This paper describes a new automated disengagement tracking system (DTS) that detects learners’ maladaptive behaviors, e.g. mind-wandering and impetuous responding, in an intelligent tutoring system (ITS), called AutoTutor. AutoTutor is a conversation-based intelligent tutoring system designed to help adult literacy learners improve their reading comprehension skills. Learners interact with two computer agents in natural language in 30 lessons focusing on word knowledge, sentence processing, text comprehension, and digital literacy. Each lesson has one to three dozen questions to assess and enhance learning. DTS automatically retrieves and aggregates a learner's response accuracies and time on the first three to five questions in a lesson, as a baseline performance for the lesson when they are presumably engaged, and then detects disengagement by observing if the learner's following performance significantly deviates from the baseline. DTS is computed with an unsupervised learning method and thus does not rely on any self-reports of disengagement. We analyzed the response time and accuracy of 252 adult literacy learners who completed lessons in AutoTutor. Our results show that items that the detector identified as the learner being disengaged had a performance accuracy of 18.5%, in contrast to 71.8% for engaged items. Moreover, the three post-test reading comprehension scores from Woodcock Johnson III, RISE, and RAPID had a significant association with the accuracy of engaged items, but not disengaged items. Many intelligent tutoring systems (ITSs) implement natural language dialogue and provide one-on-one human-like tutoring in an automated fashion . A well-Regardless of whether students learn from an ITS, a human tutor, or a teacher in a classroom, students are likely to become disengaged due to various reasons, such as fatigue, environmental distractions, loss of interest, or the stress of falling behind in a course, as will be elaborated below. One strategy that ITS developers have taken has been to increase engagement through gamification , but stuDisengagement occurs in a number of situations, such as when the student is 1) mind wandering , 2) distDisengagement also presents a problem for researchers interested in evaluating learning and performance. Time on task alone  can be considered contaminated by disengagement in contrast to diligent efforts to complete the task. Disengaged students may take too long a time (thinking about something irrelevant to the reading task) or too short a time (quickly finishing the question or session without comprehension) on a given question, problem, text or session. That is, a disengaged reader can be extremely slow or fast in processing during a learning task with low performance. Data analyses that do not consider the abnormal reading time due to disengagement may lead to unreliable or even misleading results. Moreover, a simple unidimensional measure of time is not sufficiently diagnostic of disengagement because both very fast times and very slow times can be signals of disengagement.Existing disengagement/engagement detection methods that focus on mind wandering have applied supervised learning approaches to train models using self-reported mind-wandering  or use oN = 52) who completed up to 30 lessons in a computerized learning environment (AutoTutor) that was part of a 4-month intervention that trained them on comprehension strategies. Answer time alone was not sufficient to identify the incidence of disengagement because accuracy in answering the questions is obviously important. Therefore, questions outside of the zone of engagement, “disengaged question-answer observations” were defined as being answered incorrectly and too quickly or slowly. This approach to identifying disengaged observations was completed after the 4-month study was completed. Unfortunately, this method, however, is not suitable for monitoring concurrent disengagement since disengaged question-answer observations can only be detected at the end of a reasonably large sample of lessons. That does not allow an intelligent learning environment to give feedback and guidance to the learner when disengagement is detected. Moreover, if a question is incorrectly and slowly answered, it may not necessarily indicate disengagement. It is possible that a student is at the very early stage of learning new material and spending time in productive comprehension activities. Nevertheless, an approach to detecting disengagement based on the accuracy and time to answer questions during training is a reasonable approach to building a disengagement tracking system. It does not require special physiological or neuropsychological sensing devices, eye tracking, self-reports of engagement, or machine learning with supervised training that cannot scale up to real-world applications. The approach would be more useful to the extent it could detect disengagement in a smaller time span, such as a minute or two.Graesser, Geenberg, Frijters, and Talwar (submitted) identified questions that a student answers that are within the student's “zone of engagement”. These “engaged question-answer observations” included questions that were answered neither too slowly nor too quickly (within ± 0.5 standard deviation of mean log of response time), based on a student's personal average speed of answering questions in a lesson. The participants were struggling adult readers (AutoTutor for Adult Reading Comprehension (AutoTutor-ARC), a version of AutoTutor to teach adult learners reading comprehension strategies. In AutoTutor systems, a tutor agent and optionally a peer agent hold conversations with a human student. When the conversation has two agents (tutor and peer), the conversations are called trialogues, as opposed to tutor-student dialogues , or mind wandering. We implemented the proposed algorithm to predict/monitor disengagement in AutoTutor-ARC. Our results show that items that the detector identified as the learner being disengaged had a performance accuracy of 18.5%, in contrast to 71.8% for engaged items. Moreover, three post-test reading comprehension scores from Woodcock Johnson III, RISE, and RAPID had a significant association with the accuracy of engaged items, but not disengaged items. The development of DTS algorithm is motivated by response time and performance data generated by the users of AutoTutor-ARC system. DTS has not been used in any intelligent system yet. The validation analyses in the manuscript can be considered as a “low stakes” application of DTS. If successful at detecting disengagement, the proposed real-time disengagement tracking system could be of value in enhancing learning efficiency in future AutoTutor-ARC systems, if it can be coupled with interventions during a lesson that re-engage a disengaged student. The algorithm could also be applied to other computer-based learning or assessments that utilize a question-answer environment.http://csal.gsu.edu). AutoTutor-ARC is a web-based intelligent tutoring system with 30 lessons focusing on building reading comprehension strategies  later in the manuscript) on each question is recorded per lesson per student.The lessons typically start with a 2–3 min video that reviews the comprehension strategy that is the target of the lesson. After the review, the computer agents scaffold students through the learning by asking questions, providing short feedback, explaining how the answers are right or wrong, and filling in information gaps. Since adult learners in AutoTutor typically have substantial challenges in writing, AutoTutor tends to rely on point-and-click (or touch) interactions, multiple-choice questions, drag-and-drop functions, and other conventional input channels. The learner chooses the answer by selecting an answer, while the peer agent sometimes gives his answer by talking. Flow within each lesson is driven by either a fixed sequence or contingent branching. The first set of question-answer items within a particular lesson is the same for all students who take the lesson. Fixed sequence lessons deliver the same set of conversational questions to all students, independent of their performance throughout the entire lesson. Contingent branching lessons start out with questions and materials at a medium level of difficulty, but subsequently shift to harder or easier materials/questions depending on their performance on the medium difficulty material . For exaThe data sets used to test the proposed algorithm were taken from three waves of an intervention study in two medium sized cities. Participants were 252 adult learners who were offered approximately 100 h of instructional intervention designed to improve their reading skills. The intervention period lasted over 4 months and was implemented in hybrid classes, which consisted of teacher-led sessions and the computer-based AutoTutor-ARC sessions. Their ages ranged from 16 to 74 years  and 74.6% were female. The reading level of participants ranged from 3.0 to 7.9 grade equivalencies. On average, the 252 participants completed 30 lessons. The adult students were also assessed with three standardized tests of comprehension before and after the instruction.The AutoTutor-ARC content  were scaled according to An automated disengagement tracking system (DTS) is ideally personalized to the response times of individual students who work on a particular lesson. For any given student, a DTS should adapt to the learner's pattern of engaged performance, that is, the typical response time when engaged in attending to lesson content. Disengagement is detected when a student's performance (reading time or accuracy in answering questions) significantly deviates from this ‘typical’ pattern. In AutoTutor-ARC, a student is asked to read a text or sentence and to answer questions that are woven into the conversation between the two agents and the student. The system records the time that this student spends on each question and whether a question is answered correctly . The amount of time a student takes to respond to a question, namely the response time (RT), is one behavior that can be used to determine whether a student is disengaged while working on this question. Performance suffers when the student is disengaged. One indication that students are disengaged is that they respond too fast or too slow  on a question. Too short or long RT does not necessarily mean “disengagement”, since other factors influence RT. However, the short and long times can often be signals that probabilistically predict disengagement. For example, a short RT could be impetuous responding or gaming the system, whereas a long RT may be a difficulty level shift in texts/questions, mind wandering, or a personal bio break. To supplement the validity of RT indicator, we can consider another indication of disengagement: a significant drop in the correctness rate of a student. Thus, the DTS detects questions that a student is disengaged using both indications together. A flow chart of the DTS algorithm is shown in The top half of In the first phase at the beginning of a lesson, the DTS obtains the distribution of a student's engaged RT on questions. Response time is usually right-skewed, as is the current data set, so a log transformation was applied to make the data resemble a normal distribution. We assume an engaged student's b correctly answered questions and start to get disengaged at the s should be greater than or equal to b for the system to learn a user's engaged response time in a specific lesson. If b correctly answered questions as engaged response time. If there are less than b correct question-answer observations up to the b's response time as engaged response time instead. We excluded question #1 since the users usually take extra time to read the text in the first question and spend much longer time than usual. Let I be the first b correctly answered questions, whereas μ and σ are estimated byb correctly answered questions if they believe that there are extreme outliers in the s if the standardized response time satisfiesWe will now turn to some of the mathematical specification of the DTS algorithm. Suppose that students are engaged on the first X, but may be too fast to be engaged for student Y on the same question. Learners usually get disengaged for a variety of reasons. They may even get disengaged at different questions in different trials of the same lesson. Furthermore, an individual's reading ability may vary depending on the characteristics of the texts  included in each lesson. Because of these sources of variation, the system is required to learn engaged RTs (or reference behaviors) for each learner within each lesson.To have an adaptive DTS, the mean and standard deviation of engaged RT should be personalized for different students in different lessons. This means that 20 s may be an engaged RT for student Disengagement detection that is only based on response time would lead to a large number of false positives. Some lessons have a small number of “confidence-boosting” questions, which means learners will respond more quickly with high accuracy to these questions than to others. Students may slow down in subsequent questions that are more challenging, which may be falsely detected as disengagement by an DTS that only relies on response time. Other than “abnormal” response time, another important signal of disengagement is that disengaged students usually perform poorly since they are not focusing on the question. If a good student  responds to a particular question too fast or slow and also answers this question as well as neighbor questions incorrectly in a sequence, there is a high chance that this student is disengaged while working on the particular question. However, if a student performs poorly throughout the entire lesson, DTS should not categorize the questions with abnormal response time and poor performance as disengagement since the student may be struggling with this lesson, but not just disengaged on a few questions. As noted, in this study, DTS only identifies disengaged question-answer