture see a, while This deformation mechanism is not observed at 0 K, since thermal fluctuations of atoms play a key role in the formation of long carbon chains. At 300 K, the hexagonal graphene lattice is easily transformed to form long covalent carbon chains. Carbon chains are also formed during low-temperature tension of the composite, but chains are shorter than at 300 K see b,c, and aterials ,63,64. NThe ultimate tensile stress and strain of the composite decreased with temperature increase, which is associated with a decrease in the strength of interatomic interaction due to thermal fluctuations of atoms. The resulting carbon chains under uniaxial tension b,c are dgate see b,c.The material properties can considerably depend on the direction in which these properties are measured, which is anisotropy. As it was shown for Ni/graphene composite, fabrication of the composite with a more homogeneous structure enhanced mechanical properties . The strx-axis, however, plasticity under these deformation conditions is the lowest, while when stretched along the y-axis, the composite has the maximum plasticity. At the same time, at the strain x and z-axes, the deformation occurs as a result of the formation of long carbon chains with their subsequent fracture. Such a difference in the strength of the composite is primarily associated with the initial structure of the Ni/graphene composite  of the obtained composite are 219 GPa  give close results on ultimate tensile strength and Young’s modulus. The formation of long carbon chains occurs faster under incremental uniaxial tension, and therefore the composite is broken faster. It can be concluded that the chosen strain rate should be reasonably small to obtain physically correct results, but not too small, which enlarges the simulation time. For such composite material, a strain rate of A simulation approach to study Ni/graphene composite under uniaxial tension at different simulation conditions by means of MD has been presented. Incremental and dynamic tension techniques, strain rate, and simulation temperature are chosen as the key factors in order to obtain a satisfactory simulation methodology for the simulation of metal/graphene composites. It is shown that a decrease in strain rate, as well as simulation at room temperature for such structures, can give lower ultimate tensile strength under uniaxial tension since it allows for slower structural transformations. However, dynamic and incremental mechanisms of tension lead to similar mechanical behavior, though the incremental regime of the simulation is much longer.Overall, the strengthening of the composite with a graphene network composed of graphene flakes is shown. The route for composite fabrication plays a key role in the formation of the composite with low anisotropy. The considerable difference in the composite strength under uniaxial tension along three directions is shown. However, in  it was s


In view of the good adsorption properties of graphene and carbon foam, they were combined to achieve the optimal matching of microstructures. Taking mesophase pitch as a raw material, pitch-based carbon foam was prepared by the self-foaming method. Graphene gel was prepared as the second phase to composite with the carbon foam matrix; graphene-modified, pitch-based carbon foam composites were finally obtained. Graphene gel was dispersed in the rich pore structure of carbon foam to improve its agglomeration and the porosity, and the active sites of the composite were further increased; the adsorption properties and mechanical properties of the composites were also significantly improved. The microstructure and morphology of the composites were studied by SEM, XRD and Raman spectroscopy; the compressive property and porosity were also tested. Methylene blue (MB) solution was used to simulate a dye solution for the adsorption test, and the influence of the composite properties and MB solution on the adsorption property was studied. Results showed that the compressive strength of the composite was 13.5 MPa, increased by 53.41%, and the porosity was 58.14%, increased by 24.15%, when compared to raw carbon foam. When the mass of the adsorbent was 150 mg, the initial concentration of the MB solution was 5 mg/L, and the pH value of the MB solution was 11; the graphene-modified carbon foam composites showed the best adsorption effect, with an adsorption rate of 96.3% and an adsorption capacity of 144.45 mg/g. Compared with the raw carbon foam, the adsorption rate and adsorption capacity of the composites were increased by 158.18% and 93.50%, respectively. With the continuous maturity of industrialization and economic development, the pollution of air, water and soil waste has become increasingly serious, especially water pollution, which has caused great impact on the human living environment and has become a key problem