Natural conversations are characterized by short transition times between turns. This holds in particular for multi-party conversations. The short turn transitions in everyday conversations contrast sharply with the much longer speech onset latencies observed in laboratory studies where speakers respond to spoken utterances. There are many factors that facilitate speech production in conversational compared to laboratory settings. Here we highlight one of them, the impact of competition for turns. In multi-party conversations, speakers often compete for turns. In quantitative corpus analyses of multi-party conversation, the fastest response determines the recorded turn transition time. In contrast, in dyadic conversations such competition for turns is much less likely to arise, and in laboratory experiments with individual participants it does not arise at all. Therefore, all responses tend to be recorded. Thus, competition for turns may reduce the recorded mean turn transition times in multi-party conversations for a simple statistical reason: slow responses are not included in the means. We report two studies illustrating this point. We first report the results of simulations showing how much the response times in a laboratory experiment would be reduced if, for each trial, instead of recording all responses, only the fastest responses of several participants responding independently on the trial were recorded. We then present results from a quantitative corpus analysis comparing turn transition times in dyadic and triadic conversations. There was no significant group size effect in question-response transition times, where the present speaker often selects the next one, thus reducing competition between speakers. But, as predicted, triads showed shorter turn transition times than dyads for the remaining turn transitions, where competition for the floor was more likely to arise. Together, these data show that turn transition times in conversation should be interpreted in the context of group size, turn transition type, and social setting. In everyday conversation, speakers’ turns are well coordinated in time. As noted in a seminal article by Several laboratory studies have examined whether speakers indeed already begin to plan an utterance while still listening to their partner. This is not self-evident because both comprehending and planning utterances require cognitive resources, and because the conceptual and linguistic encoding processes may interfere with each other e.g., . In thesThough these studies unambiguously supported the Early Planning Hypothesis, they consistently failed to elicit latencies that were as short as the mean turn transitions obtained in quantitative analyses of conversational corpora. These are, as noted above, often around 200 ms. For instance, in the study by A number of potentially important factors have been discussed in the literature. For instance, in natural conversation speakers can often refer to mutually shared knowledge , which facilitates understanding and the generation of appropriate responses . RelatedIn short, there are many potential reasons why laboratory-recorded response times are much longer than turn transition times in conversation. In this paper, we draw attention to a factor that seems to have been largely overlooked so far: competition for turns, which may arise in multi-speaker conversations but is much less likely to occur in dyadic conversations and is absent from standard laboratory experiments where participants are tested individually.To elaborate, in a standard psycholinguistic experiment, participants are tested individually and all of their responses  are recorded and averaged. The same is likely to apply in analyses of dyadic conversations. Here participants abide by common turn-taking rules, which involves that the current speaker gives the other speaker(s) the opportunity to take the turn before continuing to speak themselves . This meThe situation is different in multi-party conversations. Here several speakers may wish to speak but typically only one person, the fastest responder, obtains the next turn . Thus, tTo illustrate the impact of this sampling bias on typical response times we describe the results of simulations showing how the mean and median response times from a laboratory experiment would change, relative to the mean and median of all response times, if for each trial only the fastest response times from two or more participants were recorded and averaged. For instance, if participant A had a response time of 650 ms and participant B had a response time of 700 ms, either both latencies or only A’s latency would be entered into the dataset. These simulations show the impact of recording all or a subset of the response times and combining them in a mean or median. They do not concern the ways the speakers’ behavior may change when they talk in smaller or larger groups.Then we turn to actual conversations. As discussed above, in quantitative analyses of multi-party conversation, the recorded turn transition time is the response time of the fastest speaker. It follows that as more people participate in a conversation, the mean turn transition time, which is the mean fastest response time, should decrease even when the individual speakers’ behavior is not affected by the number of participants. The same holds for the median. Of course, the speakers’ behavior may change as well. For instance, speakers may begin to plan or launch utterances earlier as they compete for the floor with more co-participants. Although conversation as a whole may be characterized as collaborative, there is often a competitive element to turn-taking , arisingNo systematic quantitative comparison of turn transitions in dyadic and multi-party conversations appears to exist to date. The existing quantitative studies of turn transition times focused on dyads e.g.,  or on muTo begin to fill this gap in the literature, we present an analysis of a corpus of unscripted casual conversations investigating the effect of group size (dyadic versus triadic) on turn transition times. The conversations were recorded in a laboratory, and the participants in the dyadic conversations also participated in the triadic conversations. We distinguished between two types of turn transitions, question-response sequences (QR transitions hereafter) and non-question-response turn transitions (non-QR transitions). The rationale for this is that questions often specify a specific respondent even in multi-party settings e.g.,  or addreIn sum, we propose that turn transition times in multi-party conversation may be reduced for two related reasons: First, there is a statistical reason; the fastest of several planned responses determines the recorded turn transition time. Second, the speakers’ behavior may change when they compete for the turn. Note that we are not proposing that competition for the floor is the only, or even the most important, cause of short turn transition times in multi-party conversation. We view it as one factor that may be influential but has so far received little attention in the psycholinguistic literature on conversation.The aim of the simulations was to examine how much the response time distribution obtained in a laboratory experiment would change if, instead of recording all responses, for each