Knowledge, once acquired, degrades over time. Exams that contain questions related to previously acquired knowledge  may promote retrieval practice and spaced learning, and subsequently prevent knowledge loss. To investigate this hypothesis, we compare the score of retrieval practice questions to regular questions in exams of a two-year (bio)medical study program.RiT-values), the impact of retrieval practice questions on the internal consistency of exams was determined. Mixed model analyses determined changes in outcomes across time.The two-year “Mechanisms of Health and Disease”-program for biomedical sciences and medical students in Nijmegen (the Netherlands) contains 14 spaced exams of 80 questions each. The percentages of correct-, false-, and non-answers were compared between regular questions and retrieval practice questions. Using Pearson correlations between question scores and exam scores (P < 0.05). Scores did not change across time. RiT-values were slightly lower in retrieval practice questions, with a small inverse trend across time.Analysis of 2006 regular questions and 1728 retrieval practice questions revealed a significantly higher percentage of correct and false answers, and a significantly lower percentage of non-answers, in retrieval practice questions versus regular questions (all RiT-values of retrieval practice questions were slightly lower than those of regular questions, the discriminative capacity was well within acceptable range. These data highlight the potency of retrieval practice questions to prevent knowledge decrement, without altering exam quality.Our data indicate preservation of knowledge, possibly related to retrieval practice and/or spaced learning. Although the The online version contains supplementary material available at 10.1186/s12909-021-03075-y. One of the main goals of education is the retention of (newly attained) knowledge. An effective way to achieve knowledge retention is repetitively reviewing study material on multiple occasions , 2. ExamExamination stimulates students to recall and thus utilize knowledge, where recalling knowledge in itself improves the retrieval of the same material in a later retrieval attempt , 4, 5. RThe abovementioned suggests that strategies that promote retrieval practice and spaced learning may be helpful to improve knowledge retention in medical and biomedical sciences students. Recalling knowledge represents an approach that can be actively controlled and influenced by teachers, ultimately leading to improved long-term knowledge retention for students . The impSince 2015, the medical faculty of Radboud University  implements repeated retrieval practice in the examination of a two-year educational program called “Mechanisms of Health and Disease” (MHD). MHD covers basic scientific and medical principles built from six disciplines: anatomy, psychology, physiology, cell biology, biochemistry and genetics. The program is divided into eight periods, containing one or two exams each. About half of these exams’ questions relate to knowledge obtained in the current period (regular questions (RQs)), whilst the other half relates to knowledge obtained in previous periods ). Consecutive exams can contain RPQs related to the same knowledge. Hence, certain knowledge may be examined multiple times . Anecdotally, annual evaluation cycles indicate that students deliberately re-study course material from previous periods, implying that students adopt a study strategy with increased “spaced” learning. RPQs may have altered students’ learning behavior.RiT-score  to assess the discriminative capacity of RPQs and RQs. We hypothesize that the RPQs-score does not significantly attenuate across consecutive repetitions, whilst the discriminative capacity of RPQs does not attenuate compared to RQs and across number of repetitions. Accordingly, this work will provide relevant insight into the potential impact of RPQs on assessment quality and its role in the preservation of knowledge.Therefore, the main purpose of our study is to evaluate whether knowledge obtained in the current period (assessed with RQs) alters across subsequent periods when knowledge is recalled through RPQs. Specifically, this retrospective cohort study aims to compare RPQ-scores (across multiple repetitions) to scores of RQs, obtained by medical students and biomedical sciences students, when following the Radboud university medical faculty’s MHD program. Secondly, we aim to determine the effect of RPQs on exam quality by comparing the contribution of RPQs (across multiple repetitions) versus RQs to the internal consistency of exams. This will be done by using the In this retrospective cohort study, we analysed exam results of medical students and biomedical sciences students. Data collection commenced in September 2015 and continued until March 2020.All questions were grouped based on the course they refer to and received the label regular questions (RQs) or retrieval practice questions (RPQs). Whether a question was labelled as RQ or RPQ depended on whether it was the first time a certain topic was examined. For example, from a pool of 50 questions all related to the same topic, three questions were randomly added in consecutive exams. The first time that these questions appeared in an exam, the students attended the course related to that topic the weeks before. These questions were labelled as RQs, meaning that it was the first time that the topic was examined. When the students received questions related to that same topic in subsequent exams, the questions were labelled RPQs. Each question can only be drawn from the question pool once, meaning that each exam is unique and contains different questions.N of repetitions” under “outcome measures”), fluctuations in students’ knowledge levels were mapped.Additionally, By establishing how many times a certain course has been examined  from 51 exams (80 questions per exam)  of 10 weeks. Exams involve both RQs and RPQs and take place twice in every period with exception of P7 and P8, where examination occurs once and involves solely RPQs. The examined knowledge is cumulative, meaning that questions related to newly taught courses enter the exams as the program continues, while questions related to course material from past courses keep re-appearing. All exams used in the present study are original exams administered between September 2016 and March 2020All parameters below represent information related to individual questions. The outcomes originate from the standard item analysis and are available for each question included in our database.%Cor indicates the percentage of correct question responses and is calculated by 100 * [N of correct answers] / [N of all participating students]. This parameter was used as the indicator of students’ knowledge level. Over-time, changes in %Cor reflect changes in the amount of retrievable knowledge (i.e. knowledge preservation).N of answer options] – 1). Consequently, students may prefer to not answer a question. The percentage of non-answers (%Open) and the percentage of false answer  are reported separately. Their calculation is congruent to %Cor, with %Open = 100 * [N of non-answers] / [N of all participating students] and %False = 100 * [N of false answers] / [N of all participating students].Within the calculation of the score for the exam, formula scoring is adopted. This procedure is designed to reduce multiple-choice test score irregularities due to guessing. A formula score is obtained by subtracting a predefined score for each incorrect answer, which equals 1 / ([RiT) of a question refers to the calculation of the Pearson correlation between the scores for a specific question versus the total scores of the exam, and indicates the grade of adherence to the quality requirements mentioned above. In other words, a high (positive) RiT indicates that a high score on the individual question is strongly and positively associated with a high overall exam-score, but also that a low score on the individual question is associated with a low overall exam-score. This means that the individual question is able to successfully discriminate between students with a low versus a high exam score. A low (or negative) RiT shows poor discriminative capacity, which may be caused by questions being ambiguous, too simple, or too difficult. We used RiT to analyze the effect of RPQs on the internal consistency of the exams and to conclude whether RPQs are of similar quality as RQs.Exam questions have to be of reasonable (thus comparable) difficulty and need to discriminate between students with different knowledge levels. The item total correlation . It represents the size of the response-pool.This parameter indicates the number of individuals that submitted any answer to the question ). Period of exam is therefore not a suitable predictor to explore knowledge preservation. It does however represent trends of different periods’ questions and the longitudinal character of this study when questions are grouped based on origin . Each period contains exam questions with a variety of origins and thus with different Origin of question as well as Period of exam of each question is known. It can therefore be calculated what number of retrievals the specific question represents. If a question belongs to the second exam of a period, N of repetitions was calculated by (Period of exam – Origin of question) * 2. If the question belongs to the first exam of a period, 1 was subtracted from the outcome of that formula. For example, the N of repetitions of P2 questions in the first exam in P5 = ((5–2)*2)-1 = 5. This means that the material from P2 is examined for the fifth time in exam 1 of P5. If the exam occurred in P8, 2 was subtracted from the formula since P7 only contains 1 exam. Possible values are all integers between and including zero to ten, with zero being all RQs.The N of repetitions  on question score and question quality. Accordingly, %Cor and RiT were our main response variables. N of repetitions as a predictor essentially causes normalization of the data: it causes data to overlap as all questions (regardless of their origin) are introduced for the first time at x = 0 (zero repetitions = RQs). To assess question score over time, the relationship between N of repetitions and %Cor was determined using linear mixed model analysis. By using the origin (to which period a question relates) of a question as a grouping variable, %Cor across N of repetitions was assessed for each origin individually before estimating the size of the fixed effect (N of repetitions). We analyzed the relationship between N of repetitions and %False and %Open in a similar way to the analysis of %Cor, to provide full disclosure of the distribution of question feedback. To determine the effect of RPQs on internal consistency of the exams, the RiT of RQs versus RPQs was compared using a Mann-Whitney U test. To assess a possible relation with N of repetitions we also performed a mixed model analysis using RiT as a dependent variable.Analysis primarily focused on exploring the relationship between N of answer options was statistically analyzed using a Chi-Square test. Significant Shapiro-Wilk and Kolmogorov-Smirnoff tests indicated that none of the other variables were normally distributed (a common finding in psychometrics). Hence, these variables were analyzed using non-parametric Mann-Whitney U tests.A descriptive section in which the pool of RPQs is compared with the pool of RQs is included in this paper Table . The disStatistics were performed using Statistical Package for the Social Sciences (SPSS) 26  and GraphPad Prism 5.03 .n = 1728, RQs n = 2006).A total of 196 questions were excluded from the database as they were not included in the final grade calculation, and 150 questions were excluded as they related to high school knowledge. The remaining 3734 questions were used for analysis (RPQs P < 0.001). %Cor and %False were significantly higher in RPQs compared to RQs . RPQs had a significantly lower %Open and RiT (both P < 0.001) than RQs.In Table %Cor (graph A), %False (graph B) and %Open (graph C) for separate origins across all periods of the MHD-program. Linear mixed model revealed no significant change in %Cor across the number of repetitions . The analysis also showed no significant association between number of repetitions and %False , or %Open . Graph D in Fig. RiT across number of repetitions. A minor, but significant trend was found in RiT across number of repetitions . In Appendix Figure and false answers, and subsequently a significantly lower number of questions that were left open. This indicates that students were more likely to answer RPQs, with preservation of the mean score for the questions. Second, time-dependent analysis of RPQs revealed no significant change across multiple repetitions, supporting the absence of the ‘forgetting curve’. This suggests preservation of knowledge within this investigated program. Third, the discriminative capacity of RPQs, quantified as the RiT, was slightly but significantly lower than RQs. Fourth, time-dependent analysis revealed a small, inverse