observations when assuming that the texts and questions are within the zone of what the student can handle and the student is engaged at the beginning of each lesson. Our targeted disengaged question-answer observations are those with “abnormal” (too fast or too slow) response times, poor local performance, but adequate overall performance. Students with low performance and engagement throughout the entire lesson or study is important also. It is possible that the content of the texts or questions may be too difficult. It is important to note that these questions will not be treated as ‘disengaged’ by DTS in this study.s), defined as A more formal specification of the algorithm may lend clarity. Let It is important to reiterate that the proposed DTS algorithm will not handle occurrences when a student is disengaged from the very beginning. These occurrences would not be counted as disengagement (even though they should be) so our predictive algorithm is conservative rather than been generous at detecting disengagement and such observations will dilute the predictive power of the DTS algorithm. It is also important to acknowledge that the algorithm has not yet been validated by self-reports of disengagement, eye tracking, and neurophysiological measures so the precise psychological status of the disengaged observations await further research. That being said, D’Mello and has colleagues  have proThe proposed DTS is designed to be a real-time monitoring of disengagement in an intelligent system. Using the predicted engagement/disengagement status for individual questions, we explored the pattern of disengagement in the AutoTutor-ARC as an empirical evaluation of the algorithm (It is important to clarify that DTS was not used during the CSAL AutoTutor study. It was developed after the end of the study.). For each of 252 students, we calculated the proportions of disengaged items, including fast- and slow-disengaged question-answer observations. A k-mean clustering analysis was applied to develop student profiles on proportions of the two types of disengaged question-answer observations. K-means clustering assigns data points into groups by iteratively reassigning and re-averaging the cluster centers until the points have reached convergence . GroupinAs an independent evaluation, learning gains were analyzed on subsets of the 252 students who took three standardized tests of comprehension before and after the larger CSAL AutoTutor intervention that included AutoTutor-ARC lessons. Of the 252 participants, 205 took both pre- and post-test of the Woodcock Johnson III Passage Comprehension subtest ; 143 tooTo investigate whether the learning gain is affected (presumably reduced) by disengagement, we performed paired t-test on the pre- and post-test scores of the three standardized tests after contrasting groups of students with different disengagement patterns at a fine grain level . These groups were obtained from the clustering analysis of the 252 participants on proportions of disengaged question-answer observations. It should be noted that these DTS-based clusters are different from the aggregate-based clusters identified by Fang et al. (submitted). We compared the two different types of clusters in this paper. Moreover, we tested the association of learning gain measured by the three standardized tests with the AutoTutor accuracy (i.e. proportions of questions correctly answered by students) of 252 participants. We first separated engaged and disengaged question-answer observations detected by the proposed DTS. For engaged (or disengaged) questions, reading comprehension at post-test was regressed onto the accuracy of engaged (or disengaged) questions adjusted by reading comprehension at pre-test.p-value We applied the proposed DTS algorithm to the data extracted from AutoTutor . We identified 16,851 questions with “abnormal” response times, of which 3,082 were disengaged question-answer observations  among the 252 participants. After aggregating the total number of questions from the lessons, we obtained the frequencies and proportions of disengaged question-answer observations for each of the 252 participants. The total number of questions that a student answered varied from Some students tend to have a higher proportion of fast-disengaged question-answer observations, whereas others have more slow-disengaged question-answer observations and yet others are high in both. To address this, a k-mean clustering analysis was performed on groups of students with similar disengagement patterns according to the DTS algorithm. Since students answered a different number of questions, we focused on the proportion (rather than the count) of fast- and slow-disengaged question-answer observations for each participant. The k-mean clustering analysis was implemented in R (version 3.6.0) on the proportions of fast- and slow-disengaged question-answer observations. We clustered the 252 participants into four groups (k = 4) according to the “elbow” method by visualizing the plot of “number of clusters” vs. “within groups sum of squares”.HiFast/HiSlow for short) represents students with a relatively medium-to-high proportion of fast-disengaged question-answer observations (2%) and a comparatively high proportion of slow-disengaged question-answer observations (7%). The second cluster (deep blue dots in LowFast/HiSlow for short) includes students with a small proportion of fast-disengaged question-answer observations (1%) and a medium-to-high proportion of slow-disengaged question-answer observations (4%). The third cluster (aqua blue dots in HiFast/LowSlow for short) represents students with a high proportion of fast-disengaged question-answer observations (3%) and a small proportion of medium-to-high slow-disengaged question-answer observations (2%). The last cluster (green dots in Engaged for short) represents students with small proportion of fast-disengaged question-answer observations (1%) and small proportion of slow-disengaged question-answer observations (2%). p-valueEngaged students by DTS, which is higher than “conscientious” and “struggling readers” (42% = 13/31) and much higher than “under-engaged” reader (34% = 32/93). Furthermore, when considering the students with local disengagement , the conditionalized percentages on on the slow end rather than the fast end were : higher performers (41/47 = 87%), conscientious (7/18 = 39%), struggling (14/18 = 78%), under-engaged (49/61 = 80%); low relative percentages for the conscientious readers is unexpected, but perhaps can be attributed to the relatively small number of observations.The current classification based on local engagement  was compEngaged. In contrast, Fang et al. categorized readers with low global accuracy to “under-engaged.”The major discrepancy between the two clustering approaches can be attributed to the fact that DTS was developed to detect disengaged question-answer observations, rather than disengaged students. Thus, DTS only checks the accuracy of answers to a question locally (i.e. accuracy of neighbored questions), not globally (e.g. accuracy within lessons that accumulated over the 4-month intervention). In our study, a student is considered to be disengaged while working on a question if his/her performance on this (and neighbored) questions is lower than their global performance. If a student has a low accuracy throughout the entire lesson, DTS will count these question-answer observations as The next analysis computed the proportion of fast- and slow-disengaged question-answer observations among the 252 participants within each of the 30 lessons separately. A subset of the lessons have one or two texts with conversation-based questions woven into the lessons. Eleven of the lessons have multi-sentence texts that branched during the course of the lessons. For each of these lessons with branching texts, the AutoTutor system starts with a medium difficulty text with 8–12 questions and then branches to an easy or hard text, depending on the student's performance on the questions in the medium difficulty texts. A second set of nine lessons provide one medium level text with 10–20 questions woven into the conversation about the text. A third set of 10 lessons focused on single words or sentences rather than multi-sentence texts. These lessons had 10–30 questions that were scaled on easy, medium or difficult levels. When considering all 30 lessons, the questions at the medium difficulty level constituted the majority of questions. Since some lessons contain questions of different difficulty levels, we evaluated the proportion of fast- and slow-disengaged items stratified by difficulty levels of questions for 252 participants in the 30 lessons. Disengaged Question-Answer Observations. For each person and lesson, we calculated the proportion of disengaged question-answer observations. To test whether the disengagement rate of lessons from one theoretical level is different from another, a linear mixed model was conducted while adjusting the correlated observations due to the same student. The results revealed that lessons in the Word (W) theoretical level had the highest disengagement rate , textbase (TB), Referential Situation Model (RSM), and Rhetorical Structure (RS), based on p-valueComprehension was evaluated by three standardized tests . There was a pretest before the 4-month intervention and a posttest at the end of it. Learning gain is calculated by the difference between pre- and post-test. To assess whether disengagement has an effect on learning gains in AutoTutor, we separated disengaged question-answer observations from the engaged ones and tested the association between learning gains from three standardized tests of comprehension and the accuracy rate  in AutoTutor on disengaged and engaged observations respectively. Regression analyses were conducted on the learning gains in the comprehension tests as a function of the AutoTutor intervention with engaged vs. disengaged question-answer observations. These results are presented in This paper provides a disengagement tracking system (DTS) with an intelligent algorithm to monitor students’ disengagement based on their response time and performance on each question during their learning process in AutoTutor. A variety of approaches have been applied to predict and track disengagement in intelligent tutoring systems . Existinin vivo as a performance monitoring algorithm within an intelligent tutoring system.The proposed DTS consists of two steps. In the first step, the algorithm learns a student's baseline response time from his/her first 3Our study of disengaged question-answer observations in AutoTutor that were identified by DTS is consistent with the claim that disengaged observations have substantially lower accuracy on AutoTutor items whereas engaged observations high performance. This is a confirmation of the internal validity of the algorithm. Evidence of external validity was also confirmed in analyses of learning gains on comprehension skills that were measured by independent psychometric tests . LearninDisengagement detection and monitoring is of course important for improving learning in conventional learning contexts as well as intelligent tutoring systems . A few IThere are a number of limitations in this study that call for follow-up research. First, we assumed that the log-transformed response time follows a normal distribution, and hence an “abnormal” response time can be identified if a log-transformed response time falls outside of ested by . Thus, aSecond, the DTS algorithm assumes that questions in a lesson are similar/interchangeable in terms of the lesson content and difficulty. Third, the algorithm does not detect situations when the student is disengaged from the material at the beginning of the lesson. For the DTS to be meaningfully applied to AutoTutor, we assume that texts/questions given to students are suitable for them and have some modicum of value and/or interest. This is a plausible initial assumption because the lessons focus on subject matters that have value for struggling adult readers  or are interesting to adults. Hence, students presumably start out engaged in most of the questions and may be disengaged on a number of questions some time later. If a student is disengaged in the beginning of a lesson or disengaged from most of the questions, DTS would need to be adjusted with a different algorithm to improve its predictions.Fourth, there are a number of other situations that the DTS algorithm would need to be modified to handle. The proposed algorithm does not consider any intervention to re-engage the students. DTS needs to be adjusted if any intervention action is taken after a disengaged question is detected. If users encounter frequent technical issues in the early/testing stage of a new ITS system, the data should take that into consideration. DTS run the risk if identifying “false alarms in disengagement” or “misses in disengagement observations” if the questions at the early phase of a lesson are unusual and fail to calibrate their performance when engaged.In summary, DTS provides an algorithm that can automatically predict/monitor disengaged behaviors in other learning environments that collect self-paced responses to question-answer items during training. It was designed for, but not limited to, the AutoTutor-ARC system. It can be tailored to fit any ITSs. In the proposed algorithm, only the response time and accuracy of each question are utilized to predict disengagement since they are the only relevant items that are recorded by AutoTutor. If other predictors or measurements, such as item difficulty, self-reported engagement or student's gaze patterns captured by a commercial eye tracker are available in different intelligent tutoring systems, they can be incorporated into the proposed DTS with appropriate modifications to the proposed algorithm. These other sources of data can also be used to validate the DTS algorithm. Of course, these other measures may be difficult or impossible to collect when scaling up a learning system in the real world.