to be solved . Accordi2/g, and the carbon dioxide adsorption capacity is 12 mol/kg. Feng et al. [Graphene is an excellent adsorption material due to its large specific surface area and unique, two-dimensional lamellar structure ,25. The g et al.  preparedg et al.  preparedBased on the broad application potential of graphene and carbon foam in the adsorption field, and taking full advantage of their adsorption properties, graphene-modified, pitch-based carbon foam composites were synthesized in this study. Using mesophase pitch as a raw material, the self-foaming method was adopted to prepare carbon foam, and graphene gel was prepared as the second phase to composite with the carbon foam matrix. On the basis of the good adsorption performance of carbon foam, by increasing the material porosity and the number of surface active sites, graphene gel was dispersed in the rich pore structure of carbon foam to improve its agglomeration, which significantly improved the adsorption performance and mechanical properties of the composite and formed an optimized adsorption system. Test results showed that, when the adsorbent mass was 150 mg, the initial methylene blue (MB) concentration was 5 mg/L, and the pH value of MB solution was 11; the prepared graphene-modified, pitch-based carbon foam composite showed an adsorption rate of 96.32% and an adsorption capacity of 144.45 mg/g for MB, which reveals a wide application prospect in the field of water pollution treatment.Coal tar pitch as carbon foam precursor was purchased from Koppers Carbon & Chemical Co., Ltd. ; flake graphite as the raw material of graphene was purchased from Nanjing XFNANO Materials Tech Co., Ltd. .3, added 50 mL of concentrated sulfuric acid into a beaker containing a mixture of flake graphite and NaNO3 and stirred it magnetically in a water bath for 30 min. Then, we added 6 g of KMnO4 slowly into the beaker, put the solution in water bath at 35 °C for 1 h, added 80 mL of distilled water slowly and heated and stirred continually at 95 °C for 0.5 h. Then, we added 80 mL of distilled water when the water bath decreased to room temperature and added 6 mL of H2O2 after stirring for 15 min; the solution turned bright yellow, and we adjusted the pH value of the solution to 7. Then, graphene oxide solution was obtained. We added L-cysteine to the graphene oxide solution and treated it ultrasonically for 2 h; the mass ratio of graphene to L-cysteine varied from 0.5 to 2.0 and then we put the solution in a water bath at 95 °C for 1 h to obtain graphene gel.Graphene oxide (GO) was obtained by modified Hummers’ methods. First, we took 1 g of flake graphite and 1 g of NaNOCarbon foam was prepared from coal tar pitch by self-foaming method. First, coal tar pitch was thermally condensed to prepare mesophase pitch. An 8 g amount of ball-milled mesophase pitch was placed in a high-pressure reactor vessel, filled with nitrogen to 1.5 MPa, heated to 300 °C for 1 h and then the temperature was raised to 480 °C for 4 h. After that, the sample was taken out for carbonization. We placed the sample in the tubular furnace, with argon as the protective gas with ventilation rate 0.2 L/min, heated it to 600 °C for 1 h and then raised the temperature to 900 °C for 2 h. After this program was finished, the tubular furnace was cooled naturally to room temperature to obtain carbon foam material.Carbon foam was cut into 50 mg, 75 mg, 100 mg, 125 mg and 150 mg samples; after ultrasonic cleaning, these samples were immersed into graphene gel for 48 h and then put in a vacuum drying oven at 70 °C for 48 h to obtain graphene-modified carbon foam composites.Scanning electron microscopy  was used to analyze the morphology and microstructure of the composite. The crystal structure of the samples was characterized by X-ray diffractometer with Cu target . A Raman spectrometer  was used to characterize the microstructure and defects of samples.The universal testing machine  was used to test the mechanical properties of graphene-modified carbon foam composites. First, the composite materials were cut into small samples with the dimension 10 mm × 10 mm × 10 mm; these samples were compressed between two stainless platens with a loading rate of 0.5 mm/min, which is widely adopted in the compression strength testing of carbon foam ,33, and The bulk density of graphene-modified carbon foam composites was measured according to the Archimedes method. The porosity was calculated as follows:2 = 0.98228.In this paper, MB solution was taken as the target pollutant to test the adsorption performance of the graphene-modified carbon foam composites. First, the standard concentration curve of the MB solution needed to be calibrated. According to the measurement results of a double-beam ultraviolet–visible photometer, the maximum absorption wavelength of MB is 666 nm. MB solution with concentration 1–5 mg/L