trial only the fastest response from two or more participants were recorded. The observed response times, on which the simulations were based, come from an experiment by The participants were 21 adult native speakers of Dutch. The data from one participant were excluded due to technical failure. The participants were asked to listen to recorded questions about the objects on their screen  and to respond as quickly as possible with “ja” (yes) or “nee” (no). There were always four objects on the screen. The referent object (the sweater in the example) was always included in this four-object display, but not necessarily in the color mentioned in the question. There were two experimental conditions, called the monochrome and the multi-color condition. In the monochrome condition all objects had the same color. In the multi-color condition they had different colors. This affected when the participants could begin to plan their response. For instance, when the question was about a green sweater and all objects on the screen were green, the participants could prepare to answer affirmatively as soon as they had comprehended the color adjective. Similarly, when all objects were white, the participants could begin to prepare a negative answer as soon as they had comprehended the adjective. In contrast, when the four objects on the screen had different colors, the participants could only begin to prepare a response after they had comprehended the noun as well. Thus, the Early Planning Hypothesis predicts that the response times should to be shorter in the monochrome than in the multi-color condition. The experiment included 256 trials, with 128 monochrome and 128 multi-colored displays. 64 monochrome and 64 multi-colored displays required an affirmative answer. The remaining displays required a negative answer.A native Dutch speaker transcribed the utterances. Incorrect responses (less than 3% of the responses) and outliers, i.e., responses times deviating by more than 2.5 standard deviations (SD) from the condition mean (0–3% of the responses per condition) were excluded from the analyses. For the simulations, we created simulated pairs of participants, and for each simulated trial selected the fastest response time: First, we paired each of the 20 participants with each of the other participants, yielding 190 pairs. To obtain simulated data for each trial, response times were selected at random from each participant’s data per condition (for instance from the response times in the monochrome affirmative condition). Thus for each trial, two response times, one from each participant, were selected. This was done 32 times per condition, simulating the 32 trials of the experiment. For the entire experiment, this yielded a dataset of 44,442 response time pairs. The shorter of the response times was selected and plotted as the “winning” response time per trial.The simulated condition means and medians of the winning response times are tabulated in As the numbers in the gain columns show, in the multi-color conditions, the estimated mean response time was shorter by 92 ms in both the affirmative and negative response condition for the “winning” responses compared to all responses. The medians changed less, by 28 and 48 ms for affirmative and negative responses, respectively. This is because medians are less affected by extreme values than means. In the comparison of the two response times per trial and the selection of the fastest one the longest response times are most likely to be discarded.In the monochrome conditions, the response distributions were bimodal, as can be seen in the density plots. This is because here participants could, but did not have to, respond before the end of the question. As all objects on the screen had the same color, they could respond as soon as they had understood the adjective (“Do you have a green?”), which yielded response onsets shortly before the end of the question (“.sweater?”). However, participants often only responded after they had heard the entire question, yielding much later response onsets. The average reduction in mean response time was 149 ms for affirmative and 128 ms for negative responses. The medians were reduced by 66 and 75 ms, respectively.In further simulations we assessed how the typical “winning” response times changed when responses from more than two participants were included and compared, and the fastest response time was selected. The simulations were run in the same way as described above, but instead of pairs, we generated sets of up to ten participants. The goal of the simulations was to show how the recorded mean response times would change when, instead of including all responses, only the fastest, “winning,” response per trial from a sample of two or more participants was recorded. The key point–that the mean of the fastest response time is bound to be shorter than the average time of all response times–could have been made without the simulations. However, we hope that the simulations make the point more apparent for the reader. In addition, we obtained estimates of how much the mean and median response times across all responses versus the fastest responses differed from each other. Whether similar values would be obtained for other datasets remains to be seen. The differences should depend on the properties of the response distributions. When the participants already perform, on average, close to the human performance limit and when there are few slow responses due to, for instance, lapses of attention, the difference will be smaller than when the participants respond more slowly, and/or more erratically. However, regardless of the properties of the distributions, the mean of the fastest response times will always be lower than the mean of all response times.We simulated the effect of selecting all responses versus a subset of the responses of the participants in an experiment. We did not simulate any changes in the behavior of the participants that might occur when they actually compete with each other. Whether and how competition affects participants’ response times in a laboratory setting can be assessed in further work. The point of the simulations was to illustrate the potential impact of a difference in the data from laboratory experiments on speech planning and corpora of conversational turn-taking, which often include multi-speaker conversations. The experimental data include all response times, but, as explained before, the corpus data typically only include the fastest responses of speakers competing for the floor. This may contribute to explaining why turn-taking in multi-party conversations appears to be so fast, compared to participants’ response times in laboratory experiments.Note : OutlierThe aim of the corpus study was to examine how group size may influence turn transition times, i.e., the gaps and overlaps between turns in conversation. We expected turn transitions to be faster in triadic than in dyadic conversations. This should be the case for two reasons: First, in a triadic conversation two speakers may plan a response to a turn but only the fastest response enters the analysis and thus determines the turn transition time. As shown by the above simulations, the mean of these fastest turn transition times should be shorter than the mean of all turn transition