Our devices show an enhanced photoresponsivity (~ 800 AW−1) while the response speed is still fast (up to 10 μs). This photoresponsivity is about two orders of magnitude higher compared to that of a previous graphene-based photodetector. The photodetector exhibits a wide-range tunability via source-drain bias and back gate voltage. Our work addresses key challenges for the photodetectors and potentially provides the desired pathway toward practical application of graphene photodetectors that can be externally manipulated by an electric field with fast response speed and high sensitivity.Graphene has been demonstrated to be a promising material for optoelectronics and photodetection devices because of its ultra-broadband optical absorption and high carrier mobility. However, its integration with optoelectronic systems has been limited by the zero-bandgap and the lack of a gain mechanism. Herein, we demonstrate a novel photodetector based on the graphene nanoribbons (GRNs) with a sizable bandgap. Utilizing trapping charge at the interface between SiO Graphene, a two-dimensional 2D) layered material, plays an important role in many fields including electrodialysis , batteriD layered2 ]2)] and τ is excess hole lifetime (trapped hole lifetime), and τT = l2/(μVS − D) is the transit time of the carrier, while l is the length of the channel and μ is the carrier mobility, whereas VS − D is the source-drain bias. Hence, the gain and source-drain voltage exhibit a positive correlation. Apparently, G is linearly dependent on the source-drain bias. As a result, the maximum photoresponsivity of R = 170 AW−1 and the maximum gain of G = 14,500 were achieved at room temperature at VS − D = 0.5 V, which was a 100-fold improvement over previous graphene-nanostructure-based photodetectors [where etectors , 35, 36.R = 800 AW−1 and the maximum gain of G = 22400 were obtained at VG = - 4 V. This maximum value of photoresponsivity was five orders of magnitude higher than that of pure graphene photodetectors (~ 10 mAW−1) [RC) between the Au electrodes and the GNRs which was inseparably related to the Schottky barrier height at the interface [VG, the Schottky barrier height was reduced due to the lower Fermi level. In contrast, when the VG was increased to a positive value, the Schottky barrier height was enhanced, and the current in the channel was greatly suppressed.Figure 0 mAW−1) . Furthernterface . As the Iph = Pα, α = 0.85). Under lower light power, the contribution of the photogate current was dominant, and the photoconductive effect could be ignored due to a decrease in the number of photogenerated carriers [Finally, we turn to the time-dependence investigation of photocurrent under incident light of power. Figure carriers . Upon hi2 interface. The device exhibited a high photoresponsivity of 800 AW−1 at VG = − 4 V, which was two orders of magnitude higher than those in the previous research. Furthermore, the structure of our device is much simpler compared to the previous graphene-based optoelectronic device with the potential broad applications. The performance of the graphene nanoribbon device can be further improved by h-BN encapsulation, surface plasmons, ferroelectric field, and hybrid structures. The proposed graphene nanoribbons photodetector opens up exciting opportunities for ultrafast and high sensitivity for future graphene-based safety monitoring, photo-communication, and aviation applications.In summary, we have demonstrated a high-performance graphene nanoribbon photodetector modulated in a wide range through the external electrical field at room temperature. Meanwhile, without the external electric field, the performance of the device could be enhanced by the localized field at the Si/SiO


TC up to ∼400 K and saturation magnetization of 0.11 emu g−1 (300 K) can be achieved in graphene by embedding isolated Co atoms with the aid of coordinated N atoms. Extensive structural characterizations show that square-planar Co-N4 moieties were formed in the graphene lattices, where atomically dispersed Co atoms provide local magnetic moments. Detailed electronic structure calculations reveal that the hybridization between the d electrons of Co atoms and delocalized pz electrons of N/C atoms enhances the conduction-electron mediated long-range magnetic coupling. This work provides an effective means to induce room-temperature ferromagnetism in graphene and may open possibilities for developing graphene-based spintronics devices.Graphene is extremely promising for next-generation spintronics applications; however, realizing graphene-based room-temperature magnets remains a great challenge. Here, we demonstrate that robust room-temperature ferromagnetism with  Graphene has shown incredible promise as ideal material for numerous fields; however its use in spintronics has been hampered by the lack of intrinsic magnetism. Here, Hu et al succeed in embedding Cobalt in the graphene lattice, creating robust room-temperature ferromagnetism. It possesses many unique properties such as large specific surface area3, superior mechanical strength4, high intrinsic carrier mobility6, and thermal conductivity7. Great efforts have been devoted to promoting the applications of graphene in a diversity of fields including field-effect transistors8, chemical sensors9, fuel cells10, and so forth. Graphene is also envisaged to be a highly promising material for next-generation spintronic applications, owing to its extraordinary carrier mobility, long spin diffusion length, weak intrinsic spin–orbit coupling, and limited hyperfine interactions12. For practical applications in spintronic devices, the graphene-based material is required to be ferromagnetic above room temperature. Unfortunately, pristine graphene is intrinsically diamagnetic and lacks local magnetic moments14. Therefore, development of effective strategies to induce and manipulate the ferromagnetism of graphene has become a key issue to extend its applications in spintronics.Graphene is a two-dimensional (2D) layered material composed of a one-atom-thick hexagonal network of 15, sp3 functionlization16, chemical doping17, surface adsorption18, and zigzag edges19. However, the magnetic moments induced by point defects or edges are relatively weak and unstable; and consequently the ferromagnetic ordering often collapses at rather low temperatures21. For instance, Grigorieva and colleagues21 produced high-density vacancy defects on graphene with ion beam radiation and realized a macroscopic magnetic moment of 0.001 14 found that S-doped graphene could only maintain ferromagnetic ordering at temperatures below 62 K, and Liu et al.22 demonstrated that only pure diamagnetism can be observed in N-doped graphene at 300 K. To improve the stability of ferromagnetic coupling, the common wisdom is to increase the content of defects so as to shorten the distance between weak local magnetic moments. However, an excessive content of defects would break the π-bonding networks and degrade the electric properties of graphene. In analogous 2D system of graphene like transition metal (TM) dichalcogenides, doping of magnetic TM  has been experimentally demonstrated to be an effective method for inducing stable room-temperature ferromagnetic ordering24. However, direct doping of TM atoms in graphene is impractical because graphene is not an ionic crystal like TM dichalcogenides and its honeycomb networks are maintained by strong C–C covalent bonds. This makes the energy barrier of embedding a single TM atom into pristine graphene lattices very steep. Indeed, previous attempts to produce TM-graphene compositions merely yielded TM atoms or clusters adsorbed on the surface of graphene26, where the magnetism is not intrinsic but mainly contributed by the metal clusters. Meanwhile the adsorbed TM atoms on graphene could be easily dissociated or coagulated at elevated temperatures. For example, graphene with Co atoms attached on the surface can maintain ferromagnetic ordering only below 10 K26. Hence, stable anchoring of magnetic TM atoms on graphene is important for the manufacture of graphene-based room-temperature ferromagnetic materials. Inspired by the metal-nitrogen (M-N) moiety strategies developed recently for atomically dispersed TM catalysts28, we speculate that doping TM with the aid of coordinated N might be a possible way to embed TM atoms into graphene lattices. As the Pauling electronegativity of N (3.0) is significantly higher than that of C (2.5), the TM atoms can be strongly bound by N atoms rather than by C atoms as observed in many TM-N-C systems29. Therefore, the TM-Nx moiety can be doped in graphene and act as the ferromagnetic centers. Previous density functional theory (DFT) simulations confirmed the structural stability of the heavy N-doped graphene at up to 1000 K30, further supporting the feasibility of TM-Nx moiety on building the graphene-based room-temperature ferromagnetic materials.So far, various strategies have been attempted to realize ferromagnetic ordering in graphene, including utilization of atomic vacancies4 moiety doped graphene, which was synthesized via an impregnation-pyrolysis method, and exhibits unprecedented ferromagnetic orderings with Curie temperature (TC) up to ∼400 K. A high saturation magnetization of 0.11 emu g−1 at 300 K (0.73 emu g−1 at 5 K) is obtained at the Co content of 0.4 at.