and concentration gradient 1 mg/L was prepared, and the corresponding absorbance was measured, respectively. The standard working curve of MB solution with MB concentration as the abscissa and absorbance as the ordinate was drawn, as shown in t (tq), the final adsorption capacity (eq) and the removal rate (R) of composite were calculated as follows:V is the volume of MB solution (L) and m is the mass of the adsorbent (mg).Then, we measured the absorbance of MB with graphene-modified carbon foam composites as adsorbent and calculated the concentration of MB according to the standard working curve of the MB solution; the change of dye concentration was obtained and then the adsorption capacity at time The SEM images of graphene-modified carbon foam composites are shown in Crystallographic analysis was carried out on the raw carbon foam and graphene-modified carbon foam composites, respectively, and the results are shown in 2 hybrid plane. The 2D peak also represents the degree of atomic confusion. The stacking degree of graphene can be determined by the height of the 2D peak. In addition, the strength ratio of the D peak and G peak, R = ID/IG, represents the defect degree and graphitization degree of the material. The larger the R value, the more defects of the material and the worse the structural order.The Raman spectrum of raw carbon foam and graphene-modified carbon foam composites is shown in −1, 1570 cm−1 and 2700 cm−1. The D and G peaks of the composite were significantly higher than those of raw carbon foam, while the 2D peak of composites showed a broad peak shape, indicating that the graphene prepared was multilayer. In addition, the R value of the raw carbon foam was 0.98, while the R value of the composite reached 1.15, indicating that the prepared graphene-modified carbon foam composite had more defects, because the graphene had not been fully reduced, and the carbon foam had not been graphitized. These large numbers of defects provided more adsorption active sites, which were conducive to improving the adsorption performance of the composites. It can be seen from It should be emphasized that the adsorption rate and adsorption capacity of raw carbon foam were 37.33% and 74.65 mg/g, respectively, so the addition of graphene gel effectively improved the adsorption performance of the composites. Compared with raw carbon foam, when the graphene-to-L-cysteine ratio was 0.5, the maximum adsorption rate and adsorption capacity of the modified composites reached 93.71% and 93.47 mg/g, increased by 151% and 25%, respectively. This is because, at the stage when the ratio was 0.5–0.6, the specific surface area of carbon foam was significantly increased by adding graphene gel, and there were many surface active sites of graphene gel which provided more adsorption sites for MB molecules, so the adsorption rate and adsorption capacity were significantly improved. When the ratio reached 0.6, the graphene gel content reached saturation. In the second stage, with the increase in the graphene content, the graphene folds covered each other due to the stacking of multiple layers of graphene. Moreover, too much graphene gel covered the through-hole of carbon foam, which was not conducive to the increase in the specific surface area of the composites, thus, gradually reducing the adsorption rate and adsorption capacity. The samples of graphene-modified carbon foam composites with masses of 50 mg, 75 mg, 100 mg, 125 mg and 150 mg were used for the adsorption test on the MB solution with concentration of 5 mg/L; the adsorption rate and adsorption capacity of the samples were calculated subsequently. The testing results are shown in We immersed 150 mg of composites in MB solution with different initial concentrations ; after the adsorption equilibrium state was achieved, the relationship between MB solution concentration and adsorption rate/adsorption capacity was calculated, as shown in It can be inferred that the higher the concentration of MB solution, the more MB molecules. Hence, more MB molecules can contact the sample surface and be captured. Finally, on the premise of the same sample mass, the higher the concentration of MB solution, the more MB molecules are adsorbed on the sample surface, so the adsorption capacity increases. On the other hand, although the number of adsorbed MB molecular increases, the MB solution concentration also increases in a larger amount, so the adsorption rate of the composite to MB solution is reduced.