relation between the number of repetitions and RiT-score across multiple repetitions (n = 10). Despite the significantly lower discriminative capacity of RPQs, the magnitude of this decline remained within the limits of acceptable internal consistency of the exam. Altogether, our data indicate that the use of RPQs across a two-year educational program prevents significant loss of knowledge during that program, without affecting exam quality.Our retrospective study compared the scores of regular questions (RQs) to retrieval practice questions (RPQs) across a two-year program on ‘mechanisms of health and disease’ for medical students and students in biomedical sciences. It explored the relation of these questions with the overall exam score. This work presents the following findings. First, compared to RQs, the RPQs received a small but significantly higher proportion of correct and false answers are increased in RPQs, which logically coincides with a decline in the percentage of non-answer (%Open). Consequently, the small increase in %Cor does not simply imply improved knowledge retention. A gain in confidence of respondents could be present as they proceed in the program, resulting in a slightly higher percentage of correct, but also incorrect answers for RPQs. At the very least, the lack of a significant decline in score for the RPQs across multiple repetitions indicates no knowledge loss over time, and a successful prevention of the characteristic ‘forgetting curve’ for the duration of the MHD program.Comparing the outcomes for RPQs and RQs, we found no evidence for a decline in scores when repeatedly testing knowledge. In accordance with other research , we linked the percentage of correct answers to knowledge levels and knowledge retention. Importantly, the exam set-up warrants caution in interpreting the percentage of correct answers, because students were allowed to leave questions open. This option prevents ‘correction for guessing’, as an incorrect answer leads to subtracting points from the exam score. We found that both the percentage of correct A notable loss of knowledge occurs after its acquisition. Virtually all information, including daily life situations, is subject to the characteristic forgetting curve , often within days to weeks , 16, 17.existing memory trace, whereas testing leads to the formation of a new memory trace [Furthermore, we suggest that our results can be explained using the dual-memory theory . This thry trace . During An alternative explanation for the preservation of knowledge is that the acquired MHD knowledge is repeated or is built upon in subsequent periods within the MHD-program and/or within the broader spectrum of the curriculum. The structure of the biomedical curriculum as a whole may therefore prevent knowledge decrement, as additional learning reactivates related memories . InteresRiT, reflecting a lower contribution of RPQs to the internal consistency of an exam. Furthermore, the marginal but significant decline across number of repetitions suggests that the discriminative capacity of RPQs declines with more repetitions. A possible explanation could be an increase in confidence regarding repeatedly examined course material in all students. Therefore, responses to RPQs may be related to students’ overall exam scores to a lesser extent than before, causing a slight decrease of RiT in RPQs. Although these observations imply that RPQs negatively impact the capacity of exams to discriminate between ‘good’ and ‘bad’ students, caution is warranted. Previous work recommends to aim for RiT-values of at least .15 [RiT of 0.22, only 0.02 higher than RPQs, we question the relevance of this small difference for the internal consistency of the exam. This is further supported by the relatively small variation in RiT-values for both types of questions. Indeed, the 95% confidence interval for RiT-values of RPQs was .197 to .206 versus .212 to .220 for RQs, which is all well above the mentioned .15 minimum, being classified as non-harmful to exams’ internal consistency. The minor difference in RiT between RPQs and RQs means that students do not pass exams based on their knowledge of past periods only and need to obtain sufficient knowledge related to both previous and current period(s).Our second aim was to investigate whether the discriminative capacity of the RPQs differed from RQs. In contrast to our hypothesis, we found a significantly lower east .15 . Since oAn important limitation to highlight for this study is the structure of the exam, where students had the possibility to not answer questions. This complicated the interpretation of our data, especially the question whether and how leaving questions open relates to knowledge preservation (or loss). Nonetheless, the relatively small change in questions that were left open allowed us to robustly examine our primary research question. Another obvious limitation is the lack of a control group to truly examine the role of the RPQs. This is important since, in contrast with most studies , 16, 18,In conclusion, although one of the main goals of education is the retention of knowledge, teaching and exams are often followed by loss of knowledge (i.e. the forgetting curve). Our study examined the proportion of correct answers to questions within the cumulative examination strategy of the ‘mechanisms of health and disease’-program for medical and biomedical sciences students. In line with the hypothesis that retrieval practice questions promote retrieval practice and a more spaced learning approach, we found no decrement in scores for retrieval practice questions across the two-year educational program. It implies that the cumulative examination strategy may be an effective strategy in counteracting the forgetting curve. The cumulative aspect of the examination through the RPQs did not importantly decrease overall quality of the exams. This work highlights the potential of a cumulative examination approach to promote knowledge retention in medical and biomedical sciences students, by utilizing the principles of retrieval practice and spaced learning.Additional file 1.


In this work, we demonstrate the use of electrical impedance spectroscopy (EIS) for the disentanglement of several dielectric contributions in encapsulated single graphene layers. The dielectric data strongly vary qualitatively with the nominal graphene resistance. In the case of sufficiently low resistance of the graphene layers, the dielectric spectra are dominated by inductive contributions, which allow for disentanglement of the electrode/graphene interface resistance from the intrinsic graphene resistance by the application of an adequate equivalent circuit model. Higher resistance of the graphene layers leads to predominantly capacitive dielectric contributions, and the deconvolution is not feasible due to the experimental high frequency limit of the EIS technique. Since the discovery of single-atom graphene layers in the year 2004, large research efforts have been dedicated to the investigation of their fundamental properties and more practical aspects in terms of the handling and incorporation of graphene into functional devices for potential application in the electronics industry ,9,10,11.E vs. propagation wave vector k [Single-layer graphene (SLG) is not only a promising candidate as an electrode material in electrochemical applications , but alsvector k ,21. Convvector k ,23. Asymvector k ,25,26.One of the hurdles on the way to commercial integration of graphene layers into electronic devices is the precise understanding of the role of the electrode resistance, which usually arises at the interface between electrode and graphene layer. This extrinsic resistance is usually referred to as the contact or electrode resistance, where here in this work, the latter term of electrode resistance will be used consistently.f noise in graphene devices [In potential applications such as graphene-based transistors ,29,30,31 devices . Convers devices ,34,35, b devices  or senso devices , the for devices ,39, the  devices ,41, and  devices . For the devices .For the characterization of the electrode resistance at the electrode/graphene interfaces in terms of the graphene surface potential and work function, the use of Kelvin probe force microscopy (KPFM) and theoretical predictions by density functional theory (DFT) have been demonstrated in the literature ,44,45,46For devices with higher nominal graphene resistance, the determination of the electrode/graphene interface resistance by EIS and concomitant equivalent circuit fitting of the data was not feasible. This was due to the fact that the equivalent circuit needed to be modified in terms of the constituent components  due to such increased nominal graphene resistance, and the electrode contribution could not be fitted separately anymore. The equivalent circuits proposed here may be generally valid for EIS characterizations of graphene layers.The EIS technique is well established to allow for disentanglement of intrinsic and extrinsic resistances in functional materials that contribute to the overall direct current (DC) resistance. EIS has, thus, been widely applied in polycrystalline bulk materials ,51 as wet)-dependent alternating voltage U of variable angular frequency ω and fixed amplitude U0 applied to the sample: U = U0 cos(ω·t). Effectively, the amplitude I0 and the phase shift δ of the alternating current response signal I are measured over a wide frequency range: I  = I0 cos(ω t + δ). One period of the applied voltage stimulus corresponds to a 2π rotation of the U arrow on the phasor diagram shown in δ = 0) with respect to the applied voltage for an ideal resistor (R), IR, (2) out of phase by δ = −π/2 for an ideal capacitor (C) with preceding current, IC, and (3) out of phase by δ = +π/2 for an ideal inductor (L) with lagging current, IL, where all phase angles are time independent.EIS experiments consist of an electric stimulus in terms of a time  are not accessible for spectra dominated by inductive contributions (positive Z’’) and vice versa.To obtain a physically meaningful interpretation, the impedance needs to be defined as a time independent complex number ibutions , whereasibutions . Note thτ, where τ = R × C [In the case of RC elements, the ideal capacitor is often replaced by a constant phase element (CPE), sometimes also termed a Q-element, leading to R-CPE or RQ elements. On a microscopic level, the CPE behaviour can be interpreted in terms of a broadening of the distribution of relaxation times  = R × C ,54.2O3 film that was deposited previously onto sapphire substrates by atomic layer deposition (ALD) using tetramethylammonium hydroxide (TMAH) and water precursors [ℓ between 2–11.2 μm. The graphene or device width w was fixed to w = 50 μm in the first more conducting sample A, whereas w = 10 μm was chosen in a second sample B to intentionally obtain higher nominal graphene resistances. Next, side electrodes to the graphene were sputter deposited (20 nm Ni followed by 20 nm Pd) onto a lithographically defined photoresist pattern followed by lift-off. Encapsulated graphene single layers were fabricated by transferring graphene on top of an Alecursors . The graℓ is considered to be the distance between the two side electrodes, i.e., the length of the graphene layer measured . ℓ. The two Ni/Pd measurement electrodes were contacted by wire bonding, punching across the insulating Al2O3 layers  between 20 Hz–1 MHz with an applied voltage of 20 mV amplitude, whereas the PPMS system provided variable temperature T (1.7–320 K). A special sample holder was custom built  to fit into the PPMS to minimize the internal probe capacitance (≈0.2 pF) and maximize the internal probe resistance (≈10 GΩ), which is both detrimental for reliable EIS measurements.The device length ured see . The devyers see . For conT between 160 and 560 K upon heating. Note that the graphene impedance was always measured in an in-plane measurement configuration. The sample tray at the bottom of the probe was equipped with drop-down pins with a mechanical load to ensure optimal contact to the sample electrodes. Further EIS measurements were carried out using an Alpha Analyser Novocontrol system operating at 1 Hz–10 MHz using an applied AC voltage signal of 100 mV amplitude under various Z’, Z’’) of the complex impedance Z* = Z’ + iZ’’ under steady state conditions, where the selected T was allowed to settle for ≈10 min before taking impedance readings. Equivalent circuit fitting of the dielectric data was performed by using commercial Z-View® software.All dielectric data were collected in terms of the real and imaginary parts  plot obtained from the graphene devices with a larger device width of 50 μm on the first more conducting sample A. In this case, a positive Z’’ at all frequencies is detected, which can be modelled with an equivalent circuit containing a parallel RL element and a single resistor in series as indicated in the f limit of the impedance analyser. Further note that capacitive contributions to the impedance would still be present and are expected to have a perceptible effect