%. Detailed X-ray absorption spectroscopy characterizations and multiple-scattering simulations confirm the embedding of Co atoms in the graphene lattices, via the formation of isolated Co-N4 units. Electronic structure calculations reveal that stable room-temperature ferromagnetic coupling is mediated by carriers and arises from the d-p orbital hybridization between the embedded Co and C/N atoms. Besides the ferromagnetic ordering, the Co-N4 doped graphene possesses unique electronic structure where only one spin channel crosses the Fermi level, which is beneficial for the generation of spin-polarized current in spintronics31. This deliberately designed strategy opens up an avenue for the development of graphene-based spintronic devices.Herein, motivated by the above consideration, we propose a coordination atom assisted strategy by embedding magnetic TM into graphene lattices under the assistance of coordinated N atoms, where stable room-temperature ferromagnetism can be achieved in graphene. This strategy is exemplified by the single-metal-atomic Co-N33. No Co-related second phases were detected. Furthermore, Co K-edge X-ray absorption near edge structure (XANES) spectra  spectra  was prepared through a two-step synthesis strategy consisting of the impregnation and following pyrolysis procedures. For comparison, samples with other Co contents were prepared, denoted as 0.2Co-N/G, 0.5Co-N/G, and 3Co-N/G with 0.2×, 0.5×, and 3× Co precursors during preparation than that of 1Co-N/G sample . According to the X-ray photoelectron spectroscopy (XPS) analysis , pyrrolic (400.0 eV), graphitic (401.2 eV), and oxidized N groups (402.3 eV), there is also a signal from Co-N bonds with the XPS peak at ~399.2 eV34. The formation of Co-N bonds was also confirmed by N K-edge XANES spectra  by comparing the formation energies . The calculation results and structural schematics are given in Fig. x4−Cx/G  moiety was ensured to be the converged configuration after structural optimization  moiety is much more energetic favorable than other CoNx4−Cx/G  compounds. In addition, the simulated Co K-edge XANES spectrum based on the structure model of a square-planar Co-N4/G generated by DFT calculations is presented in Fig. 4 moieties is the most possible structure for Co atoms in the 1Co-N/G nanosheets.In order to explore the bonding configuration of Co atoms in 1Co-N/G nanosheets, we performed XPS, and N and C K-edge XANES measurements. XPS survey spectrum   for 1Co-N/G nanosheets are given in Fig. TC above room temperature (300 K). The magnetization difference (ΔM) between FC and ZFC curves (MFC−MZFC) in the inset shows a decreasing trend but still perfectly positive in the whole temperature range (up to ~400 K). Hence, the possibility of spin glass effects and superparamagnetism can be excluded in the 1Co-N/G nanosheets41. The corresponding M−T curves for 0.2Co-N/G and 0.5Co-N/G under an applied field of 500 Oe are also depicted in Fig. M–H curves at 300 K are given in Supplementary Fig. 17. Seen from Fig. 45. Furthermore, in order to visually show the change of magnetism with Co contents in doped graphene nanosheets, we extracted magnetic signals from the M−H curves for all the samples and plotted them against the Co contents in Supplementary Fig. M−H curves for 1Co-N/G nanosheets exhibit similar ferromagnetism with higher saturation magnetization of ~0.73 emu g−1  with that of Co metal (1.7 μB Co−1), we can estimate that ~24% of the total doped Co atoms should be in the form of clusters. Such a content of Co clusters should be easily detected by TEM and XANES46, which is however contrary to the experimental observations  Fig.  at 300 Kons Fig. . Meanwhi4-graphene system, we performed spin-polarize DFT calculations . The total densities of states (TDOS), projected densities of states (PDOS), energy band structure and spatial distribution of spin density are presented in Fig. E = 0 eV in the DOS)48. In contrast, the TDOS and PDOS of CoN4-graphene system  and antiferromagnetic (AFM) states of different bi-Co-N4 configurations 53. Due to the intense hybridization between Co d and delocalized N/C pz orbits, the long-range magnetic coupling between Co atoms can be attributed to an indirect conduction-electron mediated Ruderman–Kittel–Kasuya–Yosida (RKKY)-like interaction55. Meanwhile, we have also carefully considered other magnetic coupling frameworks including successive spin polarization-based ferromagnetic coupling (SSP-FMC)57 and bound magnetic polaron (BMP)58 that have successfully interpreted the origins of ferromagnetism in many TM-doped system. However, due to the metallic nature, enhanced DOS at Fermi level and spin-polarized π electrons for our CoN4-graphene system, the conduction-electron mediated ferromagnetism is a more reasonable and straightforward explanation for the main origin of ferromagnetism in CoN4-graphene nanosheets. Since the hybridization between the Co-3d, N-2p, and C-2p electronic bands is essential for the development of the ferromagnetic coupling in CoN4-graphene nanosheets, the strong room-temperature ferromagnetism originates from the chemical bonding that embeds Co atoms into the graphene lattice. Hence the DFT simulations lead us to conclude that the Co-N4 moieties are responsible for the observed room-temperature ferromagnetism in the as-synthesized CoN4-graphene nanosheets.To obtain an in-depth understanding on the origin of the room-temperature ferromagnetic coupling in CoNtem Fig.  exhibitsity Fig.  also indp electron, making them an n-type dopant in graphene, which can effectively improve the electric conductivity of graphene and enhance the DOS around the Fermi level to meet the Stoner criterion. (3) In the context of the bipartite nature of graphene, the N atoms induce a bipartite arrangement of magnetic moments of the sublattices where they are. Moreover, it is worth noting that the CoN4-graphene nanosheets have greatly improved performance of spin-polarized current because only one spin channel crosses the Fermi level31.Finally, we summarize the important role of co-doped N atoms in the establishment of graphene magnetism: (1) N atoms serve as a stable anchoring site for Co due to the higher Pauling electronegativity than that of C atoms, which avoids the loss of ferromagnetic Co atoms from graphene lattice. (2) Compared with C atoms, N possess one additional x moieties. A simple and controllable impregnation-pyrolysis process was employed to synthesize the Co-N-graphene system. Comprehensive structural characterizations confirm the existence of square-planar Co-N4 moieties in the graphene lattices at the content of 0.4 at.% (for 1Co-N/G nanosheets), which displays unprecedented stable ferromagnetic ordering with TC up to ∼400 K and saturation magnetization of 0.11 emu g−1 (300 K). No Co-related second phase that may induce extrinsic ferromagnetism is observed. On the one hand, Co-N4 moieties avoid the loss of Co atoms from the graphene lattices, thus providing a strong local magnetic moment; on the other hand, DFT calculation reveals that the long-range ferromagnetic coupling originates from the hybridization between the Co-3d and C/N-2p orbitals through a carrier-mediated magnetic interaction. The stable room-temperature graphene magnets developed in this work will pave the way towards practical graphene-based spintronic applications.In summary, we have explored the possibility of achieving stable room-temperature ferromagnetic ordering in graphene by embedding single magnetic TM atoms in the lattice via the strong chemical bonds in the TM-N59. Then, 50 μl of CoCl2·6H2O solution (3 mg ml–1) and 15 μl of H2O2 solution (30%) were added dropwise into 10 ml of GO suspension (2 mg ml–1) and the mixture was sonicated for 1 h. The obtained suspension was transferred into a stainless steel autoclave and maintained at 180 °C for 6 h, forming a hydrogel. After freeze drying, the Co2+-containing gel was heated in the center of a quartz tube furnace at 900 °C for 1 h under gas flows of 100 sccm Ar and 50 sccm NH3 to obtain 1Co-N/G. For comparison, the N/G nanosheets were prepared with the same procedure with no addition of CoCl2·6H2O.1Co-N/G nanosheets were synthesized through a two-step impregnation-pyrolysis procedure. In the typical procedure, graphene oxide (GO) was firstly prepared by oxidation of powdered flake graphite following a modified Hummers’ method0.2Co-N/G, 0.5Co-N/G, and 3Co-N/G nanosheets were synthesized with a similar procedure to 1Co-N/G except for 0.2×, 0.5×, and 3× Co precursors during preparation, respectively.60 and some calculation results were visualized with VMD software61. The electron exchange-correlation was processed within the framework of generalized gradient approximation (GGA) in the Perdew–Burke–Ernzerhof (PBE) parametrization. The electron-ion interaction was described using the projected augmented wave (PAW) method. The kinetic energy