+ in the solution was ionized and attached to the surface of the composites, which made the sample have positive electricity. Because MB is a cationic dye, there was a competitive adsorption phenomenon between ionized H+ and MB molecules in the solution. Therefore, the adsorption of the MB molecules on the composites was reduced, and the stronger the acidity, the lower the adsorption of the MB molecules. On the contrary, when the solution was alkaline, a large number of OH− ions in the solution were ionized, which attached to the sample surface to make the composites have negative electricity. OH− ions on the sample surface adsorbed cationic dyes to the surface of the sample through electrostatic action. With the increase in pH value in the MB solution, more OH− ions were ionized from the solution; thus, more OH− ions attached to the surface of the composites. As a result, more MB molecules were adsorbed by the composites, which led to the improvement of the adsorption performance. Compared with raw carbon foam, when the pH value of the MB solution was 11, the maximum adsorption rate and adsorption capacity of the modified composites increased by 158.18% and 93.50%, respectively.We immersed 150 mg of composites in MB solution with concentration of 5 mg/L and pH values of 3, 5, 7, 9 and 11, respectively. The relationship between the pH value of the MB solution and the adsorption rate/adsorption capacity is shown in In this paper, graphene-gel-modified, pitch-based carbon foam composites were synthesized. The microstructure and morphology of the composites were studied by SEM, XRD and Raman spectroscopy; the compressive property and porosity were also tested. The adsorption test was carried out using MB solution to simulate a dye solution. The influence of the composite properties and MB solution on the adsorption performance was investigated. Through the microstructure characterization of the composites, it was found that the surface of the carbon foam was fully covered by graphene, and some graphene oxide was not completely reduced. Raman spectroscopy showed that the graphene gel on the surface of the composite had a multilayer graphene structure with many edge defects, which provided sufficient active adsorption sites and helped to increase the adsorption performance of the composites. With the increase in the mass ratio of graphene to L-cysteine, the compressive properties of the composites also increased. When the mass ratio of graphene to L-cysteine was 2.0, the compressive strength of the composites reached 13.5 MPa, increased by 53.41%. Results showed that the porosity of the composite after graphene modification was 58.14%, which was 24.15% higher than that of raw carbon foam. According to the analysis of the adsorption performance, when the mass of the adsorbent was 150 mg, the initial concentration of the MB solution was 5 mg/L, and the pH value of the MB solution was 11; the composites showed the best adsorption effect. In this case, the adsorption rate was 96.3%, increased by 158.18%, and the adsorption capacity was 144.45 mg/g, increased by 93.50%.


It is aructures . FurtherDan and Rick). The minimum electron dose for reducing the high-resolution limit determined by SPA was tenfold higher than that measured by MicroED. The authors also propose strategies for collecting high-resolution data using SPA and MicroED.Radiation damage is one factor that limits the resolution of three-dimensional structures of biological specimens when using cryo-EM. In their contribution to the collection, Shi and Huang thoroughly compare radiation damage assessments using single particle analysis (SPA) and micro-crystal electron diffraction (MicroED) (Hongcheng and Fei).Cryo-EM specimen preparation is the current bottleneck that impedes the broader use of cryo-EM, and many groups have been working hard to improve this. Common issues with the vitrification process include poor/non-uniform distribution of protein molecules (particles), preferred orientation, protein denaturation/degradation at the water-air interface, and high background noise from thick ice. One of the techniques developed to improve cryo-EM specimen preparation is the use of support films made of graphene and its derivatives. In their review, Fan and Sun discuss the advantages of graphene grids over conventional holey carbon film grids, the functionalization of graphene support films, how to make graphene grids, and the origins of pristine graphene contamination (Ilyes and Slavica), referred to as DeepHEMNMA, that speeds up the original method by combining it with a residual neural network (ResNet)-based deep-learning approach. The authors demonstrate the performance of DeepHEMNMA using synthetic and experimental data. Two-dimensional classification has played an important role in getting rid of junk particles from large datasets, and in sorting out the conformational heterogeneity of proteins/protein complexes in a dataset. However, the process can take a long time to complete if only central processing units (CPU) are used, especially for large datasets or large box sizes, which are common in today’s cryo-EM environment. Fabian et al. presented a graphics processing unit (GPU)-accelerated version of iterative stable alignment and