on the data. However, their contribution was not strong enough to allow for fitting with an equivalent circuit model containing an additional capacitor. The small discrepancy between the model and data shown in ℓ. Conversely, R2 is approximately constant and does not scale with ℓ. R2 may therefore be interpreted as an extrinsic contribution that arises from electrode interfaces, electrodes, cables or any other contributions that do not scale with ℓ. Generally, a good agreement between data and fitted curves is observed. As mentioned in Z’’ due to dominating capacitive contributions and a large pike in form of an approximately perpendicular curve are displayed for the data taken from the same sample A but with painted electrodes with an insulating Al2O3 layer in the circuit, for control purposes. The perpendicular curve can be interpreted as the onset of a large semicircle that cannot be resolved leading to the capacitive behaviour reflected by negative Z’’. The diameter of a capacitive semicircle that typically appears in −Z’’ vs. Z’ (or Nyquist) plots with negative Z’’ corresponds to the resistance of the respective RC element. Therefore, the onset of a massive conventional semicircle can be interpreted as an indication of an electrically insulating contribution, which may well be associated with the Al2O3 layer. Note that this contribution is absent in 2O3 layer. At intermediate f, Z’’ vs. Z’ plots at intermediate f-range. Close inspection of the −Z’’ vs. Z’ plots at the high f-range . R1 had been extracted from the models shown in T. A continuous increase in resistance with T is observed, indicating metallic type charge transport that may be associated with the intrinsic graphene resistance. The association of R1 with an intrinsic graphene contribution is justified in ℓ. R1 approximately scales with ℓ, where the linear trend line may pass through or close to zero. The sheet resistances for the devices with ℓ = 2.5, 5 and 11.2 μm are, therefore, similar and were calculated with a rough approximation to be 1, 1.15, and 0.93 kΩ/□, respectively. Note that these values for encapsulated graphene are slightly lower than the 1.84 kΩ/□ reported for free standing graphene [T-dependence of R1 had been averaged out. Conversely, the second resistor R2 from the model shown in ℓ and, thus, may be associated with extrinsic contributions from the electrode resistance and the measurement cables .To associate different equivalent circuit components with certain areas in the sample, it is helpful to analyse their trends of the resistance with the device length graphene , and thegraphene . For appt cables b. Thus, w = 10 μm and, therefore, displays a higher nominal graphene resistance. The larger nominal graphene resistance leads to mainly capacitive contributions in the impedance spectra as depicted in The situation is different for sample B that had been fabricated with the smaller device width of T-dependences of the resistance extracted from resistor R1 are depicted in ℓ to some extent, but the scaling curve does not pass through the origin of the graph. Instead, a residual resistance of ≈1 kΩ is indicated. The graphene sheet resistances for the different device lengths of ℓ = 5, 8.8 and 12 μm were calculated to be approximately 2.4, 1.4 and 1.25 kΩ/□, respectively. In contrast to the more conducting devices in the first sample A, the resistor R1 here yields sheet resistances that change with the device length ℓ. The term “sheet resistance” may be problematic here, because R1 contains the graphene resistance and additional extrinsic contributions. The “sheet resistance” for the larger devices (8.8 and 12 μm) approaches the value obtained from the first sample A (≈1 kΩ/□). These findings suggest that R1 in sample B may not consist of the contributions from the graphene layer only but contains multiple contributions: (i) extrinsic contributions from the electrode resistance and possibly the measurement cables, which do not scale with ℓ, and (ii) the intrinsic graphene layer resistance that scales with ℓ. This is plausible since the dielectric data from the second more resistive sample B could be fitted only with one single R-CPE element, although several contributions are expected to be present.The Z’’ vs. Z’ plots. Thus, in sample B, the resistance R1 in the equivalent circuit model depicted in the insets of T-dependencies of R1 shown in R-T dependencies.Therefore, it is not possible in this case to disentangle the different dielectric contributions, which is debited to the fact that only the onset of one semicircle was detected in −It was demonstrated here that the extrinsic electrode resistance and the intrinsic graphene resistance of encapsulated graphene layers can be disentangled under certain conditions using EIS. The dielectric data vary qualitatively with the nominal graphene resistance (not the sheet resistance). In the case of sufficiently low graphene resistance, the dielectric spectra are dominated by inductive contributions, which allows for disentanglement of electrode and graphene resistance by the application of an adequate equivalent circuit model. Conversely, higher nominal graphene resistance leads to predominantly capacitive contributions, in which case the deconvolution is not feasible due to the experimental high frequency limit of the EIS apparatus of 1 MHz.It can be concluded that EIS can be applied to graphene-based devices to achieve disentanglement of the electrode resistance from the intrinsic graphene layer resistance, in the case that the graphene resistance is sufficiently low. It is anticipated that the equivalent circuits proposed here in this work may be generally valid for EIS characterizations of graphene layer devices in future studies.


For the first time, few-layer graphene (FLG) nanosheets were synthesized by the method of self-propagating high-temperature synthesis (SHS) from biopolymers . We suggest that biopolymers and polysaccharides, particularly starch, could be an acceptable source of native cycles for the SHS process. The carbonization of biopolymers under the conditions of the SHS process was chosen as the basic method of synthesis. Under the conditions of the SHS process, chemical reactions proceed according to a specific mechanism of nonisothermal branched-chain processes, which are characterized by the joint action of two fundamentally different process-accelerating factors—avalanche reproduction of active intermediate particles and self-heating. The method of obtaining FLG nanosheets included the thermal destruction of hydrocarbons in a mixture with an oxidizing agent. We used biopolymers as hydrocarbons and ammonium nitrate as an oxidizing agent. Thermal destruction was carried out in SHS mode, heating the mixture in a vessel up to 150–200 °C at a heating speed of 20–30 °C/min and keeping at this temperature for 15–20 min with the discharge of excess gases into the atmosphere. A combination of spectrometric research methods, supplemented by electron microscopy data, has shown that the particles of the carbonated product powder in their morphometric and physical parameters correspond to FLG nanosheets. An X-ray diffraction analysis of the indicated FLG nanosheets was carried out, which showed the absence of formations with a graphite crystal structure in the final material. The surface morphology was also studied, and the IR absorption features of FLG nanosheets were analyzed. It is shown that the developed SHS method makes it possible to obtain FLG nanosheets with linear dimensions of tens of microns and a thickness of not more than 1–5 graphene layers .  Graphene materials such as graphene nanoplates (GnP), graphene nanosheets (GnS), graphene oxide (GO), reduced graphene oxide (rGO), and so forth, are successfully used by researchers around the world in various promising areas. There are the following areas: as an additive in the polymer  and metaChemical vapor deposition  and epitThe classical methods for obtaining GO are the Brodie method (proposed in 1859 by Benjamin Brodie)  and the Another approach to the chemical exfoliation of graphite involves the intercalation of graphite with alkali metals such as potassium and lithium, followed by the exfoliation of such intercalated graphite compounds in several layers of graphene nanosheets ,13.This approach is much more straightforward and more economical than the previous one. However, this is time-consuming, and hazardous strong acids are used throughout the entire process. This factor also leads to a decrease in the quality of graphene nanosheets, an increase in the cost, environmental problems, and a reduction in the productivity of the method.The complexity, environmental unsafety, and low quality of the synthesized graphene structures (due to defects in their cellular structure) of the considered methods make them unpromising for obtaining graphene structures in volumes exceeding the volumes required for interlaboratory studies. As a result, many research groups began looking for alternative methods for synthesizing graphene structures.For example, several authors used a well-practiced hydrothermal synthesis method to obtain graphene structures.Reference  reported2/g) and developed microporosity (up to 1.33 cm3/g).In some cases, FLG nanosheets may be obtained directly by mechanical activation . In this3–106) deg/s. [In several works, the self-propagating high-temperature synthesis (SHS) process is used to synthesize graphene nanostructures. Physically, SHS is a process of moving a wave of a strongly exothermic reaction through a mixture of reagents (oxidizing agent and reducing agent), in which heat release is localized in the layer and is transferred from layer to layer using heat transfer. Typical characteristics of the SHS process are as follows: flame front propagation speed −(0.1–20) cm/s; maximum combustion temperature −(2300–3800) K; the heating rate of matter in the wave is (10) deg/s. .3, 99.5%) and magnesium Mg to carry out the SHS reaction. Carbamide (CO(NH2)2, 99.5%) and carbon dioxide (purity 99.9%) were used as the carbon source. In references [3. Another work using the advantages of SHS [3, basic magnesium carbonate, 3MgCO3·Mg(OH)2·3H2O) and AR grade glucose (99.5%). Few-layer graphene was also obtained.In particular, for the synthesis of graphene and few-layer graphene in ,19 the cferences ,21, for s of SHS  used MagIn this paper, we report on a new technique for synthesizing large volumes of FLG nanosheets based on the nontraditional self-propagating high-temperature synthesis (SHS) method. From our point of view, to a certain extent, the SHS method can be considered an analog of detonation synthesis of nanodiamonds . DetonatThe implementation of detonation synthesis shows no theoretical restrictions on the synthesis of 2D carbon nanostructures in the sequence of processes “destruction-self-organization”. Chemical reactions under the conditions of the SHS process proceed according to a specific mechanism of nonisothermal-branched chain processes, which are characterized by the combined action of two fundamentally different factors accelerating the process: avalanche multiplication of active intermediate particles and self-heating. In contrast to thermal ignition, branched-chain ignition is caused by avalanche multiplication of active intermediate products—free atoms, radicals and sometimes-excited particles—in their rapid reactions with the initial reagents and among themselves .When choosing a carbonization technique, we proceeded from the fact that the advantage of using the SHS process over the pyrolysis and hydrothermal carbonization processes traditionally used for carbonization of biopolymers  is the sWe assumed that biopolymers with a cyclic macrochain structure could be an acceptable source of native cycles (pre-cursors) for the SHS process based on the analogy with detonation synthesis.In formulating this assumption, we relied on comparing graphene to a specific macromolecule, the native element of which (monomer) can be a cycle of six carbon atoms—hexagon. An acceptable source of hexagons can be plant polymers, the macromolecules of which are either formed by cyclic structures . The choice of the source of such cycles, as well as the development of a technique for initiating their “bottom-up” self-organization processes, would make it possible to obtain 2D carbon nanostructures on a scale required for real application.Biopolymers carbonization was carried out under the conditions of the process of the SHS method. The process was carried out using a laboratory reactor, a quartz vessel (1 L capacity) with a heating element in the lower part, which provides the initial heating of the reaction zone to the temperature required to initiate the process (220 °C). Heating to a lower temperature did not lead to the start of the synthesis process. Temperature control in the reaction zone was carried out using a thermocouple. The preparation of the synthesis included a number of sequential stages. The pre-dried biopolymers (until weight loss stops) were transferred to a ball mill and processed for 15 min. The average particle size of biopolymers was no