cutoffs of the plane wave and electron density were 80 and 500 Ry. The long-range van der Waals interaction was considered with the DFT-D3 scheme62. A 6 × 6 × 1 k-grid was used for sampling in the Brillouin zone. Energy convergence of ~1.0 × 10−3 meV/atom was ensured during the self-consistent field calculations. And the convergence criteria for the atomic forces was 0.05 eV/Å. Various typical planar and nonplanar configurations have been fully considered during the structure optimizations, as the examples shown in Supplementary Figs. The spin-polarized DFT calculations were performed with Quantum Espresso software package4. A vacuum layer of 15 Å was placed along the z direction to prevent the interlayer interaction.The Co embedded nitrogen-doped graphene nanosheets were modeled based on an 8 × 8 × 1 hexagonal supercell of graphene. Six adjacent carbon atoms were replaced with Co-NxCx4- moieties in graphene were calculated according to the following equation,xCx4- moiety; The formation energies of Co-NKα line (λ = 1.54178 Å) and an ESCALAB MKII equipped with Mg Kα source (һν = 1253.6 eV), respectively. The N K-edge, C K-edge, and Mo L3-edge XANES were obtained at the BL12B beamline of National Synchrotron Radiation Laboratory  under a total electron yield (TEY) mode with vacuum better than 5 × 10−7 Pa. The Co K-edge EXAFS spectra were obtained on the 1W1B beamline of Beijing Synchrotron Radiation Facility  under fluorescence mode. The temperature- and magnetic field-dependent magnetization curves were measured on a superconducting quantum interference device (SQUID) magnetometer.TEM and EDX spectroscopy results were taken on a field emission transmission electron microscope (JEM-2100F) with 200 KV accelerating voltage. The HAADF-STEM images were captured on a JEM-ARM200F instrument at 200 KV. The XRD and XPS patterns were collected on a Philips X’Pert Pro Super diffractometer with Cu Supplementary Information


In this paper, we propose a tunable coordinated multi-band absorber that combines graphene with metal–dielectric–metal structures for the realization of multiple toward perfect absorption. The parametric inversion method is used to extract the equivalent impedance and explain the phenomena of multiple-peak absorption. With the change of the Fermi level, equivalent impedances were extracted, and the peculiarities of the individual multiple absorption peaks to change were determined. By changing the structure parameters of gold rings, we obtain either multiple narrow-band absorption peaks or a broadband absorption peak, with the bandwidth of 0.8 μm where the absorptance is near 100%. Therefore, our results provide new insights into the development of tunable multi-band absorbers and broadband absorbers that can be applied to terahertz imaging in high-performance coordinate sensors and other promising optoelectronic devices. Metamaterials are artificially structured materials that can have negative permittivity or/and permeability, which can be achieved by periodic metal–dielectric arrays ,2,3,4. WGraphene is a relatively new two-dimensional sheet composed of carbon atoms of atomic thickness with unique quantum, thermodynamic , and opt2 is situated, while the top layer represents an array of different sized gold rings (see – 1). The proposed structure is simulated using the commercial finite element method (FEM) solver COMSOL Multiphysics with the purpose of obtaining a number of closely located near-perfect absorption peaks. For reliability purposes, an additional numerical simulation utilizing the method of inverse parameter extraction is carried out to extract effective parameters and explain the phenomenon of multiple absorption peaks. By adjusting the Fermi level of graphene, at least three absorption peaks can be tunable between 8 μm (37.5 THz) and 14 μm (21.4 THz) while the structural changes in parameters of metal rings lead to a single wide absorption peak with an absorptance higher than 80%. The position of the single peak is also Fermi-level adjustable.While the proposed novel, multi-band, and tunable selective absorber utilizes graphene as its key component, its base structure uses gold as a ground plane that reflects electromagnetic waves. Between the ground plane and graphene, a substrate layer of SiOXZ plane by red arrows. The yellow layer and rings represent gold with thicknesses of d1 and d3, respectively. The blue layer represents SiO2 with a thickness of d2. A single layer of graphene between the upper gold rings and the SiO2 substrate is pictured as a honeycomb structure. Within the operating wavelengths, the dispersion of dielectric constant of SiO2 [εSiO2 = 3.9, and the permittivity of gold can be expressed as ε = 1 − (ω2 + iωγ)) using the Drude model [pω = 1.36 × 1016 rad/s and collision frequency γ = 3.33 × 1013 rad/s. The graphene is simulated in COMSOL by setting the surface current density.The three-dimensional structure of the graphene-based selective absorber is proposed in  of SiO2  is neglide model  with theσinter and σintra are expressed as the interband and intraband transition contributions, respectively. Bk, ℏ, and e represent the Boltzmann constant, reduced Plank constant, and electron charge, respectively. ω is the angular frequency of the incident radiation, fE is the graphene Fermi energy level, τ is the electron-phonon relaxation time, and T is the ambient temperature. As we only consider highly doped graphene, we should take into account that fE ≫ BTk and fE ≫ ℏω. Therefore, the Kubo equation can be simplified to a Drude-like equation [τ = fμE/ – rings R, resulting in the green dotted line in R1, resulting in the red dotted line in R2, resulting in the blue dotted line in The black curve in μ1 and ε1, which is surrounded by an outer layer with material parameters μ0 and ε0, as illustrated in Z axis—that is, perpendicular to the surface of the absorber. For simplicity, layers 1 and 3 in An inversion algorithm is applied to extract the normalized impedance of the multiple-peak absorber for explaining the physical mechanism of the phenomenon. The absorber is considered as a uniform layer with material parameters Ex0 = 1. Then, the electromagnetic field strength in medium 1 can be written as:When TM propagates in medium 1, without loss of generality, we set the amplitude Then, the TM wave moves through the interface of medium 1 and medium 2, and the electromagnetic field strength in medium 2 can be written as:k1 = k3 = k0 is the wave number in vacuum, k2 = nk0 is the wave number in the relative medium, Γ1 and Γ2 are the reflection coefficients between medium 1 and medium 2, and medium 2 and medium 3, respectively. τ1 and τ2 are the transmission coefficients between medium 1 and medium 2, and medium 2 and medium 3, respectively.Finally, the TM wave enters the medium 3, and the electromagnetic field strength in medium 3 can be written as:Z = 0. According to the boundary conditions encountered by the electromagnetic wave at the discontinuous interface when Z = 0, we can obtain:Z = d, that is, at the interface between medium 2 and medium 3:We let the interface of medium 1 and medium 2 be the origin—that is, Γ1 = (Z − 1)/(Z + 1). Since we use gold as the reflective layer of the entire absorber, there is no transmission spectrum, so S21 = 0. According to the selection of the sign [According to the above-mentioned boundary conditions that the electromagnetic wave meets on the discontinuous interface, the scattering parameters can be obtained:the sign ,44, we cIn order to minimize reflection, the normalized impedance of the absorber should be close enough to the impedance of free space—that is, the closer the real part of the normalized impedance is to unity and the closer the imaginary part is to vanishing, giving rise to the greater the absorptance of the structure. As is shown in The relationship between the normalized impedance and the energy of Fermi level is investigated in R increasing independently from 1.04 μm to 1.08 μm, R1 increasing independently from 1.11 μm to 1.15 μm, and R2 increasing independently from 1.27 μm to 1.30 μm, the resonance wavelengths of the corresponding absorption peaks are red-shifted by 1.2 μm, 0.4 μm, and 0.4 μm respectively  is the structural constant of graphene, and rλ is the polarization resonance wavelength of graphene. From the above formula, the resonance frequency of the plasmon surface of the graphene can be obtained: rf ∝ (α0fE/(2π2ℏcL))1/2. That is, as the Fermi level increases, the resonant frequency also increases. Since the structure proposed by us lays a layer of graphene, we can adjust the Fermi level of graphene by applying a voltage across the graphene, thereby changing the resonance frequency of the absorption peak. The Fermi level energy g Ef see . Known fR = 1.06 μm, R1 = 1.24 μm, R2 = 1.26 μm, and w = 0.44 μm. In this case, the absorptance greater than 80% occurs between wavelengths 10.51 and 11.31 μm  absorption peaks with an absorptance greater than 80% is achieved. We assume that our findings can potentially be useful in those practical applications relating to adjustable optical filters, splitters, modulators.