times from responses provided by a single speaker in a dyadic conversation. Second, competition for the turn is more likely to arise in triadic than dyadic conversations. These influences should affect turn transition times in non-QR transitions, i.e., turn transitions that are not questions followed by responses. We did not expect a strong group size effect for question-response sequences (QR transitions), since here the current speaker often selects a next speaker, thus reducing competition for the floor.We analyzed a corpus of 12 dyadic and 12 triadic conversational interactions, each about 20 min in length. The conversations were selected from a larger corpus, based on the amount of pre-existing relevant annotation from a previous study . All conParticipants arrived in groups of three and were recorded while participating first in a 20-min triadic conversation followed by a 20-min dyadic conversation. To create the latter, one of the three people was asked to leave the recording laboratory. Throughout the session, the participants’ eye movements were recorded to address questions outside of the scope of this paper e.g., . Which pThe conversations were unscripted and unprompted in terms of topic. Participants were left alone in the recording laboratory and asked to talk to each other as if they were engaging in casual conversation outside of the laboratory environment. They could talk about anything they liked, except for topics which might make their partners feel uncomfortable, for instance for ethical reasons. The entire test session per group lasted around 2 h, including set-up, instructions, two 20-min conversations, and obtaining a range of questionnaire measures at the end of the session, which were not used in the present analyses.The recordings were made in a laboratory setting at the Max Planck Institute for Psycholinguistics in Nijmegen (Netherlands) and later, when recruitment of native English speakers in Nijmegen became too slow, at the University of Manchester . The audio recordings were made using uni-directional head-mounted microphones (Shure SM10A) which are suitable for detailed phonetic analyses of the audio signals from the individual speakers. For details on the laboratory set-up and equipment see We distinguished turn transition times for question-response sequences (QR transitions) from other turn transitions (non-QR transitions). For the analysis of QR transitions, the full 20 min of each conversation  were selected. To identify QR transitions we used the coding scheme developed by For the analysis of non-QR transitions, the first 5 min of each conversation were selected . All speaker changes were included, except for the following categories: (a) QR transitions; (b) backchannel responses, such as “mhm,” “uhu” ; (e) traTurn transitions were measured from the offset of vocalization of the first turn to the onset of vocalization of the next turn. This meant that turn transitions involving overlap resulted in negative numbers, and turn transitions involving a gap resulted in positive numbers. Vocalizations were any form of verbal utterance. Audible inbreaths, coughs, and such like were excluded see also . The onsWe excluded as outlier turn transition times deviating by more than 1.5 times the interquartile range . This was done separately for the dyads and the triads, combining QR and non-QR transitions within those groups. There were 90 outliers (5.98% of the data), 67 QR transitions and 23 non-QR transitions. The data included in the analysis were characterized by a residuals distribution low in skewness and kurtosis  with values falling well within the range of those acceptable for a normal distribution. We thus considered the assumptions underlying linear mixed effects models met. Means, medians, and modes have been calculated to give a comprehensive description of the data .2(7) = 1124.1, p < 0.001]. For the participant taking the turn, by-participant intercepts and slopes were included for transition type and overlap. As the model already showed signs of approximate singularity, no interaction between the by-participant slopes were included. The participant identifiers were distinct for dyads and triads. As such, including a by-participant slope for group size was not possible. However, the by-participant contribution was captured by distinct by-participant intercepts for each conversation. To calculate p-values, Satterthwaite’s method was used to compute the denominator degrees of freedom with the lmerTest package  = −1.97, p = 0.053]. That is, in those cases where turn transitions involved a gap, the gaps for triads were on average 55 ms shorter than those for dyads. Despite the model revealing no difference in terms of overlap duration for the two groups, we applied a second model to test whether group size nevertheless impacts on the probability of overlap. While overall , non-overlap was more probable than overlap , this logistic model highlighted that the difference in mean turn transition duration between the groups was mainly driven by overlap being more probable for triads than dyads . Additionally, there was an interaction between transition type and group size . Decomposing this interaction with expected marginal means  but not in QR transitions .The statistical models, reported in We expected at most a small effect of group size for QR transitions because in such sequences the current speaker often selects the next speaker, which reduces competition for the floor compared to non-QR transitions. Questions in conversation can have many features that single out a particular addressee, such as the use of gaze or the addressee’s name , 2003. TThe absence of a group size effect for QR transitions might appear to be inconsistent with the results of the simulations reported above, which yielded strong group size effects for responses to questions. However, the simulations merely illustrated how means of response times change when they are based on all responses versus a subset of responses, specifically the fastest responses per trial from the set of participants. The linguistic properties of the responses used as the basis for the simulations, i.e., that they happened to be responses to questions, played no role for the outcome of the simulations. In other words, the simulations, while based on QR turn transitions, do not reflect the pragmatic attributes associated with such transitions and may, paradoxically, more accurately capture speakers behavior in non-QR transitions.For non-QR transitions a substantial effect of group size was indeed obtained: The mean turn transition time for dyads was more than double the time observed for triads. The 151 ms difference corresponds roughly to the duration of a syllable in colloquial English . This diOf course, this initial finding must be supported by further work. A strength of the current corpus is that the same participants were involved in the triadic and dyadic conversations. An obvious shortcoming is that for all speakers the triadic conversations preceded the dyadic ones. It is conceivable that the observed group size effect for non-QR transitions resulted from fatigue or decreasing motivation to talk in the second session of the study . However, we think this is unlikely because fatigue or decreasing motivation should not selectively affect the non-QR