more than 100 μm. Next, a mechanical mixture of an oxidizing agent (ammonium nitrate) and a precursor (biopolymers) was prepared in a weight ratio of 1:1. This ratio was experimentally selected as giving the maximum yield  of the product. The prepared mechanical mixture of the oxidizing agent and the precursor was placed in a “drunken barrel” mixer and processed for 15 min. Then the resulting crushed mixture was transferred into a reactor preheated and purged with a dry argon flow. The beginning and end of the reaction were judged by the beginning and end of the evolution of gaseous reaction products. The duration of the process is 5–8 min. The rest of the reaction mass passes into the gas phase and is captured in a trap cooled with liquid nitrogen. The process flow diagram is shown in The methodology for producing FLG nanosheets from wood waste includes several steps, as shown in All raw materials were crushed  and dried to constant weight in an ShS-40-02 drying oven at 80 °C. The dried powder was finely ground in a laboratory planetary mill LP-1-HT Machinery and sifted through a sieve to a particle size of the order of 100 μm (±10 μm).After completing these elementary cleaning and drying steps, the waste and ammonium nitrate (average particle diameter 10 µm) were thoroughly mixed using a “drunken barrel” mixer in a specific ratio.The mixture of the initial components prepared in this way was slowly heated to a temperature of about 150–200 °C in an oil bath in an air atmosphere before the start of the decomposition of ammonium nitrate.A schematic representation of the setup for SHS and the SHS process scheme is shown in The main feature of carbonization in the process of rapid pyrolysis  is that fast pyrolysis is carried out during the thermal decomposition of woody biomass in the absence of an oxidizing agent. The fundamental difference between the carbonization of plant polymers (mainly woody biomass and products of its processing) under the conditions of the SHS process is that in our case, there are active oxidants—nitrogen and oxygen obtained during the decomposition of ammonium nitrate. This is the most crucial differences between carbonization and the SHS method.Another significant difference between the processes of fast pyrolysis and the SHS process we use is that the carbonization process under conditions of fast pyrolysis is carried out with a constant supply of thermal energy, that is, the process is self-accelerating , while tBased on these two differences in the considered carbonization processes, it was assumed that, under the conditions of the SHS process, the carbonization of plant biomass would proceed only to a solid carbonized product. This moment of the SHS process we use creates the basis for the nucleation of FLG nanosheets with the secession of all gas oxides and metal oxides.After the completion of the SHS process, a black powder was obtained. The black powder collected in this way at the end of the SHS process was washed with distilled water to remove water-soluble products . The resulting FLG nanosheets were either dried or left in an aqueous suspension. Experimental conditions are optimized to synthesize FLG nanosheets with up to five layers. To confirm the successful synthesis of FLG nanosheets, various spectroscopic research methods have been performed, SEM and TEM images were obtained.Raman spectroscopy  at an ex-citing laser length of 532 nm was used to demonstrate the vibrational properties of FLG nanosheets.Fourier transform infrared spectroscopy was performed using an INFRALUM FT-08 instrument , to study functional groups on the surface of FLG nanosheets. X-ray diffraction (XRD) was performed using an XRD-7000 .SEM and TEM studies of the morphology and structure of FLG nanosheets were carried out using the TESCAN Mira-3M  with Electron Dispersive X-ray Spectroscopy (EDX) device attachment  and FEI Tecnai G2 30 S-TWIN (50 kV) microscope. UV-VIS spectra investigated on Shimadzu-IS . The studies based on the method of the X-ray photoelectron spectroscopy were conducted on the instrument Thermo Scientific K-alpha .To evaluate the success of the above experimental strategy for synthesizing FLG nanosheets using the SHS method, we used various methods to validate the synthesis of FLG nanosheets.The material synthesized by the SHS method was a black, highly dispersed, highly volatile powder. The structure of synthesized FLG nanosheets obtained using SEM and TEM is shown in As shown in As shown in The EDX studies  showed tFrom the above data on the study of a sample obtained from glucose, it can be concluded that there are no impurities of metals or other elements. The main impurities are oxygen and nitrogen (due to the presence of an oxidizing agent—ammonium nitrate in the SHS process) contained in an amount of no more than 10 and 15 at.%, respectively. Oxygen content in graphene sample up to 15 wt.% is typical for all graphene nanostructures, except graphene oxide, in which the oxygen content can reach 50 wt.%. FLG nanosheets produced from other starting materials had the same impurity purity.Raman spectroscopy was performed to confirm the exact synthesis of FLG nanosheets and to assess their quality .−1), G (1570–1580 cm−1), 2D (2680–2690 cm−1) and D+G (2920–2930 cm−1).Various active modes are observed in the synthesized samples. The Raman spectra of all samples contain four peaks characteristic of graphite materials, corresponding to D  were observed in references ,30,31,322. The 2D band is a second-order Raman characteristic of crystalline graphite materials and is sensitive to the aromatic C-structure. The D and D+G bands represent Raman features caused by lattice disorder. Disorder in the graphite lattice occurs due to defects associated with vacancies, grain boundaries, carbon particles sp3, and the inclusion of O in the aromatic sp2 C-network [The G band arises from the planar vibrations of the aromatic carbon sp-network .2 carbon atom associated with structural defects, and band G shows the degree of graphitization. The low-intensity ratio of the G band to the D band  indicates the existence of randomly arranged graphene nanosheets. Based on the data on the position of the 2D peak, the number of layers in the synthesized samples does not exceed 5 [It was found that comparison of the 2D and D+G Raman bands can indirectly control the electronic structure . Band D shows partially disordered structures of the spexceed 5 .2-hybridized carbon material, while the D-band is activated only if the defects are involved in double resonance Raman scattering of light near the K point of the Brillouin zone. Thus, our results show that the average size of sp2 domains changes significantly in samples prepared from lignin relative to samples obtained from cellulose and glucose, since the intensity ratio ID/IG is usually used to estimate the size of the sp2 domain of graphite-based materials.The G-band is Raman-active for the sp−1 region. The 2D band is Raman active for crystal-line graphite materials and is sensitive to the band in the electronic structure of graphite, while the D+G combination mode is caused by the disorder. The I2D/ID+G intensity ratios show that the recovery of graphite electronic interfacing for glucose-derived samples is significant.In addition to the G and D bands, two weaker Raman bands, called 2D and D+G, are located in the 2920–2930 cmThus, it can be concluded that the FLG nanosheets obtained by the SHS method have structural defects and have a slightly curved surface with kinks, breaks, and bends.An X-ray diffraction analysis was carried out to prove the absence of graphite nanostructures in the obtained material , the results of which are shown in A halo in the range from 10 to 35 degrees and reflections at ~43 degrees strictly proves the absence of any graphite materials, the diffraction patterns of which are characterized primarily by a narrow and intense peak in the region of 26 degrees, as well as a narrow peak with low intensity in the region of 54–55 degrees .N = (L/d), where N is the number of layers; L is the thickness of the stack of graphene structures; d—interlayer distance [L is the thickness of the pack; λ = 0.154051 nm—radiation wavelength; θ—scattering angle; β—physical line broadening in the diffractogram in radians ; n—particle shape factor equal to 1. The number of layers in FLG nanosheets obtained by the SHS method from biopolymers is set as a value not exceeding five layers.As seen from distance . The thidistance :L = nλ/βFTIR studies were carried out to assess the functional composition of the edges of FLG nanosheets . It is sIt can be seen that, depending on the initial biopolymer, the content of C≡N, C–O and C–OH groups on the surface of FLG nanosheets changes. Similar results were obtained using lignin as starting material.In addition, to characterize the synthesized FLG nanosheets, studies were carried out by the method of gas (helium) pycnometry and the measurement of the specific surface by the BET method, the results of which are presented in 2/g). Although GNP samples can have a specific surface area of up to 750 m2/g [As can be seen from the data presented in 750 m2/g , usually750 m2/g . HoweverThus, based on the set of data obtained, we can confidently conclude that the structure of the particles obtained in the work corresponds to the structure of the FLG nanosheets.Studies of optical absorption in the UV-VIS range have shown that the spectrum corresponds to graphene structures . The absIt could be attributed to π–π* transitions of aromatic C–C bonds, which is in agreement with a report for high-quality graphene dispersions by liquid-phase exfoliation and corresponds to FLG nanosheets with up to five layers .2, 284.4 eV) C–OH (285.4 eV), O–C–O/C–OH (286.5 eV), C=O (287.4 eV), OC=O (289 eV), and also the C–N (285.9 eV) peak is observed in the spectrum [spectrum . The dat2, 284.4 eV) peak, which is typical for graphene and is not specific for oxidized forms of graphene nanostructures, primarily graphene oxide [All spectra demonstrate the presence of the C–N peak in the obtained FLG nanosheets, which fundamentally distinguishes them from GO and rGO, where such peaks are not observed. The intensity of the peaks of the carboxyl and other groups does not exceed the intensity of the C=C  as precursors. The FLG nanosheets obtained are characterized with linear dimensions of tens of microns and several (up to five) graphene layers thick.The conducted research allowed us to conclude that the imperfection of the lattice of FLG nanosheets, obtained by carbonization of biopolymers in the process of self-propagating high-temperature synthesis, are free from Stone–Wales defects but are mainly determined by vacancy defects.The method developed by us allows obtaining large volumes of FLG nanosheets. The use of the developed material in areas requiring large volumes of raw materials and not requiring high-quality single-layer graphene, namely, when creating composite materials, as an effective sorbent and the other regions, will be possible.Moreover, as our preliminary experiments demonstrate, graphene synthesized by the SHS method can be used for various other applications, such as a material for supercapacitors, fuel cells and a filler for polymer and metal nanocomposites, nanofluids based on water and organic solvents for cooling systems, and so forth.For the synthesis of few-layer graphene, the method SHS was applied because it is a versatile method of nanomaterial production. What is new is the use of biopolymers and ammonium nitrate as precursors for the SHS process. Fast self-propagating of reaction in a mixture of ammonium nitrate and biopolymers  results leads to the effective formation of carbon nanostructures (few-layer graphene) in the purified product. Other biopolymers, such as lignin and tree bark, can be used.SEM and TEM studies, Raman spectroscopy, X-Ray and UV-Vis spectroscopy, IR spectroscopy and XPS analysis confirmed the graphitic crystalline structure of FLG nanosheets and its thickness of not more than five graphene layers.The EDX studies showed the high purity of the FLG nanosheets obtained by the SHS method.This SHS method is environmentally friendly, simple, fast, economical and suitable for the large-scale production of FLG. The preparation of FLG using this technique offers the advantage of simplicity without harmful environmental effects.Thus, a new high-performance SHS method has been developed to synthesize few-layer graphene nanostructures of a large area from biopolymers, including waste from the woodworking industry—lignin and tree bark. The SHS method will make it possible to obtain graphene nanostructures cheaply in large quantities while solving the environmental problem existing in the world. The SHS method is a breakthrough in the technology and production of graphene nanosheets.Moreover, this method can solve a significant environmental problem of lignin and tree bark utilization.The SHS method developed in this article for the production of FLG is promising for creating, for example, doped graphene by choosing various reagents to participate in the SHS reaction.