Beyond traditional approaches in understanding amyotrophic lateral sclerosis (ALS), multiple recent studies in RNA-binding proteins (RBPs)—including transactive response DNA-binding protein (TDP-43) and fused in sarcoma (FUS)—have instigated an interest in their function and prion-like properties. Given their prominence as hallmarks of a highly heterogeneous disease, this prompts a re-examination of the specific functional interrelationships between these proteins, especially as pathological SOD1—a non-RBP commonly associated with familial ALS (fALS)—exhibits similar properties to these RBPs including potential RNA-regulatory capabilities. Moreover, the cytoplasmic mislocalization, aggregation, and co-aggregation of TDP-43, FUS, and SOD1 can be identified as proteinopathies akin to other neurodegenerative diseases (NDs), eliciting strong ties to disrupted RNA splicing, transport, and stability. In recent years, microRNAs (miRNAs) have also been increasingly implicated in the disease, and are of greater significance as they are the master regulators of RNA metabolism in disease pathology. However, little is known about the role of these proteins and how they are regulated by miRNA, which would provide mechanistic insights into ALS pathogenesis. This review seeks to discuss current developments across TDP-43, FUS, and SOD1 to build a detailed snapshot of the network pathophysiology underlying ALS while aiming to highlight possible novel therapeutic targets to guide future research. Amyotrophic lateral sclerosis (ALS) is a fatal, incurable, and highly heterogeneous neurodegenerative disease that deteriorates both upper and lower motor neurons (MNs), leading to extensive muscle weakness, atrophy, and paralysis . More thOver the years, the number of neurodegenerative diseases that have been associated with pathological protein aggregates has increased. In ALS, more than 50 genes  have beeInterestingly, both SOD1 and several RNA-binding proteins including TDP-43 and FUS, have also been shown to contain aggregation-promoting prion-like domains that are able to rapidly associate. This property cements these RNA-modifiers as plausible instigators of proteinopathy. Recent studies have also highlighted the interference of pathological TDP-43 and FUS aggregates with normal stress granule formation upon mis-localization to neuronal cytoplasm ,16, whicSeveral miRNAs have also been put forth as potential diagnostic biomarkers for ALS in both the neuron and microglial cells including miR-155, Let-7a, and miR-125b ,18. ResuSOD1 account for 20% of familial ALS and 5% of sporadic disease .,44.43,44Examining this heavily conserved biogenesis ultimately illustrates the extreme degree of regulation of an intricate, essential, multi-step process that emphasizes both the scale of the cumulative effects of miRNA control and the potential for missteps at multiple points along production.TDP-43 is a nuclear protein that shuttles between nucleus and cytoplasm and like FUS, is structurally and functionally similar to the heterogeneous nuclear ribonucleoproteins (hnRNPs), which are involved in RNA processing. TDP-43′s association with over 6000 mRNA targets—almost 30% of the entire human transcriptome—suggests a significant capacity to cause regulatory chaos when this gene becomes dysfunctional . PatholoThis raises the question as to whether mis-localization and subsequent nuclear depletion leads to a reduced supply of the protein for its nuclear role, or if pathology results from toxic cytoplasmic aggregates. In the nucleus, the role of TDP-43 has been implicated in DNA repair , mRNA spTDP-43, therefore plays a clear and essential role as a nuclear protein; pathologically, TDP-43 has a less understood role, as hypothesized to be translocated to the cytoplasm ,59,60,61Although Kawahara and Mieda-Sato revealed the association that TDP-43 and Drosha share, they noted that the interaction was only permitted by certain RNA species, or a direct protein–protein interaction, which occurs through the C-terminal tail (of amino acids 316–401). Treatment with RNase VI, A, or both, elicited a dose-dependent response, highlighting RNA to be crucial in the typical functioning of TDP-43 and Drosha. However, even upon excess RNase treatment and a proven dispensability of the RNA-binding domain of TDP-43 in vitro, some association remained. The study thus concluded that the interaction between TDP-43 and Drosha exists as both RNA-dependent and -independent, although still indispensable for pre-miRNA processing. This raises the question of the role that Dicer complexes without TRBP play: are they simply “floaters” in the bigger picture of miRNA biogenesis, or do they have an unspecified, yet critical role? One possibility may include the existence of a feedback system, where these non-TRBP Dicer complexes assist, should there be a shortage of certain miRNAs. Overall, this study demonstrates the unique function of TDP-43 not only in the nucleus, but also in the cytoplasm, which may have immense relevance in the compartmental shuttling of TDP-43.As evidenced through multiple studies conducted on induced pluripotent stem cell (iPSC)-derived neurons, TDP-43 affects several miRNA expression levels upon downregulation, and it is thus unsurprising that the regulatory effects of TDP-43 extend beyond biogenesis and toward direct associations with produced miRNA such as that of miR-let-7b ,64,65. TConsidering the lack of categorization of miRNAs, and the numerous, yet specific, miRNAs involved in TDP-43 pathology and beyond, perhaps one should consider, at least in the scope of ALS, the grouping of miRNA into “families” of those associated with TDP-43 and at what stage of miRNA biogenesis this effect occurs. More research is encouraged to make such specific miRNA connections more valuable, as they present a promising target group for novel therapies.Less prevalent amongst cases is the presence and discussion of the similar RBP, fused in sarcoma/translocated in liposarcoma (FUS/TLS). Following the discovery of TDP-43 and its mutants in ALS, FUS was identified to be associated in both familial and sporadic forms of ALS, which held considerable significance as both proteins share intense functional similarities as RNA binding proteins and display mis-localization and aggregation as main features ,68,69,70Although TDP-43 inclusions are absent in ALS patients with FUS mutations ,77,78 anFurthermore, FUS has been shown to promote optimal miRNA-mediated gene silencing via direct binding to certain miRNA and mRNA targets including mature miR-200c, with mutant FUS forms impairing AGO2 protein function in the miRNA-induced silencing complex (miRISC) . As a coHowever, beyond biogenesis, loss-of-function effects related to reduced miRNAs including miR-375 and its target genes, have also been elicited in vitro in human MNs upon induction of mutant FUS . ReducedWT) is not commonly associated with aspects of neurodegeneration including in ALS, although upon post-translational modification such as iper-oxidation, even if noninheritable, studies suggest the possibility of its additional toxic role in sALS [WT in Parkinson’s disease (PD) has sparked interest in its toxic ‘gain-in-function’, as aggregates of SOD1WT were discovered in postmortem idiopathic PD neural tissue [WT can then become dysfunctional, misfolding, and aggregating [WT in other NDs and controls are therefore required to confirm this hypothesis, and to identify whether toxicity from SODWT build-up is merely a common result of neurodegenerative-related stress.Unlike TDP-43 and FUS, superoxide dismutase (SOD1) is not classified as an RBP, despite sharing key roles ; however in sALS ,88, whic in sALS ,89,90,91 in sALS  into thel tissue ,94. It il tissue ,96, espel tissue ,98; fromregating . Notablyregating . To enliLocated in exon sequences, SOD1 mutations were initially thought to be purely due to a loss of physiological function, which would result in an increase in oxidative stress and eventual excitotoxicity. However, recent investigations have instead illuminated a gain of new toxic properties through its role in protein aggregation, as a result from its mutant-induced co-aggregation with other proteins  such as Moreover, in the literature, it is generally accepted that SOD1 and TDP-43 inclusions are exclusive of one another, particularly when examining mutant SOD1 patients, as is more common with fALS . HoweverInterestingly, as miRNAs do regulate various genes involved in oxidative stress response, and as the converse also proves true, it is important to consider the strength of interrelationship between miRNA dysregulation and ALS pathogenesis, especially as oxidative stress is so strongly related to both SOD1 and other parts of pathogenesis, potentially leading to other processes of degeneration, despite often being examined in separation to RNA dysregulation . The sigIn sALS individuals, SOD1 is not as commonly discussed as it is in fALS, where it mediates the disease in around 20% of affected individuals .In 2009, a paper by Williams et al. highlighted how miRNA-206 delays ALS progression in G93A-SOD1 mice, and was significant in confirming the overall importance of miRNA involvement in ALS pathogenesis . MediateG93A mice, compared to the total miRNA levels at any different stage of disease progression [G93A mice may be possibly reflected in pathological increase in miRNA biogenesis machinery.In a similar vein, Russell et al. (2018) investigated the regulation of select members of miRNA biogenesis pathways in pre-symptomatic, symptomatic, and end-stage SOD1gression . They usG93A) mice, a result similar to those previously discussed in the 2015 Butovsky study [G93A mice show the potential to be delayed [Furthermore, the recent work from Weiner’s laboratory implicated the role of miR-155, a glial-enriched miRNA, in its upregulation in the spinal cords of end-stage ALS model  has been particularly shown to propagate intercellularly via exosome-dependent and independent mechanisms , as is sConsidering this, one should always remain wary of the likelihood of ALS being attributed to several etiologies, and thus a value exists in understanding this final “common ground” before the more downstream pathogenic effects that are known to characterize the disease, particularly as this appears to be the tight interrelationship between miRNAs and RNA-modifying proteins, especially RBPs TDP-43 and FUS. Overall, these common properties in TDP-43, FUS, and other RBPs may also warrant future investigations on the existence of any direct relationship with miRNA dysregulation, as this will also clarify the extent of the miRNA-RBP two-way relationship, while also providing newer, plausible targets for novel therapies.As is the case with ALS, despite these heavy self-regulatory effects, RBPs are not immune to pathological changes. Both FUS and TDP-43 have been a focus for the past decade, with the latter strongly aligning with the pathology  in >95% In recent years, these prion-like domains in RBPs have been a source of speculation as they prove to be essential for “functional aggregation” and building SNPs, regulators of gene expression via nuclear retention of RNA ,150, lacMoreover, the relationship that RBPs have with SNPs