transitions, but also the QR transitions, for which no group size effect was found. Nonetheless, further studies with counterbalanced order of group sizes, or between-participant designs, are needed to separate group size and order effects more conclusively. Moreover, it would also be good to consider larger corpora and corpora of different languages to see how generalizable the present effects are.We set out to investigate how the number of potential next speakers in a conversation may influence turn transition times. The starting point was the observation that in quantitative corpus analyses of multi-party conversation, only the fastest response to a turn enters the analysis. The reason is that participants who needed more time to respond may end up not producing a response, or they may produce it after someone else has taken the next turn, thus precluding it from the analysis. This may partly explain why turn transition times in natural conversations are so short compared to the response times in laboratory-based experimental studies of turn-taking. In the lab, participants are tested individually and all response times (except for some extremely long or short response times), feed into the mean. Indeed, the results of our simulation study confirmed that, up to a set size of about five participants, the more participants were entered into the model as potential responders, the shorter the mean “winning” response time became. This principle–that the mean of a subset of short response times will be lower than the mean of all response times in a dataset–is bound to play a role in multi-party conversation as well.However, turn transition times may also be shorter in triads than dyads for other reasons, in particular differences in competition for the turn. At those points in conversation where turn transition becomes relevant (i.e., points of semantic, syntactic and pragmatic completion, The earlier corpus-based literature on turn transition times has concerned dyads, multi-party conversation, or a mixture of the two e.g., . This coWhile we have here focused on variables that may contribute to responses in conversation being faster than in the laboratory, there are also variables that occasion slow responses in conversation. These may relate to uncertainty about the content of the response e.g., , or theyIn sum, we propose that the absence of slow responses from typical corpus data, enhanced by the competition for turns, may partly explain the short turn transition times in everyday multi-party conversation. Further work is needed to substantiate this suggestion. In addition, the impact of other variables that may also influence turn timing e.g.,  remains https://osf.io/um7ta/?view_only=9cd734fa2e9346e3aa89b3f958b46ef6.The datasets presented in this study can be found in an online repository: The studies involving human participants were reviewed and approved by Social Sciences Faculty Ethics Committee, Radboud University Nijmegen. The patients/participants provided their written informed consent to participate in this study.JH and AM conceptualized and wrote the manuscript. PA designed the simulation. CD contributed to the design and data collection analyzed in Study 1. KK and JH collected the data for Study 2. KK and MG annotated the data for Study 2. PA analyzed the data for Studies 1 and 2. CD and MG contributed to the analysis for Studies 1 and 2, respectively. All authors contributed to the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The high conductivity of graphene material (or its derivatives) and its very large surface area enhance the direct electron transfer, improving non-enzymatic electrochemical sensors sensitivity and its other characteristics. The offered large pores facilitate analyte transport enabling glucose detection even at very low concentration values. In the current review paper we classified the enzymeless graphene-based glucose electrocatalysts’ synthesis methods that have been followed into the last few years into four main categories: (i) direct growth of graphene (or oxides) on metallic substrates, (ii) in-situ growth of metallic nanoparticles into graphene (or oxides) matrix, (iii) laser-induced graphene electrodes and (iv) polymer functionalized graphene (or oxides) electrodes. The increment of the specific surface area and the high degree reduction of the electrode internal resistance were recognized as their common targets. Analyzing glucose electrooxidation mechanism over Cu- Co- and Ni-(oxide)/graphene (or derivative) electrocatalysts, we deduced that glucose electrochemical sensing properties, such as sensitivity, detection limit and linear detection limit, totally depend on the route of the mass and charge transport between metal(II)/metal(III); and so both  should have the optimum values. Glucose is a complex molecule, containing six carbon atoms, which is difficult to split, via controlled stepwise oxidation. Consequently, facilitating the glucose electrooxidation reaction (GER) and the further evolution of the enzymeless glucose sensors, remains a challenge to the scientists. The main key requirements for the development of any high performance enzymeless electrochemical sensor are ,2,3,4: -based electrodes concerning their application into enzymeless electrochemical glucose sensors ‘field, are huge. Because of this fact many research groups have focused on the amelioration of such electrode’s characteristics, proposing new synthesis route methods. In the current review paper, we mainly focused on the last three years works and we tracked mainly four different synthesis methods that have been explored for the development of non-precious graphene-based glucose sensing electrodes: (i) direct growth of graphene (or oxides) on metallic substrates, (ii) in-situ growth of metallic nanoparticles into graphene (or oxides) matrix, (iii) laser-induced graphene electrodes and (iv) polymer functionalized graphene (or oxides) electrodes. Additionally, we discuss glucose electrooxidation mechanism over Co-, Ni-, Cu- (and their oxides) graphene (or its oxides)-based electrocatalysts. Finally, the status and future perspectives of the non-precious graphene (or oxides)-based glucose sensing electrodes, are outlined.3O4 and Co(OH)2 [Graphene (or its derivatives) support is the element of the metal (or oxide)/graphene-based electrode that mainly contributes to quick charge transfer during analyte molecule sensing process . In addi Co(OH)2 ,14,15,163O4-based electrodes in 0.1 M NaOH environment (pH ≈ 13), the majority of the research groups report a three-step glucose sensing mechanism [Over the Coechanism :(1)Co3O42/C6H12O6 complex plays a critical role. Moreover, as it is observed the GER mechanism is a multi-step electron transferring process requiring a lot of active sites as well as abundant electroactive surface area. This fact maybe explains the improved performance of the non-precious electrodes when supported onto graphene (or its derivatives) material. Under alkaline environment, the higher oxidation states of the metallic sites are necessary in order the GER to be promoted. According to the described GER mechanism (Equations (1)–(3)), the formation of CoOSpecifically, reduced graphene oxide (rGO) is reported ,19 as an3O4 onto rGO and onto GO, is described by the following equations: According to other research groups  GER mech2 (or Co(IV)), producing gluconolactone (C6H10O6) as the final product. The redox couple CoOOH and CoO2 usually appears at ca. 0.6 V and 0.5 V vs. Ag/AgCl, respectively. The conversion of CoO2 to CoOOH ends at higher oxidation number capable of oxidizing organic molecules [As it is shown GER is promoted via a redox cycle between CoOOH (or Co(III)) and CoOolecules . − groups derived from the alkaline electrolyte. It is characteristic that the presence of OH− is necessary for the CoOOH/CoO2 redox couple formation and the subsequent GER progress. Moreover, it has been reported that when Co3O4 nanoparticles are included into graphene’s network, its active surface area is dramatically increased, and the adsorption of much more reactant molecules is enabled [When rGO support  is used, enabled . 