clustering (ISAC) .Conformational heterogeneity of a biological molecule is a prerequisite for it to be able to perform its functions. In cryo-EM, specimen heterogeneity not only presents the challenge of obtaining high-resolution structure but also provides the opportunity to determine multiple structures of the same molecule/complex, with different conformations, from the same dataset, allowing investigation of the conformational changes associated with its biological functions. Hybrid electron microscopy normal mode analysis (HEMNMA) was first developed in 2014  to analyg (ISAC)  that enaPaula).Cryo-electron tomography (cryo-ET) has drawn much attention in recent years, and people believe it has great potential in the fields of cellular and structural biology. In this regard, Paula Navarro has provided an overview of current hardware and software developments that allow quantitative cryo-ET studies, and discussed the limitations of cryo-ET and how to overcome them to unleash its full power .Despite the wide applications of cryo-EM, which are due, especially, to its potential to revolutionize structural biology and new drug development, the high monetary and human resource costs involved with establishing and maintaining a high-end cryo-EM facility limit its accessibility. As a result, governments, universities, research institutes, and pharmaceutical companies around the globe have established high-end cryo-EM centers that provide access to researchers for free or at a reduced cost . How the


Contextual mobile advertising, with the advantages of high interactivity and immersive experience, is the mainstream trend of future Internet advertising. Current studies have explored the benefits of contextual mobile advertising while lacking the analysis of contextual mobile advertising factors on consumer purchase intentions. This study investigates the mechanisms by which the characteristics of contextual mobile advertising evoke consumers' purchase intentions through advertising attitudes to reveal how extroversion and neuroticism in personal traits moderate the relationship between characteristics of contextual mobile advertising and advertising attitudes. Based on a sample of 543 community residents with mobile shopping experience in China, this study uses structural equation modeling to validate the relationships between the variables and draws conclusions. The findings help advertisers to grasp the important characteristics of contextual mobile advertising, improve consumers' attitudes toward advertising, and enhance purchase intentions. Furthermore, it is possible to expand perceptions of the effectiveness of contextual mobile advertising among consumers with different personality traits. With the rapid development of the Chinese mobile Internet, the size of mobile Internet advertising continues to climb in the percentage of overall Internet advertising. As reported by iResearch in 2020, in China, mobile advertising accounted for 87.7% of all Internet advertisingIt is worth noting that if companies want to change consumers' advertising attitudes through the presentation of contextual mobile advertising, to arouse more purchase intention and promote the sales of their products or services, they should study what characteristics of contextual mobile advertising have such an effect. At present, scholars do not give a clear definition of contextual mobile advertising. Yuan and Tsao  first meIn addition, a potential factor that cannot be ignored in the process of the important characteristics of contextual mobile advertising influencing advertising attitudes is personality traits. The previous studies of personality traits can help us deeply understand the differences in consumers' attitudes. Different consumers have different advertising preferences, two people will not have the same emotional reactions and attitudes toward advertising; they often respond most positively when advertising matches their personality traits, developing positive advertising attitudes  were recruited from Chinese community residents with a mobile shopping experience. In this study, the answer to the questionnaire with missing data was not submitted successfully, so there were no missing data. In addition, we screened out the data of extroverts and neurotics from 1,473 respondents through the personality traits test, which was used in the final analysis of this study. The final valid sample (n = 543) consisted of 262 females (48.25%) and 281 males (51.75%). The sample had a mean age of 24.72 years (range 18–45). Education was assessed through four options, and participants were asked to check a box to make a choice: 11.42% selected “High school or below,” 9.39% selected “Associate degree,” 46.78% selected “Bachelor degree,” 32.41% selected “Graduate degree or above,” and no one left the question blank. In addition, among the final valid