must then also be appreciated, as both losses and gains in RBP function may prove to be protective and potentially essential in their association with de novo SNP development. A recent study  into endClinically indistinguishable ALS can also be caused by genetic mutations of SOD1, TDP43, or FUS, and can occur in the absence of known mutation as sporadic disease . In theiThis connection between these RBPs and SOD1 is highly plausible, especially as various TDP-43 and FUS forms are known to associate with mitochondrial impairment ,158, whiThese implications argue for the equal propensity for these pathological proteins to seed, intercellularly transmitting mutant or misfolded wild-type SOD1 through the release of naked aggregates by the dying cells, which, in turn, are taken up by macro-pinocyotosis and trigger seeded aggregation . AlternaHowever, another investigation showed the converse of this situation, in which administration of mutant SOD1 induced TDP-43 aggregation in the target cell population . SimilarThus far, a complex perspective has emerged between the RBPs and SOD1 in ALS. In defining the intricacies of the functional dependencies and influences between these RNA-modifying proteins themselves including their prion-like capabilities, we can finally attempt to clarify the intrinsic two-way relationship between these proteins and miRNA. From our previous discussions on miRNA biogenesis and TDP-43, FUS, and SOD1 see , it can In a recent study by Brennan et al. , clusterAs direct miRNA-to-protein interactions remain uncertain, it is also noteworthy that alterations in protein levels are instead attributed to a mix between pro-aggregation signaling and cellular mis-localization. Although most analyzed TDP-43 ALS-linked mutations accelerate aggregation in vitro , most FURegardless, since protein dyshomeostasis unifies several neurodegenerative diseases, and given the functional resemblance between RBPs, it is plausible to hypothesize that targeting ribonucleoprotein particle (RNP) assembly in human neurodegenerative diseases could be an effective strategy in the treatment of NDs. A better understanding of this process could shed much needed light on the mechanisms of regulation of this dynamic molecular machinery that governs specific cellular and physiological functions to maintain a non-disease state, and thereby elicit plausible future therapies, especially the potential for a common therapeutic strategy.Thus far, we have synthesized the importance of the three key misfolded proteins linked to ALS —TDP-43, SOD1, and FUS—whilst defining their relationships, interdependencies , and curExtensive downregulation of miRNAs has been discussed to be a common molecular determinant in different forms of ALS in humans, with pathogenic RBP mutations notably interfering with DICER and therefore, miRNA biogenesis, which then associates with abnormalities within the stress response system, as observed in ALS and other NDs. Whether miRNA dysregulation is upstream or downstream in pathogenesis is yet to be confirmed, however, the common ground of miRNA and RNA regulation, and RBP dysfunction, is ultimately undeniable across both sALS and fALS patients. In this context, Hornstein’s group  recentlyAs a diagnostic tool, miRNAs were first investigated in 2012 by de Felice et al., where the team highlighted the potential for peripheral leukocyte profiling after successful comparison of miR-338-3p to healthy controls . The samNovel treatment regimens may thus exploit the intricate two-way interaction between miRNA biogenesis and RBPs, especially as we can infer that miRNA dysregulation is likely to be the most critical upstream link, proving essential to the regulation of both (1) the RNA-binding proteins themselves (indirectly affecting their autoregulation), and (2) other gene expressions. Such genes include both STMN2  and UBQLelegans animal model. SOD1, however, presents a more complex situation, being recently implicated in a protective role upon nuclear re-localization [Encouraging are the recent developments on the therapeutic front at Prosetta Biosciences, San Francisco, USA, where they have established cellular models recreating the mis-localization of TDP-43 from nucleus to cytoplasm and the stress-induced aggregation of the cytoplasmic mis-localized TDP-43. The team identified several Hitfinder compounds that can prevent aggregate formation in stress granules, while also discovering others that instead confer dramatic re-localization of TDP-43 back to the nucleus. Moreover, these results with cellular models were complemented by the ability of both classes of compounds to relieve the motor paralysis displayed by a TDP-43-based transgenic C. lization . As a polization ,179.Further developments have also arisen, with a 2019 mouse study introducing viral-mediated antibody delivery to target the RRM1 degradation domain of TDP-43 inclusions . ShowingG37R transgenic mice, which resulted in reduced accumulation of misfolded SOD1 in the spinal cord, coupled with the increased survival by an average of 40 days. Thus, there appears to be a viable immune-therapy approach against misfolded SOD1 in fALS and sALS, which can be further expanded to other misfolded proteins.Finally, regarding our discussions in understanding the close interrelationship between SOD1 and RBPs TDP-43 and FUS, this re-examined perspective of the non-RBP may help researchers better understand the seemingly causative heterogeneity exhibited in the common mutant SOD1 fALS patients and those non-mutant SOD1 ALS patients. This may therefore act as a consolation in devising treatment approaches for such a heterogeneous disease, particularly as treatment response may therefore effectively affect beyond that single target , even if such a target proves to be downstream to central pathology, since ALS appears to be deeply integrated with multiple points of cyclical failure. In this respect, Liu et al.  have shoFinally, it is also worthy to consider the relationship miRNA holds between the potentially self-propagating pathological RBPs (as discussed in Here we have reviewed common proteomic themes that are not only mechanistically important in ALS, but may also shed light on proteinopathies and their molecular basis that are involved across several neurodegenerative diseases. To date, no single entity or event results in slowly progressive neurodegenerative diseases, and it is believed that several processes encompassing environmental, epigenetic, and genetic events may define a disease phenotype. Given this, the next generation of treatments should focus on combinatorial therapies that are selective for several of these decisive events that define a disease phenotype. As discussed, investigating the role of microRNAs and RNA-binding proteins provides only a snapshot of the causative mechanisms in ALS pathogenesis. However, their significance in disease progression is greatly recognized, and its unified discussion was necessitated. Since in several NDs, specific nuclear or cytoplasmic protein accumulation forms the causative neuropathological picture, it will be important to identify and ideally classify microRNAs regulating the translation of these targets, while studying the quantitative effects on the proteome of these therapeutic miRNAs. Although still hampered by the heterogeneity of ALS, this insight into these crucial players will hopefully guide future research attempts into more effective diagnostic advancements and potential avenues for therapy.


Two-dimensional (2D) materials and their van der Waals heterojunctions offer the opportunity to combine layers with different properties as the building blocks to engineer new functional materials for high-performance devices, sensors, and water-splitting photocatalysts. A tremendous amount of work has been done thus far to isolate or synthesize new 2D materials as well as to form new heterostructures and investigate their chemical and physical properties. This article collection covers state-of-the-art experimental, numerical, and theoretical research on 2D materials and on their van der Waals heterojunctions for applications in electronics, optoelectronics, and energy generation. The advent of graphene ,2,3, andDifferent types of 2D materials can form heterojunctions with each other or with bulk materials, without the need for close lattice matching . In thesTremendous amount of work has been done thus far on the physical and chemical properties as well as on the synthesis and the characterization of 2D materials such as graphene ,14, tranNanomaterials (MDPI), covers state-of-the-art experimental, simulation, and theoretical research on 2D materials and on their van der Waals heterojunctions for applications in electronics, optoelectronics, energy generation, and photocatalysis.This article collection, a reprint of the Special Issue “2D Materials and Van der Waals Heterostructures: Physics and Applications” published by From a material standpoint, the articles of this collection can be organized in three main categories: Graphene and graphene oxide, MXenes and transition metal (di)chalcogenides, and graphene-like materials.2 heterostructures [First principle calculations are applied to study the electronic and magnetic properties of Stone–Wales defected graphene  and the ructures , while eructures  and to rructures . Stone–Wales, formed by the rotation of a C-C bond in a hexagon ring, are the most common topological defects in graphene. They have been widely studied because they can lead to the opening of a band gap that is highly desirable for the application of graphene in electronic devices . Stone-W2) are changed after they are combined in an heterostructure. MoS2 [2 system are improved compared to those of the two separate single-layers. The band gap and the dielectric constants become larger for the graphene/MoS2 heterostructure, with redshift for the absorption coefficient, the refractive index, and the reflectance, and blueshift for the energy loss spectrum. First principle calculations are also applied by Qiu et al.  to demonre. MoS2 ,33 is thThe heterojunction formed by graphene with traditional 3D materials, which has been a promising research topic ,35,36,372V2O7 nanoparticles deposited on TiO2 nanorods followed by the addition of r-GO flakes. They show that electrophoretic deposition of p-type r-GO flakes on β-Cu2V2O7/TiO2 nanorods remarkably improves the durability, charge transfer resistance, and photocurrent density. Graphene oxide is the material produced by oxidation of graphite that includes oxygen functional groups. It has interesting properties that are different than those of graphene. The reduction of the oxygen content through chemical, thermal, and other methods leads to the so-called reduced graphene oxide (r-GO), a cheaper and lower quality form of graphene, yet with important applications ,40. r-GO2C, Ti4N3, Ti3CN, Mo2N, Mo2TiC2, Mo2Ti2C3, Cr2TiC2, etc. MXenes such as Nb2CTx, Ti2CTx, or Ti3C2Tx, with the surface terminated by Tx functional groups , combine a high metallic conductivity with the hydrophilic nature of their hydroxyl or oxygen terminated surfaces, behaving as a sort of “conductive clays”. MXenes have been applied to energy storage, water purification, chemical