3O4-based electrocatalysts and promoting GER. For example, Zheng et al. [3O4-NC nanomaterial), effectively promotes GER, providing a wide contact area and abundant active sites. Selectively, the glucose OH− groups are attracted by the nitrogen doped-support [2 oxidation peaks and at ca 0.14 and 0.45 for the CoO2/CoOOH reduction peaks [Doping the support with N and/or another element  is proposed as another effective strategy for increasing electrocatalytic activity of the Cog et al.  reported-support , that caon peaks . − species concentration is reduced and so many Co (or oxides) active sites remain uncovered. Thus, when GER takes place, the Co-active sites are initially oxidized at higher states, and then they are engaged into GER, reducing sensor’s response, stability, and others of its operational parameters. So, under neutral or slightly alkaline environment electrocatalyst should own increased OH− adsorption ability [x or Co-M(OH)x-based electrocatalysts. Examining GER mechanism, it can be deduced that the presence of oxidative species is essential for its promotion. However, the production of those oxidative species at neutral media, meaning values close to the blood serum pH (pH ≈ 7.2), still constitutes a challenge. When the pH of the environment lowers approaching neutral values, the OH ability . On acco2-based materials, such as Co(OH)2@3D graphene frameworks [The presence of the redox CoOOH/Co(II) pair over Co(OH)ameworks , is also2O4/rGO (0.1 M NaOH), may offer more hydroxide species, leading to a multi-step and more complex GER mechanism [The high potential values cause the oxidation of the Co(II) to CoOOH which is favorable for the GER in order to be preceded. Furthermore, the combination of Co with another metal oxide, e.g., CoMnechanism :(7)CoMn2The alkaline environment causes the oxidation of both metals to the CoOOH and MnOOH species (the 1st one in Equation (7)), and then each one of those, reacts with glucose molecules forming metal oxides and gluconolactone (the 2nd and 3rd in Equation (7)). In general, the bimetallic Co-M(M = Ni or Cu) electrocatalysts exhibit higher activity towards GER  with theAs it is described from Equations (8)–11), also in the case of the bi-metallic electrocatalysts the generated Ni(III) and Co(III) are the species that react with glucose and so their quantity is very critical for the progress of GER. As being reported, by many research groups , also in,26 the k2O4@functionalized porous graphene, GER is also promoted due to the CoOOH and CuOOH formation [2 created centers. In this case GER mechanism is more complicated and is favored by the charge transfer between the Cu(OH)2 and CuOOH as well as between the CoOOH and CoO2. Thus, also at this case the NiOOH and CoOOH, are the two crucial ‘species’ that promote GER . When GEormation . On the ormation , that glUnambiguously, rGO and porous graphene materials lower the internal resistance of the as-fabricated electrode enabling GER via the formation of redox pair peaks, corresponding to the oxidative, couples’ formation. As the resistance is low, the charge transfer becomes easier and the responsible for GER progress metal oxide couples, M(II)/M(III) or (M(III)/M(IV)) are created faster. Graphene structure manages to offer conduction channels that remain always conductive, improving GER kinetics. Moreover, as it is analyzed in the current review paper, the appropriate modification of the graphene (and its derivatives) can significantly enhance the electrocatalytic activity. − groups, producing Ni(III) that is responsible for the GER evolution as being described below [In alkaline environments, Ni metal is oxidized forming Ni(II) which then ‘captures’ the OHed below :(15)Ni+2− concentration is responsible for the Ni(II) (Equation (15)) and the Ni(III) (Equation (16)) formation. Therefore, similar to the Co (or Co oxides)-based electrocatalysts, the key element for the GER evolution mechanism and at the same time for the quick sensor response is the very high conductivity of the support along with the large available surface area [More precisely, based on the mechanism described from Equations (15)–(17) the OHace area . This meIn literature, a few research groups ,33 repor− plays an important role motivating many scientific groups to develop NiO-based electrocatalysts. Thus, the graphene supported Ni-based glucose sensors operational parameters, seems to be further improved when Ni is replaced by its oxide [According to the GER mechanism progress over those electrocatalysts, the OHts oxide ,35,36,37The most common reported GER mechanism in alkaline media over NiO-based electrocatalysts is described by the following equations ,35,36,37Usually the mechanism described in (Equation (18)) is expressed with one pair redox peak at ca 0.6/0.4 V vs. Ag/AgCl (by the aid of cyclic voltammetry technique) . However2O4@rGO, provide more active sites for the adsorption of glucose molecules. According to the authors, the rGO support aids much more active sites to be exposed, while when it is removed the binary-oxide aggregates forming large spheres. Further, the transition metal oxides conductivity is improved even more when two transition metal oxides consisted of two metal cations are combined. To be specific, Dong et al.  deduced Thus, in the case of the binary oxides the production of NiOOH and second metal-OOH species, further favors the GER mechanism proceeding according to the following equations:Initially, the binary oxide is oxidized, leading to the Ni(III) and Mn(III) formation which then they react with glucose and then are reduced back to Ni(II) and Mn(II), respectively, producing gluconolactone. Thoroughly, the support’s conductivity and the surface area are of great importance as they should ensure a continuous charge transfer between the oxidized metals. Glucose electrooxidation reaction over graphene (or its derivatives) supported Cu (or CuO)-based electrodes have also been examined from many research groups ,40,41. I2 (Cu(II) species), which in their turn are oxidized to Cu(III) (CuOOH and Cu(OH)4−). The latter catalyses the adsorbed glucose molecules to gluconolactone and then to gluconic acid, while Cu(III) is reduced back to Cu(II).The GER mechanism in alkaline media , over CuO/Cu2O-based electrocatalysts proceeds in a similar way [According to the multi-step GER mechanism (Equation (20)), Cu is initially oxidized to CuO and Cu(OH)ilar way : (21)CuOIn more neutral environments, such as in PBS (pH = 7.4), GER is evolved via the formation