Thus, an in situ multiscalemonitoring of the 2DMs structure, coupled with a real-time controlof the growth parameters, is necessary for efficient synthesis. Herewe report real-time monitoring of graphene growth on liquid copper(at 1370 K under atmospheric pressure CVD conditions) via four complementary in situ methods: synchrotronX-ray diffraction and reflectivity, Raman spectroscopy, and radiation-modeoptical microscopy. This has allowed us to control graphene growthparameters such as shape, dispersion, and the hexagonal supra-organizationwith very high accuracy. Furthermore, the switch from continuous polycrystallinefilm to the growth of millimeter-sized defect-free single crystalscould also be accomplished. The presented results have far-reachingconsequences for studying and tailoring 2D material formation processeson LMCats under CVD growth conditions. Finally, the experimental observationsare supported by multiscale modeling that has thrown light into theunderlying mechanisms of graphene growth.The synthesis oflarge, defect-free two-dimensional materials (2DMs)such as graphene is a major challenge toward industrial applications.Chemical vapor deposition (CVD) on liquid metal catalysts (LMCats)is a recently developed process for the fast synthesis of high-qualitysingle crystals of 2DMs. However, up to now, the lack of  Grapheneis often grown using a CH4 precursor on a solid coppercatalyst at ∼1270 K.2 High nucleationrate and growth at random substrate positions result in a polycrystallinelayer, whose properties are affected by the purity, roughness, crystallographicstructure, and domain boundaries of the substrate.4 Notableachievements for graphene growth on solid copper have been reported,such as the stitching of aligned graphene domains into a single-crystalfilm5 or the growth of inch-size singlecrystals from one nucleus.7 However, these resultswere obtained using conditions that are hard to implement at an industrialscale. Moreover, because of the thermal expansion mismatch betweengraphene and copper, high residual stresses develop upon cooling toambient temperature, inducing wrinkling and folding of the final graphenefilm.8 Finally, after cooling to roomtemperature, special chemical methods  are applied to separate and transferthe graphene due to the presence of considerable van der Waals forcesbetween the graphene and solid substrate. This leads to further contaminationand damage to graphene.9Reproducible mass productionof large, defect-free two-dimensionalmaterials (2DMs) such as graphene is a major challenge toward theirindustrial applications. Chemical vapor deposition (CVD) is to datethe most promising method to produce large, high-quality graphenesheets.e.g., molten copper,have been employed for the fast growth of uniformhexagonal graphene and other 2DMs flakes and films of significantlyhigher quality, while using experimental temperature, pressure, andflow conditions that are comparable to those used with solid catalysts.12 One of the main advantages of LMCats over solid substrates is theirsurface structure. LMCats consist of an atomically flat isotropicmelt, with low surface roughness and absence of defects characteristicfor solid substrates, such as atomic terraces, dislocations, and impurities,that impact graphene growth.13 Moreover,the crystalline solid surface imposes preferential orientation, theformation of moiré superlattices, and induces strain in thegrown graphene.14 The absence of a crystallinestructure on LMCats allows the formation of graphene crystals in anyin-plane orientation, freely moving and rotating during the growthand without substrate-induced strain. This leads to much easier growthof high-quality graphene crystals, their self-assembling, and theformation of uniform layers.17 Compared with solids, the measurednucleation density is at least 1 order of magnitude lower,18 leading to fewer domain boundaries and biggerdomains. Also, the growth speed is much higher,18 which is very beneficial in producing large-area flakes,caused by high diffusion rates,18 and catalyticactivity of LMCats.19 The weak interactionsbetween LMCats and the grown layers suggest the possibility to attemptfor a direct transfer without LMCat solidification and complex postgrowthtransfer processes. Such a challenging process is both theoreticallypossible (see Supplementary Note 1), andpractically achievable, as first successful trials have already beenreported.20 The main focus in the investigationof graphene on LMCats is concentrated on the graphene–coppersystem and to a less extent on other liquid metals. However, successfulgrowth examples on LMCats other than liquid copper,21e.g., PtSi,22 Ni,23 Ga,24 CuSn, and Sn,25 reveal the existenceof a broader family of LMCats, allowing the growth of graphene atsignificantly lower temperatures24 thancurrently used in the standard CVD growth on solid copper (1100–1300K).26 Moreover, the reports on the successfulsynthesis of h-BN,27 MoC,28h-BN-graphene heterostructures,27 GaN,29 thin oxides,30 and others21 showa broader range of LMCats applications allowing fast production ofhigh-quality 2D materials beyond graphene.Recently,liquid metal catalysts (LMCats), 12 and intense effortshave been made to optimize the CVD process. In general, even in thecase of solid catalysts, there are limited reports on real-time observationof graphene growth, with notable examples of those employing radiation-modeoptical microscopy,31 environmental scanningelectron microscopy,32 or in situ reflectance spectroscopy.33 However,on liquid catalysts, and contrary to the ultrahigh vacuum34 and low-pressure32 CVD on solid substrates, the lack of precise, multiscale in situ techniques enabling direct feedback on the growthparameters of atmospheric pressure CVD , has led primarily to empirical recipes.Such recipes intrinsically suffer from a limited understanding ofthe graphene formation process and low reproducibility of the productdue to the complex and stochastic nature of the growth phenomena.For the realization of a stable, continuous 2DM production process, in situ multiscale monitoring of defects and morphologiesfrom atomic to macroscopic scales and real-time feedback control onprocess parameters is mandatory as an additional level of complexityis added by the continuous movement, rotation, and mutual interactionsof graphene crystals on molten metal. However, until now, there weresignificant hurdles against the realization of in situ monitoring techniques for 2DM growth on LMCats, including heat andevaporation of the liquid metal, intense thermal radiation, curvedand dynamic nature of the liquid surface, and the presence of reactiveCVD gas at close to atmospheric pressure.Several examplesof graphene growth on LMCats with different morphologieshave been reported,insitu techniques for multiscale monitoring of graphene growthon liquid copper at 1370 K and under atmospheric pressure CVD conditions(see Supplementary Note 2). Radiation-mode optical microscopy, which has beenpreviously demonstrated during graphene growth on solid copper,31 provides essential information on growth morphologyand dynamics in real time at the macroscopic scales. We find (see Supplementary Note 3) that radiation-mode opticalmicroscopy is extremely sensitive to the thickness of the grown grapheneon liquid copper and allows us to demonstrate the growth of single-layergraphene (SLG) or to detect any part that is multilayer.31 Raman spectroscopy, which has been employed in situ (at high temperatures), confirms monolayer graphene’spresence and yields information about its crystallinity and defectsfrom mesoscopic to nanoscales. At the atomic scale, the lattice constantand corrugation of graphene sheets floating on liquid copper are derivedfrom Bragg rods’ position and angular spread measured by synchrotrongrazing incidence X-ray diffraction (GIXD). The number of graphenelayers, roughness, and the separation between graphene and liquidcopper are provided by synchrotron X-ray reflectivity (XRR). Real-timemonitoring allows us to tailor the crystal size, shape, and qualitywhile optimizing the growth speeds. This achievement should enable2DMs’ applications in domains for which reproducible specificationsare of paramount importance, e.g., microelectronicand photonic industries.35Here we report thesuccessful implementation of four in situ monitoring,we first show the results of CVD growth processes for which the nucleationof graphene seeds is induced by an injection of a short pulse of methaneat high partial pressure. This procedure produces many flakes thatgrow and form a superordered assembly due to short- and long-rangeinteractions, as explained by multiscale simulations. Ultimately,they merge into a continuous film; however, slight misorientationsof neighboring flakes remain upon their coalescence, ultimately leavingdomain boundaries where they have merged. We next use our monitoringand feedback-control possibility to improve the flakes’ orderingand reduce the remnant defects upon merging. Although this proceduredecreases the defect density, perfect stitching of neighboring flakescould not be achieved, as revealed by etching under higher hydrogenpartial pressure. Finally, we tailor the growth parameters to nucleateonly a single flake and grow it to millimeter size. The obtained spectrausing X-ray scattering and Raman spectroscopy compare well to thoseof single-layer exfoliated graphene.To demonstratethe wealth of information and control capabilitythat can be achieved by multiscale Movie 1 show real-timeoptical microscopy measurements recorded at different stages of atypical “pulse” CVD graphene growth on liquid copper.The “pulse” (applied between 0 and 10 s of Movie 1) refers to a sudden release of a highconcentration of methane gas (p(CH4)/p(H2) = 1/138) is maintained,leading to the gradual growth of the nucleated flakes (starting at10 s of Movie 1). These flakes are seenas white features appearing on the surface, as the area covered bygraphene has a slightly higher emissivity than liquid copper at thistemperature. For almost all flakes, a central seed consisting of aneedle-like 3D graphitic structure is visible. These structures areetched in the final stages of the growth,36 which is self-limiting to the SLG (see Supplementary Note 4). After a few more tens of seconds, the flakes increasein size and move closer to each other, indicating the presence ofan attractive long-range interaction between them. Strikingly, althoughthe flakes grow in size, they do not merge but remain separated bya gap, which is evidence of the presence of a short-range repulsiveinteraction between them. They next self-assemble in a near-perfecthexagonal network with a separation that converges to a constant gapvalue of 20–40 μm  while the flakes continue to grow. In the final stage of the growth(after ∼3 min 30 s), the aligned flakes start to merge. Ourgeneral observation is that the closure of the gaps coincides withthe moment that the assembly of flakes grows large enough to reachthe borders of the liquid copper pool. At this point, the orderingis perturbed, and only local order is preserved. The gap between theflakes slowly disappears with growth time, leading to continuous SLG(see below). The SLG nature of the flakes is unambiguously deducedfrom the contrast in radiation-mode optical microscopy. We have consistentlyobserved that adjacent flakes avoid any overlapping even when thegap between them vanishes and the layer closes. This is not