sample (n = 543), 295 samples were extroverts and 248 samples were neurotics after the personality traits test. Among them, 152 samples were high extroverts based on 1sd above the mean, and 116 samples were high neurotics based on 1sd above the mean  purchase intention of products shown in Facebook advertising  were significantly poorer than those of the four-factor model  suggesting that common method bias is not a serious concern in our data set.The existence of common method bias in the data set was tested by using the Harman's one-factor test. The items of all four factors  were combined into a single factor and compared with that of the four-factor model. The goodness of fit indices of the one-factor model , advertising attitudes, purchase intention, and personality traits (extroversion and neuroticism) were collected at one time, therefore, it was necessary to conduct Confirmatory Factor Analysis (CFA) to compare different models. The Cronbach's alpha and CFA of the scales were calculated by Mplus 7.0.2 = 436, df = 203, CFI = 0.93, GFI = 0.92, IFI = 0.94, RMSEA = 0.05, SRMR = 0.04) as compared to the alternative models  was estimated in this study. Another model (three-factor) was also estimated, in which contextual mobile advertising characteristics were combined on one-factor, advertising attitudes and purchase intention were combined on one-factor, extroversion and neuroticism were combined on another factor. The fit indices of these alternative models were weak compared to the six-factor model, providing support for the distinctiveness of the model used in this study. The CFA results revealed that the six-factor structure provided a better fit , thus supporting H1. Content accuracy also positively influenced advertising attitudes , supporting H2. Advertising attitudes were shown to have positive effects on purchase intention , supporting H3. In conclusion, this study identified contextual interaction and content accuracy as important characteristics that motivate consumers to be attracted by contextual mobile advertising. In addition, these two characteristics of contextual mobile advertising are stimulated by positive advertising attitudes, which also effectively increase purchase intention.The model fit indices revealed that the model had a good fit with the Comparative Fit Index (CFI) = 0.91, Incremental Fit Index (IFI) = 0.91, and Standard Root Mean Square Residual (SRMR) = 0.04, and Root Mean Square Error of Approximation (RMSEA) = 0.05. The results of the structural model are presented in To test moderation hypotheses, a two-way moderated regression was selected , and the direct effect of content accuracy was significant on advertising attitudes . Step 3 shows the result of adding the interaction term . As shown in p < 0.01; β = 0.203, p < 0.01). This analysis produced a significant main effect and a significant interaction effect ; when high extroversion (High EX) (above one standard deviation), the positive relationship between contextual interaction (CI) and advertising attitudes (AA) is significant , that is, high extroversion is more sensitive to the changes of contextual interaction. Thus, those high in extroversion will show more positive advertising attitudes toward contextual mobile advertisements with high contextual interaction. Hypothesis 4a is supported.To further reveal the interaction effect, a simple slope test was performed. As shown in ns.); when low extroversion (Low EX) (below one standard deviation), the positive relationship between content accuracy (CA) and advertising attitudes (AA) is significant , that is, low extroversion is more sensitive to the changes of content accuracy. Hypothesis 4b is not supported.As shown in p < 0.01), and the direct effect of content accuracy was significant on advertising attitudes . Step 3 shows the result of adding the interaction term . As shown in p < 0.01; β = 0.221, p < 0.01). This analysis produced a significant main effect and a significant interaction effect . When there is high neuroticism (High NE) (above one standard deviation), the positive relationship between contextual interaction (CI) and advertising attitudes (AA) is significant , that is, high neuroticism is more sensitive to the changes of contextual interaction. Thus, those high in neuroticism will show more positive advertising attitudes toward contextual mobile advertisements with high contextual interaction. Hypothesis 5a is supported.To further reveal the interaction effect, a simple slope test was performed. As shown in p < 0.001); when there is high neuroticism (High NE) (above one standard deviation), the positive relationship between content accuracy (CA) and advertising attitudes (AA) is also significant , that is, high neuroticism can promote the relationship of content accuracy and advertising attitudes. Low neuroticism has a weak moderating effect on the