of Cu (I)-glucose complex  which th2O4, glucose electrooxidation proceeds as follows: In case of a binary Cu-based electrocatalysts, namely CuCoAnalysing GER mechanism leads to the realization that the sensing properties of enzymeless glucose electrochemical sensors, such as sensitivity and detection limit, totally depend on the route of the mass and charge transport between M(II)/M(III) or M(III)/M(IV). Thus, the key-requirement is this route to be of low internal resistance and at the same time to offer the capacity for as many as possible active sites. Graphene and especially reduced graphene oxide are considered ,45,46,47Graphene is usually used independently serving as support to metallic nanoparticles. However, recently in the pursuit of more stable electrochemical glucose sensors, direct growth of graphene nanosheets onto conducting substrates is suggested. The higher advantage of this synthesis method is the omission of the placement step of the graphene-based nanoelectrode onto the electrode’s substrate. In such a way there is not any risk of destroying graphene’s structure, thus the cost lowers and this is a timesaving procedure . FurtherCurrently, according to our literature research, the CVD synthesis is suggested as the most appropriate method for the graphene direct growth onto metallic substrates ,51,52; d−1cm−2), excellent selectivity and quick response (<5 s). The good sensitivity and stability were attributed to the full coverage of the bilayer graphene from the electrodeposited monolayer of the Cu nanoparticles   B. 4 as carbon source, managed to fabricate a glucose electrode with a very high porosity (11 μAmM−1cm−2), , C. 3 μAmM−1cm−2) and 0.0079 μΜ limit of detection. The electrode’s so high sensitivity was attributed to the porous and interconnected networks in combination with the lack of binders during its synthesis procedure, which dramatically decreased the internal resistance. Accordingly, However, the direct growth of graphene onto metallic substrate electrode encloses a possibility the graphene to be contaminated with metallic impurities , influen−1cm−2. Chen et al.  reported2+ onto ITO, applying a constant potential and for a specific time duration   E. The unThus, in the case of the direct growth of graphene-based electrode onto a substrate, the synthesis method that is used, strongly depends on the kind of the substrate. The use of a Cu foil for example, allows the direct growth of the graphene mostly via a CVD method. The CVD method when includes Ni foam as an incidental element seems to reduce the electrode’s internal resistance, producing electrodes of much higher sensitivity. Based on this, graphene can be directly grown via a CVD method on a Ni foam substrate, which plays the role of a 3D skeleton of the electrode, giving also remarkable results as far as concerns the electrode’s sensitivity. However, the use of a different than metallic substrate requires the initial growth of graphene directly onto the metal substrate and then its subsequent transfer to the desired non-metal substrate. That means that there is great risk of damaging graphene’s structure. Thus, in order this transfer step to be avoided, but also an alternative substrate to be used, a different synthesis method should be adopted; for example, the direct co-reduction of GO and metallic ions onto a substrate. The use of graphene material as support enhanced the electrocatalytic activity, of the non-precious metals, receiving a lot of attention for glucose sensing ,12,60. HOne of the most common approaches that is adopted when in-situ fabrication synthesis route is followed, is the application of metallic organic frameworks (MOFs) to the role of the precursor. The MOFs are being formed by the combination of inorganic element  being surrounded by organic linkers. Specifically, they own a cage-like structure which imparts its crystalline porous structure and an extremely huge internal surface area. Those special characteristics along with their functionable structures, chemical tunability and high compatibility, have make MOFs very attractive materials for a variety of applications . The MOFs involvement ,62,65,682P growth into graphene process, suggested by Zhang et al. [−1cm−2 and the limit of detection, 0.44 μM. Taking as an example, g et al. . InitialAs the authors report in this case graphene material played a dual role; as they enhanced both NiP nanoparticles activity and stability, while at the same time increased MOFs electrical conductivity and mechanical stability. However, this is a time-consuming method, consisting of multiple steps and depends on many chemical reagents, whose amount, and the technique that they will be utilized, play a great role to the resulting electrocatalyst’s properties. Thus, at this point arises the issue of the reproducibility of the above synthesis method. According to some research groups the defects and edge planes of exfoliated graphene (EG), can enhance electrochemical glucose sensor sensitivity. It is characteristic that in-situ MOF-exfoliated graphene could promote quick heterogeneous electron transfer of redox molecules, conferring a very low M(II) redox potential, thus accelerating GER. Moreover, it should be mentioned that sometimes exfoliated graphene is chosen instead of graphene, because is more soluble to some reagents.A characteristic example of in-situ growth of 2D MOF onto electrochemical EG surface synthesis method is depicted in Since graphene material plays the crucial role to the final glucose electrodes operational characteristics, some research groups further explored the effect of the production method of exfoliated graphene to an in-situ synthesis method. Particularly, Chen et al. ,69 and o−1cm−2 and the limit of detection equals to 0.36 μΜ. −1cm−2) non-enzymatic electrochemical glucose sensor was achieved. In general, the functionalization process of the MOFs with an etching reagent can lead to further expose of the metal active sites, making available more electrochemical active surface area. Making one step further, Sun et al.  fabricatThe in-situ fabrication synthesis strategy using MOF as sacrificial element certainly improves electrodes sensing performance. However, the controllable synthesis  as well as the development of a highly efficient interface for fast charge transfer, constitutes a great challenge. Currently, the MOF functionalization seems to enhance this interface, meanwhile much further research is necessary. −1cm−2 and the limit of detection 0.34 μM  and the graphene (or its oxides) nanosheets seems to be the key-requirement for a high electrocatalyst glucose sensing performance. Consequently, since the type of the in-situ element along with other chemical reagents that are involved into the synthesis procedure define the synergy, this synthesis method should thoroughly be explored. Laser induced method for graphene synthesis and functionalization is very promising . Using lIt has been found  that undLaser induced graphene from polymers (or known LIG) seems to thrive as a process since its discovery in 2014 ; while i2 carbons of PI [Commercial polyimide (PI) polymer is one of the most common graphene sources that is explored for LIG electrode development. The graphene’s hexagonal structure is created via subsequent carbonization and graphitization processes, due