surprisingas the adhesion energy between two overlapping graphene layers isless than between graphene and liquid copper .38 Also, oncethe edges of the adjacent coalescing flakes reach each other, theformation of covalent bonds between their unsaturated edge carbonatoms is energetically more favorable than the weak van der Waalsinteraction between hypothetically overlapping flakes. Such overlappingevents are energetically unfavorable for free-floating flakes.392 concentrationallows for etching of the layer, primarily attacking the defect sites,including domain boundaries (40 whereascompact cavities appear around point defects41 (see Supplementary Note 5).hane gas 1F into tMovies 1 and 2), domain boundaries are always present in thefinal continuous SLG, which is always polycrystalline. These boundariesare due to a fluctuation of the azimuthal orientation of adjacentdomains at the late coalescence stage, and they contain a higher densityof defects and impurities than the domain interior. This is surprisingfor a liquid substrate, where the lack of pinning sites and preferredorientation was expected to lead to continuous, domain-boundary-freegraphene. To date, a theoretical model for the self-assembly and persistentinterflake separation is still lacking. From gravitational interactions15 to the shape of the electrostatic potentialaround the flakes42 to the flow of gasesin the reactor,43 various driving forceshave been proposed, but none of these studies have provided a convincingtheory able to bridge the microscopic to mesoscopic length scalesinvolved. Here, we employ a multiscale modeling approach; that is,we carry out molecular dynamics (MD) simulations of hexagonal grapheneflakes on a liquid Cu surface  that the chargedistribution inside a mesoscopic hexagonal flake is essentially homogeneous,allowing us to approximate the repulsive electrostatic forces as simpledipole interactions. Making use of the calculated flake–substratedistance, Δh, of 2.86 ± 0.10 Å, wearrive at a dipole moment of 1.22 ± 0.04 D per C atom. We definethe flake–substrate distance as the distance between the graphenelayer center and the liquid copper surface inflection point on thegas-copper electron density profile. As shown in 45 and presently not considered electrocapillaryinteractions48 to provide an additional attractionthat would further shrink the optimum interflake distance.At the same time, the variable-charge COMB3 potentialIn situ Raman spectra acquired during graphene growth confirmedthat the grown film is indeed SLG (see Supplementary Note 7). At elevated temperatures, a 405 nm laser line was implementedto acquire Raman spectra in order to minimize the blackbody radiationbackground, however, at the expense of 2D peak intensity. As mentionedearlier, in situ Raman has successfully been employedfor obtaining Raman spectra in situ during Gr growthon molten Cu at elevated temperatures. In fact, such measurementsare mandatory to assess the nature (SLG) and quality of the grownGr. As expected, both 2D and G peak positions  are significantly blue-shifteddue to the anharmonic terms in the lattice potential energy, whichare determined by the anharmonic potential constants, the phonon occupationnumber, and the thermal expansion of the crystal.49 The Lorentzian peak shape and the relative positions ofthe 2D and G peaks are characteristic for SLG.50 The presence of SLG is further supported by exsitu Raman spectroscopy , theintense background engulfs the 2D peak, and therefore its full deconvolutionis problematic.To assess the chemical and atomic-scale structural properties ofthe growing graphene, we performed Raman spectroscopy 3A, B, XRtroscopy 3B with aIn situ XRR measurements  and self-healing. This could increase the2DM/LMCat interaction energy and lower the average separation distance.52 In addition, the presence of trace impuritieson the LMCat surface during CVD could also contribute to the discrepancybetween the experimental and theoretical separation values. However,this factor seems less probable due to the high purity of our system(see Supplementary Note 8). Insitu GIXD , only one rod is recorded within 60°of azimuthal rotation, which confirms single crystallinity of theflakes. The precise position of several hundredths of Bragg rods providesthe lattice constant (2.4601 ± 0.0005 Å at 1370 K). Thisvalue is slightly lower than the 2.4618 Å reported for graphiteat the same temperature.54 At variance with therods measured on suspended graphene sheets,55 the rods measured here do not show any increasing broadening inperpendicular qz direction,proving the damping of microscopic corrugations of SLG on liquid copper.urements 4A providurements 4B,from itu GIXD 4C,D confIn situ monitoringprovides a possibility to vary the growth conditionsand to observe the effects of these modifications in real time. Thissaves significant time and costs to find the right parameters to growhigh-quality 2DMs and improves the reproducibility of the produced2DM specifications. On the other hand, ex situ observationstypically need multiple time-consuming trials to optimize the processwhile remaining inevitably vulnerable to the stochastic nature ofgrowth mechanisms. An example of such real-time tailoring of the growthis presented in Movie 2, in which the processis manipulated by alternating growth and etching stages in order toimprove the flake ordering and size monodispersity. Initially, asshown in Movie 1, the pulsed injection of CH4 resulted in many hexagonally ordered flakes (Movie 2). However, the order is only short-rangewith two misaligned hexagonal networks. The azimuthal flake alignmentis better revealed in the 2D Fast Fourier Transform  by decreasing the CH4 pressure. This caused shrinkageof all flakes down to much smaller sizes (Movie 2), before resuming the growth at time6 min 42 s, which leads again to large organized flakes (Movie 2). However, because of atoo fast increase of p(CH4), the regrowthof previously nucleated graphene flakes was accompanied by a secondnucleation wave, resulting in a bimodal flake size distribution andonly partial hexagonal order (Movie 2), decorate the newly nucleated flakes. A second etching period(by decreasing p(CH4)) starting at time10 min 55 s resulted again in small disordered flakes (Movie 2). The methane pressure was then graduallyincreased, resulting (Movie 2 after 29 minand 30 s of growth) in the formation of a near-perfect hexagonal networkof almost monodisperse flakes /p(H2) ∼ 0.015resulted in the lack of any visible nucleation events. Only aftera few minutes, the nucleation (not shown) of a single flake havingthe shape of a hexagon with rounded corners was observed. After 8min, the ratio was increased to p(CH4)/p(H2) ∼ 0.02 in order to increase thegrowth speed. The nucleated flake gradually transformed into perfectlyhexagonal after about 12 min of growth /p(H2) ratioto etching conditions, we observe only slow etching at random pointson the flake , while more extended defects like domain boundaries are absent.Extensive Raman mapping  confirm its single-layer character. The lack of observation ofthe D peak confirms that the atomic defect density is below the detectionlevel. Furthermore, at the resolution of the mapping step (10 μm),the flake appears to be continuous without domain boundaries. Theaverage residual biaxial strain56 is minimal(∼0.1%) thanks to graphene’s weak bonding to liquidCu . Finally, thesheet resistance and the electrical conductivity were measured usingthe Van der Pauw method1 (see Supplementary Note 9) and found to be around280 Ω/sq and 1.1 × 107 S/m, respectively. Thesevalues are much higher than those obtained from the state-of-the-artgraphene growth via conventional CVD on solid metalcatalysts57 and are similar to those obtainedfrom exfoliated flakes.60 Therefore, we conclude that thegrown graphene film is of comparable quality to those obtained bytape exfoliation.61To circumvent this effect, we switched to radically different growthprotocols that aim at nucleating and growing only one seed/flake onthe entire surface. As shown in  methane6F. Initihe flake 6E. This  mapping 6G,H showin situ multiscalemonitoring of graphene growth on liquid copper via optical microscopy, Raman spectroscopy, GIXD, and XRR, and tailoringof the graphene growth thanks to direct feedback on growth parametersaccording to the observed changes in morphology, structure, or defects.The experimental observations are supported by multiscale modelingof short- and long-range interactions between graphene flakes, explainingtheir movement and assembly into a 2D hexagonal network of single-crystalgraphene on liquid copper. Three examples of such tailored growthsare given. In the first one, fast growth from multiple nucleationseeds results in single-layer but polycrystalline graphene sheetswith domain boundaries. In a second example, we showed that adjustingthe pressures of the different gases (here hydrogen and methane) inreal time allows for selecting the flake shape, improving their sizemonodispersity, and producing highly ordered flake assemblies duringjust one growth trial. In the last example, we show that startingfrom a single nucleation seed instead results in a macroscopic, single-crystalline,and single-layer graphene sheet of superb quality.Insummary, we demonstrated e.g., h-BN,62 GaN,29 or ultrathin oxide layers.30 With a growth speed of about ∼1 μm/s, a sheetresistance of 280 Ω/sq, and superior crystalline quality, thisprocess is practically viable and allows for single-crystal productionsuitable for different electronic applications. Since the viscousforces that hold the grown material on the liquid metal are extremelyweak compared to frictional forces on a solid, this process may alsobe suitable for an ambitious goal: continuous production. In thisprocess, one can imagine the forming graphene sheet gradually pulledaway from the liquid copper without cooling to room temperature, therebypreventing the wrinkling and folding due to differences in grapheneand substrate material thermal expansion.The presentedexamples demonstrate the power of real-time controlfor the growth of 2DMs, which can also be implemented for the scientificstudy or industrial production of other nanomaterial classes, insitu using X-ray-based techniques (Grazing-incidence X-raydiffraction (GIXD) and X-ray reflectivity (XRR)), Raman spectroscopy,and optical microscopy.63 The reactor hasa cylindrical shape with a wall consisting of X-ray-transparent beryllium.A quartz window is placed on top of the reactor providing opticalaccess for the Raman spectroscopy and optical microscopy measurements(see below). A custom-made heater assembly, capable of providing temperaturesup to 1600 K, is placed at the center of the reactor. The sample holderconsists of a tungsten disk in direct contact with the heater. Copperis added to the sample holder in the form of ultrapure foils. Copperis molten by heating the foils to 1370 K. A custom-built gas handlingsystem is used to mix reactant gases (CH4 and H2) with Ar background gas with controllable mass flow ratios, andto deliver the gas mixture to the reactor.All experiments were performed usinga setup suitable for the growth of graphene on liquid copper usingchemical vapor deposition. The growth can be monitored T = 1370 K) via chemical vapor deposition. Using the gas handling system, a 2% mixtureof CH4/Ar and H2 is added to the reactor ata total pressure of 200 mbar. The flow rates are 200 sccm for Ar,5–20 sccm