catalysts, electrochemical sensors, field effect transistor sensors, gas sensors, etc. [MXenes are a new, large family of layered materials consisting of transition metal carbides, nitrides, and carbonitrides. Examples of mono and double transition metal MXenes are Tirs, etc. . Transition metal (mono)chalcogenides (TMC)  are MS, 2, where M represents a transition metal and X denotes the chalcogen. Molybdenum disulfide (MoS2) and diselenide (MoSe2) [2) and diselenide (WSe2) [2) [Transition metal dichalcogenides (TMDs) are the most studied 2D materials after graphene ,43. They (MoSe2) ,46,47,48e (WSe2) ,50 are tSe2) [2) ,51,52 isSe2) [2) , flexiblSe2) [2) , 3d imagSe2) [2) , and tacSe2) [2) , as wellSe2) [2)  or photoSe2) [2) , have be3C2Tx MXene in combination with transition metal dichalcogenides to design new optical sensors based on surface plasmon resonance (SPR) to use for biosensing and chemical sensing. Highly sensitive SPR sensors have been previously demonstrated with coating of dielectric materials, graphene, or TMDs on metal films [3C2Tx-Au-TMDs in a modified Kretschmann configuration. The theoretical works of the authors show that it possesses enhanced sensitivity as compared to the bare Au film-based SPR sensor.Xu et al.  propose al films ,60. The 2 flakes is the subject of the work of Shi and coworkers [2 has a structure similar to the best-known MoS2, but exhibits stronger photoluminescence quantum yield at room temperature and larger spin-orbit coupling and is a promising 2D material for applications in optoelectronics and spintronics. The difficulty of controlling the interrelated growth parameters makes the synthesis of large-area monolayer WS2 still challenging. Therefore, the reported one-step chemical vapor deposition (CVD) process, by direct sulfurization of powdered tungsten trioxide (WO3) drop-casted on SiO2/Si substrates at atmospheric pressure that yields large-area monolayer WS2, is of great interest. Triangular monolayer WS2 flakes with edge lengths up few hundred microns and homogeneous crystallinity are obtained.The synthesis of large area and good quality TMDs is of paramount importance for their industrial exploitation. The fabrication of monolayer WSoworkers . WS2 has2, which is a TMD structurally similar to MoS2, but with a slightly lower bandgap (1.6 eV), is used in back-gated field effect transistors (FETs) by Urban et al. [2 exhibits strong optical absorption in the visible range, a good light-to-electricity conversion coefficient, and forms FETs with ambipolar behavior with most of the metals commonly used in electronics. Urban and coworkers fabricate WSe2 backgate FETs with Ni Schottky contacts and measure their electrical characteristics under different environmental conditions. They demonstrate that lowering the pressure in air-exposed WSe2 dramatically affects the electrical characteristics turning the FET conduction from the p- to n-type. Furthermore, from the electrical characterization at different temperatures, a gate modulation of the Schottky barrier (SB) at the contacts was proven. In addition, the work reports the temperature dependence of the carrier mobility and the subthreshold swing, as well as the photo response at several laser wavelengths. Some of the results are likely qualitatively valid for other TMD-based devices.CVD monolayer WSen et al. . Two-dim2Se3 [First principle calculations are again performed to investigate the electronic and optical properties of the β-polytypes of indium selenide, β-InSe , and car2Se3 . Recentl2Se3 . β-InSe is the most stable phase of InSe, due to the ABAB crystal stacking mode. The individual layer has a hexagonal structure and shows different electronic and optical properties compared to the bulk . MonolayBased on numerical work, Zhang at al. propose carbon selenide, β-CSe, as a novel ultra-thin stable material with wide indirect bandgap. The β-CSe exhibits slightly anisotropic mechanical characteristics and its bandgap and band-edge curvature are sensitive to in-plane strain, causing the carrier effective mass to be strain-dependent. Zhang and coworkers also propose a heterojunction obtained by stacking α-CSe on a β-CSe sheet. The α-CSe/β-CSe interface constitutes a type-II van der Waals p-n heterojunction with strong built-in electric field across the interface, due to the charges transferring from β-CSe to α-CSe. The built-in potential causes substantial energy band bending, which can be exploited to spatially separate photo-generated carriers in photovoltaic devices. Furthermore, as a metal-free photocatalyst, the α-CSe/β-CSe heterojunction is endowed with an enhanced solar-driven redox ability for photocatalytic water splitting via reduced electron-hole-pair recombination. This study will likely motivate experimental research on the synthesis and the properties of this new material as well as on the design of new devices.2 [2Se3 by interlayer fusion of PdSe2 [2Se3 by first principles calculations. They demonstrate that Pd2Se3 monolayers have a great potential as absorber material in ultrathin photovoltaic devices owing to their quasi-direct band gap of 1.39 eV, high electron mobility (> 100 cm2V−1s−1), and strong optical absorption (~105 cm−1) in the visible solar spectrum. Furthermore, the Pd2Se3 bandgap can be modulated and changed from indirect to direct by biaxial strain. More importantly, monolayer Pd2S3, obtained by replacing Se with S, is stable and can be used in a vertical stack with Pd2Se3 monolayer to form a type-II heterostructure. The simulation indicates that such a structure, used as a solar cell, could achieve 20% power conversion efficiency, higher than that of MoS2/MoSe2 photovoltaic devices. This numerical study presents several challenges for new experiments on 2D Pd2Se3 and Pd2S3 as well as on the application of their heterostructures for efficient solar energy conversion.Layered materials based on noble transition metals, such as Pd and Pt, have gained popularity after the successful exfoliation of PdSe2 ,67,68 anof PdSe2 ,70, Li aof PdSe2  study th3N4, and zinc oxide, g-ZnO, are used to form 2D heterojunctions with transition metal (di)chalcogenides. The investigation of CdS/g-C3N4 [2 [3N4 [3N4 as a function of biaxial strain and pH of the electrolyte, with the objective of optimizing the photocatalytic performances. The numerical simulations show that monolayer CdS weakly contacts with g-C3N4, forming a type II van der Waals (vdW) heterostructure. The predicted bandgaps and optical absorptions indicate that such a heterostructure can absorb visible light and the induced built-in electric field at the interface promotes the effective separation of the photogenerated carriers. Furthermore, the interface adhesion energy, bandgap, and band edge positions can be adjusted by applying a biaxial strain.The quest for efficient water-splitting photocatalysts motivates the first principle studies by Wang and coworkers ,72, in wS/g-C3N4  and g-Zn-C3N4 [2  and grap [2 [3N4  have sui2 or WSe2 are studied as a function of the rotation angles and the biaxial strain [2 heterostructures with appropriate rotation angles possess a suitable bandgap for visible absorption, proper band edge alignment, and effective separation of carriers, being promising visible water-splitting photocatalysts. Conversely, the water oxygen process of the ZnO/WSe2 heterostructures is limited by their band edge positions.The water-splitting photocatalytic properties of the heterostructures formed by g-ZnO with two-dimensional WSl strain  as well.l strain  and densl strain . Wang an3N4 or g-ZnO/WS2-based water-splitting solutions to prepare hydrogen fuel.These theoretical findings offer a sound basis to develop CdS/g-CAlthough graphene has proven to be an unmatched material due to its abundance of properties and applications, other 2D materials have been isolated or synthesized to better serve specific needs. Owing to their bandgaps, an example of a feature that graphene does not possess, transition metal (di)chalcogenides have emerged for instance as materials well-suited for electronics, optoelectronic, and photocatalytic applications.The layer control in two-dimensional materials provides an effective strategy to modulate their optical and electrical properties. The formation of van der Waals heterostructures, with enhanced features, offers a unique opportunity to fabricate new materials.This book offers a collection of research papers that cover the state-of-the-art of fabrication, properties, and applications of graphene and graphene-like materials, along with their heterostructures.2Se3/Pd2S3 or CdS/g-C3N4, and g-ZnO/WS2 are proposed based on first principle calculations.The electronic and optical properties of several popular 2D materials and their heterojunctions, as well as the opportunities for their exploitation in sensors, electronic and optoelectronic devices, and water-splitting photocatalysts, are extensively discussed. New 2D materials and new heterostructures like α-CSe/β-CSe, PdThese studies consolidate and extend the general knowledge of the physics and technology of 2D materials and offer many theoretical results that will likely be the challenging subject of forthcoming experimental investigations.


Dear Editor,IDO1 mRNA expression on prognosis in 33 diverse cancer types, identified 43 key genes involved in the effect, and defined the weighted average of all the 43 key gene expressions as 43‐gene score to reflect the integrated role of 43 key gene expressions in the relationship between IDO1 expression and cancer prognosis. We mined the potential regulator of 43 key genes and explored its impacts on regulating the expressions of 43 key genes and enhancing the therapeutic efficacy of IDO1 inhibitor  inhibition has been developed as a potential new tool in cancer immunotherapy and some IDO1 inhibitors have been in clinical trials.IDO1 expressionsIDO1 expression on prognosis in 33 diverse cancer types from The Cancer Genome Atlas (TCGA)  and X‐C motif chemokine ligand 1 (XCL1), still performed well in the validation set 2  regression model to compare the overall survival (OS) and progression‐free interval (PFI) of patients with high and low ) Figure . These tt Figure . We alsot Figure . For thet Figure . Within t Figure . Even so2 Figure . All of IDO1 had a beneficial effect on prognosis in patients with tumor immune subtype C1  or C2 (IFN‐gamma Dominant), a disadvantageous effect in patients with C3 (Inflammatory) or C5   Figure . We iden) Figure . The cors Figure . These sIDO1 high expression correlates to good prognosis in patients with high 43‐gene scores, a hierarchical logistic regression model was employed to fit the efficacy measures of 251 drugs across 983 human cell lines, and further to identify drugs more sensitive to that cell lines with higher 43‐gene scores. As a result, EGFR inhibitor gefitinib was mined  and the National Natural Science Foundation of China (NO. 11971117).The datasets used or analyzed in this study are available from the corresponding author on reasonable request.All the authors consent for publication.The authors declare no conflict of interest.SuppMat1Click here for additional data file.SuppMat2Click here for additional data file.SuppMat3Click here for additional data file.