to the aromatic spns of PI . Howeverns of PI , while oAt present, electroless processes are reported as appropriate alternative methods for ensuring homogenous deposition of metallic nanoparticles onto LIG or even the creation of metallic layer onto it. Substrate-assisted electroless deposition method, developed by Qu et al. , is a fa−1cm−2 and the detection limit, 0.39 μM  process lasted only several minutes, ensuring at the same time a very great homogenous deposition of them onto the LIG surface.Thus, two factors (linked to the synthesis method) that affect the sensitivity and other operational parameters, of a non-enzymatic LIG-based electrochemical glucose sensor, are tracked. The first one is the method of the metallic nanoparticles or metallic coating layer addition to the LIG that will ensure homogeneous placement and so raise of the LIG conductivity, while the form of the LIG , which essentially should be further explored, is the second one. −1cm−2) (compared to foams) to glucose detection. Zhu et al.  followed−1cm−2 glucose sensitivity and 250 nM low detection limit  of 0.41 μM, indicating that the process needs further improvement.Thus, since the LIG electrodes give very promising results to electrochemical glucose detection, the researchers have started searching methods for ameliorating its characteristics. In-situ growth of metal nanoparticles into support matrix constitutes a strategy that as we highlighted in our previous sections, ameliorates electrochemical glucose sensors operational characteristics. Thus, the LIG can also be further modified by embedded metal nanoparticles into its matrix. Zhao et al.  reportedUnder this background, the design of versatile and facile strategy for fabrication of flexible, shape-controllable metal-based LIG electrodes on various substrates and shapes, needs further exploitation. Laser direct techniques are simple, convenient, and of low cost, but are currently applicable to customize PI substrate mainly, while the metallic elements are electrodeposited onto the LIG after the laser engravement. However, metallic heterogeneous polymer precursors can be used to in-situ incorporate metal oxide nanoparticles, but this method is at premature stages. Graphene functionalization is a strategy that aims at its surface modification for altering graphene’s chemical and physical properties, mainly reducing the cohesive forces that are deployed between the sheets . This isGraphene as material displays aggregations through Van der Waal interactions, and when defects and functionalities (such as oxygen groups) are in excess, its conductivity and consequently electrocatalytic activity of the graphene-based catalyst, are negatively affected. Therefore, for eliminating such phenomena, organic conducting polymers  are used as graphene modifiers and via non-covalent bonding they stabilize graphene’s structure. In literature  covalentOne of the first strategies that was proposed, in order the properties of the functionalized graphene, to be improved; included the dilution of the desired polymer into the graphene oxide solution, before taking place graphite oxide’s reduction reaction process. In that way the resulted polymer-graphene material acquires higher solubility, very good conductivity and offers much larger area that can accommodate much more metal nanoparticles, lacking any aggregation trends. Moreover, the as-resulted pasta can be applied directly to a glassy carbon electrode for further experimental investigation ,86. In the case of glucose electrochemical sensors, the combination of polymer-graphene and metal oxide  increase−1cm−2) and the low detection limit (0.20 μM) were attributed to synergistic effects as well as to the large area that PDDA-graphene offered to metal oxides [l oxides . AnotherThe amount of the polymer that is added is a very crucial key as determines the characteristics of the final polymer/graphene catalyst. An excessive amount of polymer, could obstruct the catalytic sites, leading to current response decrease, while a lower amount could lead to a lower current response than the maximum that the catalyst could give. According to the most recent works, rGO seems to have better behaviour than graphene when it is combined with polymers; as rGO, on the contrary to GO, is more soluble to polymeric solutions . Moreove−1cm−2 sensitivity and 0.79 μM low limit of detection. Despite that the as-prepared electrode did not be examined under real samples, the sensitivity was estimated at around neutral pH values, indicating its good electrocatalytic characteristics even at close real human environmental conditions. Under this background, Deshmukh et al.  exploredThe choice of the appropriate polymer material seems also to be a really very important key parameter that strongly affects a glucose sensor performance. Taking as an example the work of Deshmukh et al. , the N-aPANI has been proved to be a very safe choice for compose polymer-graphene electrodes for enzymeless glucose sensors and so a few research groups ,91,92 inNotably, Bairagi et al.  suggeste−1cm−2) and low detection limit equal to 0.0126 mgdL−1  solution before GO calcination and reduction (in-situ synthesis method). The second one includes the use of melamine, among the polymeric reactants, whose N-atoms (as in case of PANI) increase electron conductivity and the third one is the addition of the Ni-rGO into the polymerization synthesis procedure of the Ni-rGO B. Then a6 mgdL−1 B. Furthe2+ [2+) before the reduction process and the deposition of metal nanoparticles, take place. In that way the GO aggregation is reduced and the metal nanoparticles dispersion on the GO surface is increased. Therefore, the key to further improve GO or rGO dispersion is the increment of the interfacial interactions. Towards this target a few synthesis strategies have been proposed, with the homogeneous dispersion of the polymer into the solution before the GO or rGO formation takes place, to be the most common one. For the GO or rGO production the electrochemical methods seem to be preferred. In aqueous solutions the PAA polymer produces carboxylate anions that can bind with metal cations  bIn summary, the development of polymer-graphene based electrodes is at very primitive stages, and so many synthesis methods in the future will be proposed and explored. Conducting polymers’ morphology and structure offer the possibility for using their functional groups in various synthesis methods. Additionally, some of them have chemical stability along with tunable conductivity and are of low cost, as well.Unfortunately, the dispersion ability of graphene (or its derivatives) along with the appropriate polymer is a key parameter that strongly affects the final properties of the polymer/graphene based electrocatalyst. The polymer introduction into the synthesis method, may improve GO or rGO dispersibility, but could have a negative impact on sensors properties if they are not selected carefully. According to the current research, different synthesis strategies can be applied to ameliorate an enzymeless graphene-based glucose electrochemical sensor’s characteristics. Among them we recognized that the suggested methods could be classified into four main categories: (i) direct growth of graphene (or oxides) on metallic substrates, (ii) in-situ growth of metallic