for H2, and 0.1–20 sccm for CH4, respectively. The CH4 component dissociates catalyticallyon liquid copper, and the released carbon atoms form a graphene layeron the surface.Graphene is grownon liquid copper .Raman spectroscopyhas been successfullyemployed to characterize the growth of graphene on liquid copper.This technique is known to detect traces of precursor adsorbates,intermediate reaction species, and the grown graphene characteristics,such as the number of layers, the stacking type, the defect density,and the presence of dopants and/or contaminants. A 30 mW violet solid-statelaser with an excitation wavelength of 405 nm is used for the Ramanmeasurements in order to reduce the effects of blackbody radiationdue to the high temperatures. The probe is equipped with microscopiclong-working-distance objectives and is mounted on a motorized XYZpositioning system to adjust the positioning of the Raman objectivelens in the optical port. To filter out the excitation laser light,a 405 nm edge filter was used. For E = 22 keV,λ = 0.056 nm, and ΔE/E = 1.4 × 10–4) was deflected downward to the liquid copper surface via a double crystal deflector (DCD) using Ge(111) and Ge(220)Bragg reflections. The grazing-incidence angle was set at 2.1 mrad,which corresponds to 80% of the critical angle of total reflectionon the Cu surface at this energy. The beam size was 13 × 250μm2 (V × H). Two-dimensional diffraction patterns, e.g., GIXDsignals, were recorded using a Maxipix detector with a 1 mm thickCdTe sensor and 516 × 516 pixels of 55 μm in size. Thedetector was placed 922 mm downstream of the sample, and a vacuumflight tube was used to reduce air absorption and scattering. Thedetector size allowed measuring in one shot a reciprocal-space areaof 0.03 × 0.03 nm2. Each family of graphene Braggrods was measured by placing the detector at the expected in-planescattering vector (Qxy). Several strategies to detect the graphene Bragg rods were applied:(i) time scans with a step of 0.5 s to probe the azimuthal-rotationdynamics of the graphene crystals and (ii) azimuthal rotation of thedetector in the range −30° to +35° with steps of0.05° to probe the orientation of the graphene crystals. Severalhundreds of graphene Bragg rods on liquid copper were measured in situ at different growth conditions.The grazing-incidence X-raydiffraction (GIXD) experiments were performed at ID10 . The monochromatic X-ray beam (V × H). The reflected beam was measured with a 2D detector  located at 1085 mm from the sample. Prior to the XRRmodeling, the raw experimental data were background-subtracted. Theobtained curves were fitted with the REFL1D program. Our model includesthe liquid copper substrate, the graphene layer, and a gap in between.Variable parameters of the fit were the gap thickness and the roughnessof the copper and graphene interfaces. All other parameters  were fixed.The X-rayreflectivity (XRR) measurements were performed at the P08 beamlineof PETRA III . This beamline is equipped witha double crystal deflector (DCD) suitable for studies on liquid surfaces.The DCD deflects the X-ray beam downward to the horizontal surfaceusing Bragg reflections from the Si(111) and Si(220) atomic planes.The beam size of X-rays of 18 keV photon energy (wavelength 0.0688nm) was 40 μm × 200 μm . The images are converted to binaryformat, where value zero corresponds to the intensity of liquid copper,and value one to the intensity of graphene. The distance between theflakes is the distance between the centers-of-mass of the neighboringgraphene flakes, and the gap value (the distance between neighboringflakes) is extracted from a linear profile connecting those centers-of-mass.The sample surfacewas observedusing a custom-built digital optical microscope. It consists of long-working-distance,infinity-corrected objectives with magnification options of 5×,10×, 20×, and 50×, a tube lens with magnification 0.5×,and a CMOS-based digital camera. The system is capable of recording30 frames per second with a maximum resolution of 4096 × 3000pixels. The movies of the liquid copper sample and the growth of grapheneon top of it were recorded in the radiation mode;


Notably known for its extraordinary thermal and mechanical properties, graphene is a favorable building block in various cutting-edge technologies such as flexible electronics and supercapacitors. However, the almost inevitable existence of defects severely compromises the properties of graphene, and defect prediction is a difficult, yet important, task. Emerging machine learning approaches offer opportunities to predict target properties such as defect distribution by exploiting readily available data, without incurring much experimental cost. Most previous machine learning techniques require the size of training data and predicted material systems of interest to be identical. This limits their broader application, because in practice a newly encountered material system may have a different size compared with the previously observed ones. In this paper, we develop a transferable learning approach for graphene defect prediction, which can be used on graphene with various sizes or shapes not seen in the training data. The proposed approach employs logistic regression and utilizes data on local vibrational energy distributions of small graphene from molecular dynamics simulations, in the hopes that vibrational energy distributions can reflect local structural anomalies. The results show that our machine learning model, trained only with data on smaller graphene, can achieve up to 80% prediction accuracy of defects in larger graphene under different practical metrics. The present research sheds light on scalable graphene defect prediction and opens doors for data-driven defect detection for a broad range of two-dimensional materials. The emergence of nanomaterials research has shown promise in influencing technological applications such as stretchable electronics and supercapacitors ,3,4,5,6.The development of machine learning approaches enables predictions of target properties using learned patterns from data, and has been gaining momentum in materials prediction, design, and discovery ,24,25,26Another challenge lies in the feature selection. Because whether a certain location on graphene is next to a defect or not is local information , selected features need to sufficiently reflect graphene local anomalies. Graphene stiffness, failure strength, and strain, among other collective properties, may not be suitable, because different defect distributions may achieve very similar observations . ThermalIn this paper, we propose a transferable learning strategy to detect unknown defects in larger graphene sheets using information obtained from a smaller graphene system. Trained by tens of thousands of local vibrational energy distributions of smaller graphene sheets calculated by MD simulations, our machine learning model is used to predict whether certain locations on larger graphene sheets are in the vicinity of any defect. From predicting graphene sheets that contain only one defect to predicting an unknown number of defects with an arbitrary distribution, a logistic regression model is applied, of which the performance is quantified by three practical metrics: total accuracy, true positive rate, and true negative rate. Finally, by adjusting the weights associated with defects and non-defects in the cost function, we aim to find a way of improving the prediction accuracy of defects while maintaining a relatively low false positive rate.Geometries of defect-containing monolayer graphene sheets used in this study are shown in MD simulations were used to compute the vibration of graphene, performed using the open-source code LAMMPS  . The ful9 s−1. The small tensile pre-strain was applied to imitate the experimental setup of graphene vibration in [AIREBO potential has been widely used to study the mechanical behavior of defect-containing graphene ,33,34,35ation in . FinallyThe data preparation procedure for machine learning is shown in the flowchart in Our next goal was to associate the vibration of each atom to a scalar. To this end, based on the frequency response we calculated the energy as The cost function can be expressed as:In this study, logistic regression was performed using the machine learning library Scikit-Learn .For our binary classification problem, we quantified prediction accuracy by the following three metrics: total accuracy (TA), true positive rate (TPR), and true negative rate (TNR), expressed below:The prediction of one-defect graphene sheets was studied first. The preparation of training data based on the smaller graphene is detailed as follows: First, we discretized the entire vibrating graphene domain with an The preparation of test data based on larger graphene sheets differed from the training data, as is outlined below. The number of larger graphene sheets for machine learning testing is 875. Each provided five randomly chosen query points that were at least two grids away from graphene edges. The prediction of query points within two grids to graphene edges is assigned to the With the successful implementation of predicting one-defect graphene sheets, we move on to the more complex multiple-defect cases. The total number of smaller multiple-defect graphene sheets for training is 10,108. The same as one-defect scenarios, the vibrating graphene domain was discretized by an The preparation of multiple-defect test data based on the larger graphene sheets is outlined below. A total of 621 larger graphene sheets were used and each provided five randomly chosen query points at least two grids away from graphene edges. The prediction of query points within two grids to graphene edges is shown in It is desired that the prediction accuracy of a defect in larger graphene sheets can be further improved, and that the fine-tuning of TPR and TNR is enabled as we emphasize the prediction of defects or non-defects as needed. A solution to this can be offered by a weighted version of the cost function, expressed as:By tuning the weights In this work, due to the randomized atom removal process of defect creation, most defects assigned to graphene sheets are unreconstructed single vacancies. Even though unreconstructed single vacancies have been observed in experiments  and studIn summary, we present a transferable learning strategy to detect unknown defects in larger graphene sheets using information obtained from only a smaller graphene system. Trained by tens of thousands of local vibrational energy distributions in uniformly sized smaller graphene sheets from MD simulations, our logistic regression model can predict defects in unseen larger graphene sheets with satisfactory accuracies for both one-defect and multiple-defect graphene sheets, quantified by three practical metrics: TA, TPR, and TNR. Through adjusting the weights associated with defects and non-defects in the cost function, we achieve an improvement in the prediction accuracy of defects while maintaining a relatively low false positive rate. The present research sheds light on scalable graphene defect prediction and opens doors for accelerated data-driven defect detection of a broad range of two-dimensional materials. Future work includes the further improvement of prediction accuracies utilizing more advanced machine learning models as well as the prediction of other types of defects such as substitutions and dislocations, among others.