relationship between content accuracy and advertising attitudes. Thus, those high in neuroticism will show more positive advertising attitudes toward contextual mobile advertisements with high content accuracy. Hypothesis 5b is supported.As shown in The main purpose of this study was to investigate which characteristics of contextual mobile advertising evoke consumers' purchase intentions through advertising attitudes among community residents with the online shopping experience in China and to discuss how extroversion and neuroticism in personal traits moderate the relationship between significant characteristics of contextual mobile advertising and advertising attitudes. Through an extensive review of the prior literature and the status and issues in the development of contextual mobile advertising, this study concludes that contextual interaction and content accuracy can serve as stimuli that trigger positive consumer attitudes toward contextual mobile advertising and increase consumers' purchase intentions. Thus, contextual mobile advertising, featuring strong interactivity and high precision, is much easier to evoke consumer emotions and change consumer attitudes. This study further discussed the differences in perceptions and attitudes toward contextual mobile advertising characteristics between groups with extroversion traits and neuroticism traits. The findings showed that high extroversion and high neuroticism are more sensitive to the changes in contextual interaction, and low extroversion is more sensitive to the changes in content accuracy. High neuroticism can promote the relationship between content accuracy and advertising attitudes. Low neuroticism has a weak moderating effect on the relationship between content accuracy and advertising attitudes.From the theoretical perspective, the influence of contextual mobile advertising characteristics on the purchase intention in this study is mainly based on two theories: First, the scene theory. Scene theory combines “scene” and “media,” and believes that the approach of new media will generate new scenes with fresh behaviors. With the booming of mobile Internet, the scene theory has been given new connotations, such as mobile devices, social media, big data, sensors, and location-based systems. These five technologies are closely related to scenario communication, and in turn, promote the communication effect. Based on the scene theory, this paper explored the mechanisms affecting consumers' advertising attitudes from the perspectives of contextual interaction and content accuracy, and then examined the influence of advertising attitudes on the purchase intention. Second, according to the Planned Behavior Theory, attitude is one of the essential factors influencing consumer behavior. Attitude is often considered an indicator in the study of mobile advertising effectiveness. Contextual mobile advertising characteristics not only reduce consumers' aversion and increase positive advertising attitudes but also induce consumers' purchase intentions and purchase behaviors. This study found that contextual mobile advertising characteristics play an important role in the practical application of stimulating advertising attitudes, and how to maximize consumer satisfaction through contextual stimulation would become a hot issue for future research in the domain of contextual mobile advertising. Meanwhile, this study explores how to maximize the communication value of advertising by enhancing contextual interaction and content accuracy. This study not only extends the research on mobile advertising but also further expands the antecedent variables of advertising attitudes. In addition, this study examines the moderating effects of extroversion traits and neuroticism traits on contextual mobile advertising characteristics and advertising attitudes from personality trait theory. From the practical perspective, the application of contextual mobile advertising is conditional and may affect individuals with different personality traits differently.Overall, this study broadens the horizons of future research in exploring and studying the individual differences in contextual mobile advertising audiences. Contextual mobile advertising, rooted in location-based, big data, social media, and other technologies to achieve the accurate delivery of advertising, is changing the traditional bombing advertising patterns to reduce consumer advertising avoidance. Not only can contextual mobile advertising take consumers' current time, space, and psychological state into consideration entirely and cater to consumers' needs, but achieve higher advertising click rate, conversions, and higher ROI index of enterprises. Therefore, these findings can help advertisers improve the delivery efficiency of contextual mobile advertising and provide more personalized advertising recommendations to consumers  Cross-border E-commerce College (No. 2021KXYJ06), Philosophy and Social Science Foundation of Zhejiang Province (21NDJC083YB), National Natural Science Foundation of China (71702164), Natural Science Foundation of Zhejiang Province (LY20G010001), and the Contemporary Business & Trade Research Center of Zhejiang Gongshang University .