Scalable production of large size and high quality graphene is an important prerequisite to fully realize its commercial applications. Herein, we propose a high-efficient route for preparing few-layer graphene. The secondary exfoliation of unexfoliated graphite flakes from electrochemical exfoliation was achieved by using ultrasonication assisted microwave exfoliation technique. The results show that the as-prepared sample has a C/O of 15.2, a thickness of about 1 nm and a transverse dimension of over 100 nm, and the Raman spectrogram shows low defects upon reduction of the sample. These results suggest that electrolytic graphene can be exfoliated to form graphene nanosheets under ultrasonic-assisted microwave technology, thus indicating that the current method has great potential for synthesizing high-quality graphene at an industrial-scale. The pec2 ,4. Nanoc2 ,6,7,8. N2 . Top-down graphene production involves the reduction or exfoliation of powdered graphite, with the most prominent top-down methods being mechanical exfoliation, LPE, electrochemical exfoliation and chemical redox ,11. The Since Geim and Novoselov’s discovery of graphene in 2004 , prepariIn recent years, non-chemical reduction has gradually attracted the attention of researchers. Carbon materials are excellent microwave absorbers and can be easily heated by microwave radiation, a property that allows it to be transformed by microwave heating to produce new carbonaceous materials with specific properties ,24,25,26Herein, the secondary exfoliation of unexfoliated graphite flakes from electrochemical exfoliation was achieved by using an ultrasonication assisted microwave exfoliation technique. The raw material used is flake graphite, which could not be used directly in our method because of their small size. Thus, they had to be fabricated into macroscopic electrodes to ensure voltage supply. In the post-treatment process, we utilized ultrasonic-assisted microwave technology for the secondary exfoliation of electrolytic graphene. Moreover, we discuss the various characterization results of the experimentally prepared graphene. The experiments were conducted by electrochemical exfoliation method to prepare graphene, the graphite powder was briquetted and perforated on the anode titanium rod, and the area not in contact with the graphite block was sealed with wax, and then the graphite block was wrapped using melt-blown cloth to make the anode structure, and the cathode structure was surrounded by the anode using stainless steel ring, and the electrolysis device and schematic diagram of the principle are shown in 2SO4 ; NH3·H2O ; HNO3 ; sodium dodecyl sulfate ; Beaker (2000 mL); titanium rods and; melt-blown cloth .Materials included: graphite ; HGraphene was prepared by electrolysis, firstly, the beaker was placed on the magnetic heating stirrer, appropriate amount of deionized water was added to the beaker, concentrated nitric acid and concentrated ammonia were added under magnetic stirring, then concentrated sulfuric acid was slowly added, then sodium dodecyl sulfate was added. The specific parameters of the solutions and reaction conditions are shown in A small amount of the prepared EGO was added to the beaker with the appropriate amount of distilled water. The beaker was then placed in the sonication unit for 15 min and the solution was finally filtered and dried to obtain the sonicated sample.The as-prepared EGO was added to the quartz crucible in a small amount, and then the crucible was put into the microwave tube furnace , so that the furnace was filled with argon gas and kept in and out of gas equilibrium, under this condition, the microwave power was steadily increased to 800 W by opening the serial port and kept for 60 s, and then the micro wave was turned off and the sample was taken out when the crucible was cooled.−1. Raman spectroscopy was used for the qualitative characterization of graphene samples. The Raman spectroscopy test equipment was a Horiba LabRam HR Evolution Raman  spectrometer with a continuous laser at 532 nm as the excitation source.The surface morphology of graphene was characterized by a field emission scanning electron microscope  and TEM . The sample thickness was characterized by atomic force microscope . Crystalline structure of the as-prepared samples were investigated by powder X-ray diffraction  at a scan rate of 6° minThe chemical composition of the as-prepared EGO was analyzed by combustion elemental analyzer (EA), and shows that EGO has a typical composition (at. %) of C (58.5), O (24.6), H (7.2), N (3.8), and S (5.9). This gives C/O atomic ratio of around 2.4, which indicating a high oxidation degree of EGO. We performed microwave reduction of EGO and found that several-fold expansion occurred in a short time and C/O atomic ratio increased to 15.2 in this process, which was caused by the thermal effect of π-electrons in carbon material inducing a current that propagates in the same phase as the electromagnetic field, resulting in the detachment of oxygen-containing functional groups from the carbon ring. Then, the as-prepared reduced EGO (rEGO) was characterized by AFM, SEM, XRD, and Raman.We then used an AFM, SEM, and TEM to characterize the number of layers and lateral size of rEGO. AFM can characterize the physical properties of materials including morphology and mechanical properties at the nanometer level, and is unique in terms of layer identification, and SEM has the advantages of nanometer resolution, large observation range and high speed, and is often used to characterize the morphology of grapheme ,28,29.The monolayer rEGO sheets show a thickness of about 1.0 nm a which i−1 and is caused by the symmetric stretching vibration  of the sp2 carbon atom in the aromatic ring. It also requires a defect to be activated. The G peak is caused by the stretching vibration between sp2 carbon atoms, which corresponds to the vibration of the E2g optical phonon in the center of the Brillouin zone. It generally appears near 1580 cm−1, while the 2D peak generally appears near 2680 cm−1 and is caused by the double resonance leap of two phonons with reverse momentum in the carbon atom [G/I2D, and the peak shape of the 2D-peak can determine the number of graphene layers [Raman spectroscopy is one of the most commonly used, rapid, non-destructive, and high-resolution techniques to characterize carbon materials. There are three main peaks on the Raman spectrogram of graphene: D, G, and 2D peaks . The D pbon atom ,33,34. Ie layers .D/IG) represents the degree of defects in EGO, which are caused by the formation of oxygen-containing functional groups on the carbon ring during the electrolysis process. The defects were found to not reduce well after the ultrasonic treatment due to the mechanical vibration that did not remove the oxygen functional groups well. Therefore, we carried out a microwave reduction treatment, during which we used different atmospheres  for the microwave reduction of EGO, as shown in D/IG decreased from 0.92 to 0.11, which means a large reduction of defects in rEGO. And IG/I2D decreased from 2.73 to 1.58 and the number of layers of graphene was well controlled. Here, we observed that the relative intensity of the rEGO D-peak after microwaves decreased, corresponding well with EA results, and indicating that ultrasound assisted microwave technique has a good effect on the improvement of EGO defects. The graphene oxide prepared by electrolysis has a D-peak . The relG/I2D decreases with the decrease of the number of layers. We can find that the ratio of IG/I2D decreases in the process of being reduced, which indicates that our reduced EGO has a good quality. This is a considerable improvement in defect and layer control, especially compared to the graphene prepared experimentally by Wang et al. [For graphite structures with more than five layers, it is difficult to distinguish each using Raman spectroscopy . In the g et al. .The crystal structure of the pristine graphite and the as-prepared rEGO were analyzed by conducting X-ray diffraction (XRD) studies . The priWe made the following speculations on the exfoliation process of graphene during the experiments. Under the action of voltage, the ions in the solution enter between the graphite layers and cause the graphite flakes to undergo micro-expansion. During the ultrasonic treatment of the sample, further exfoliation of the graphite interlayer occurs and the ensuing microwave decomposes the graphite interlayer ions and the following decomposition occurs:The microwave process, the decomposition of graphite interlayer ions under the effect of high temperature produces a large amount of gas, and the longitudinal thrust generated by the gas is greater than the van der Waals force between the graphite layers, which makes the graphite rapidly expand and separate to form graphene nanosheets. Using elemental analysis, we determined that EGO had a high oxygen content (15.2), which indicates that the graphite was successfully oxidized during the electrolysis process. The oxygen content of the rEGO obtained after the microwave treatment was reduced well, which indicates that the microwave has a good reduction effect on EGO. Through morphological observation and structural characterization, we found that the prepared graphene has excellent quality in terms of layers and defects, and a thickness of about 1 nm and a transverse dimension of over 100 nm. This has lower defects and larger transverse dimensions than the graphene prepared by electrochemical methods by Pei et al. .4NO3 and H2SO4 mainly act as control agents to tune the anodic electrocatalytic oxygen evolution reaction of water to enable the ultrafast oxidation of graphene lattice. In this process, no other oxidants were used. Therefore, there was no metal ion contamination in this method. The electrolyte was used secondarily because the anode structure is wrapped by melt-blown cloth. Second, in Hummers’ method, graphite oxide is uniformly mixed with concentrated sulfuric acid and other oxidizing agents to form a viscous slurry. The graphite oxide, as a result, is much easier to clean in our method, requiring five times less water. Third, our method is controllable; the degree of oxidation can be adjusted by changing the electrolyte concentration during the oxidation process. Thus, our electrochemical oxidation method combines the advantages of safety, easy control, environmental friendliness, and no metal ion pollution, thus paving the way for low-cost industrial production and application of graphene.The above results suggest that EGO synthesized with electrolytic oxidation of graphite powder is conceptually different from Hummers’ method. In this electrochemical method, NH