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Carbon-based nanomaterials are currently attracting a lot of interest in many fields, ranging from medicine and biotechnology to electronics, energy storage, and sensing applications ,2. They One characteristic of all carbon nanomaterials is the possibility of functionalizing them through non-covalent and covalent methods ,7, whichWith an enlarged global focus on tackling the challenges of environmental pollution, the interest in novel energy devices as an alternative to petroleum-based ones has also increased. In this regard, supercapacitors can be designed for use in environmentally friendly vehicles and new renewable energy; however, the limitation of a low energy density remains a challenge . To furtHierarchical porous activated carbon (HPAC) is another interesting active material for supercapacitors due to its huge specific surface are. Preparing electrodes with high-mass loading is interesting in providing high total capacitances and gravimetric or volumetric energy densities . TherefoIn another study, a novel carbonaceous material was prepared from cellulose carbonized via two-steps—hydrothermal and thermal carbonization, in order—without any chemicals , giving 2+ detection, leading to a linear concentration range of 0–250 nM, with a limit of detection of 3.93 nM, and it was successfully applied to real samples of pond water, leading to recoveries in the range from 99.6% to 101.1%.GO is well known for its outstanding fluorescence quenching capability. Sun et al.  preparedBased on the fluorescence quenching ability of nitrogen- and phosphorous-doped carbon dots, a simple and selective sensor for glutathione detection was also developed . The redNitrogen-doped amino acid-functionalized GQDs show enhanced photoluminescence and photostability and lead to the generation of reactive oxygen species through two-photon photodynamic therapy (PDT) . This amNitrogen oxides (NOx) are amongst the foremost atmospheric pollutants; hence, it is imperative to screen and detect their presence in the atmosphere. For such a purpose, low-dimensional carbon structures have been broadly used as NOx sensors. In particular, CNTs have been applied for sensing toxic gases due to their high specific surface area and excellent mechanical properties. Even though pristine CNTs have shown promising performance for NOx detection, several strategies have been developed such as surface functionalization and defect engineering to expand the NOx-sensing ability of pristine CNT-based sensors. In this regard, the surface modification approaches used in the recent decade to modify the sensitivity and the selectivity of CNTs to NOx have recently been reviewed . 2, CH4, and O3. Canales et al. [30 for the adsorption of these pollutants. They performed computational simulations to investigate their adsorption on graphene-semifullerene (C30) surfaces, considering two C30 geometries—hexagonal and pentagonal bases—and found that it is possible to dope all surfaces with Li, Ti, and Pt, which can be used as effective catalysts.Other atmospheric contaminants that threaten the environment and life include CO, COs et al.  have expOn the other hand, given that the control over radioactive species is currently critical, the development of functional materials for the decontamination of radioactive substances has also become imperative. In this regard, Bae and coworkers  have recStudying acoustic plasmons (APs) in single-layer, double-layer, and multilayer graphene or in metal/dielectric/graphene superstructures is another active field of research. Although the mechanism of the formation of these plasmons in electrostatically biased graphene or at noble metal surfaces is well known, the mechanism of their formation in alkali-doped graphene is not well understood yet. In this regard, Marušic and coworkers have investigated the interplay of the p and s intraband transitions with plasmon resonance . Their wOn the other hand, the interaction between photons and polarization modes can result in the formation of hybrid photon polarization modes, called polaritons. The same authors  have shoAs known, graphene is a versatile compound with many outstanding properties, providing a combination of a huge surface area, a high strength, and thermal and electrical properties, with a wide array of functionalization possibilities. However, the available literature on graphene-based coatings in dentistry and medical implant technology is scarce. Srimaneepong and coworkers  have rec