nanoparticles into graphene (or oxides) matrix, (iii) laser-induced graphene electrodes and (iv) polymer functionalized graphene (or oxides) electrodes. GER mechanism is a multi-step electron transferring process requiring a lot of active sites as well as abundant electroactive surface area. Additionally, as we deduced from the GER mechanism analysis, the presence of metal oxidative species is essential for its promotion. Thus, the sensing properties  totally depend on the route of the mass and charge transport between M(II)/M(III) or M(III)/M(IV). Thus, the key-requirement is this route to be of low internal resistance and at the same time to offer the capacity for as many as possible active sites. So, a porous structure and a great number of functional groups, can allow higher mass and charge transfer. In It is characteristic that the highest sensitivity values were exhibited by direct growth of graphene onto metallic substrates. This synthesis method has the great advantage that the electrode can be used directly for the detection of glucose without being necessary a transfer process of it, as it is required for many other electrocatalysts. Avoiding this extra transfer step, a possible destruction of graphene (or its derivatives) lattice as well as its contamination, are evaded. Another important advantage is the lack of any binder during the synthesis procedure. Thus, the internal resistance of the electrode can be significantly lower compared to the other methods and consequently glucose sensor’s sensitivity can be extremely enhanced. However, the metal impurities that may remain from the substrate constitutes one of the main challenges that should be overcome. The use of alternative substrates was suggested by a few research groups. Thus, according to our research, it can be stated that Ni foam is a promising substrate, that it is expected to be extensively examined in the future. In the pursuit of increasing the available specific surface area some research groups suggested the in-situ growth of an element into graphene sheets. The main element that is currently used in this synthesis strategy is the MOFs. Despite that MOFs have large surface area, high porosity and other chemical and physical properties that make them attractive for the electrochemical sensors’ development; at the same time, they present instability, and they cannot be easily controlled in order specific structures, such as hierarchical, to be obtained. This is a great challenge that should be overcome to explore all their possibilities that their special structure offers. The functionalization of them seems to enhance in-situ MOF/graphene electrocatalyst’s activity. While there is a new trend to in-situ fabricate carbon instead of MOF, which also increases the graphene nanosheets specific surface area. On the other hand, laser-induced graphene allows the on-site construction of electrodes onto flexible substrates/platforms. Additionally, the raw materials are of lower cost without requiring any post-treatment steps and it seems to gain significant ground as a synthesis strategy. In case that graphene functionalization is caused by laser tools, the process is accurately controllable, costs much less than other methods using chemical agents. Additionally, this method offers also great reproducibility. However, the technique of metallic nanoparticles addition to LIG structure is still under investigation. One of the main challenges that should be overcome is the anisotropic conductivity of the LIG 3D structure, which dependent on the preparation method that is followed, could affect the preparation time and the uniformity of the metallic nanoparticles or layer onto graphene surface. It should be mentioned that the high price of the laser equipment prohibits this method to be extensively investigated, but we expect much more works to be released.Since the internal resistance should be as much as lower can be, the combination of conducting polymers with graphene is a trustworthy synthesis method. Some of the conducting polymers have chemical stability along with tunable conductivity and are of low cost, as well. However, the kind of polymer material is an important key parameter that strongly affects a glucose sensor performance. In the future more works seem to explore various polymers and their role to the graphene material for increasing glucose electrodes sensing abilities. According to this method rGO is more favourable than graphene, as displays higher dispersing ability to polymers, that leads to better results.Graphene (or derivatives) structure offer conduction channels improving GER kinetics. More precisely graphene (or its derivatives) significantly reduces electrode’s internal resistance enabling GER via the formation of redox pair peaks, corresponding to the M(II)/M(III) or (M(III)/M(IV)) couple formation. As the resistance is low the charge transfer becomes easier and the metal oxide couples that are responsible for GER progress, are created faster. These faster processes lead to higher stability, sensitivity as well as quicker response of glucose electrochemical sensor operation. For faster and continuous metal couples formation and so higher sensitivity four synthesis methods are mainly recognized among literature works: (i) direct growth of graphene (or oxides) on metallic substrates, (ii) in-situ growth of metallic nanoparticles into graphene (or oxides) matrix, (iii) laser-induced graphene electrodes and (iv) polymer functionalized graphene (or oxides) electrodes. Comparatively, the direct growth of graphene onto metallic substrates and the laser induced graphene electrodes synthesis methods, are more mature. Those two methods use fewer chemical reagents and present higher reproducibility. Moreover, the electrode that presented the highest sensitivity to glucose detection was prepared via the direct growth of graphene onto metallic substrate.


The effect of rGO-doping on humidity sensing performance was investigated. Scanning electron microscopy, transmission electron microscopy, X-ray diffraction and Raman spectroscopy were used to characterize the nanostructure, morphology and chemical composition of SnO2/rGO nanocomposites. The SnO2/rGO humidity sensitive film was prepared by electrospinning on a polyimide film modified with gold electrodes. The humidity test results show that different doping ratios of rGO have different effects on humidity sensing properties. Among them, the sensor with 2 wt% rGO-doping has a high sensitivity  within the humidity range as well as the fast response time (80 s) and recover time (4 s). Furthermore, the sensor with 2 wt% rGO-doping remains good flexibility and stability in the case of bending (1000 times). The sensitivity of the 2 wt% rGO-doping sensor at the bending radius (8 mm and 4 mm) is 48,219% and 91,898%, respectively. More importantly, the sensor could reflect different breathing states clearly and track breathing intervals as short as 3 s. The SnO2/rGO flexible humidity sensor with accuracy, flexibility and instantaneity as well as the facile fabrication strategy is conceivable to be applied in the potential application for human health real-time monitoring.The development of a flexible and high-performance humidity sensor is essential to expand its new applications, such as personal health monitoring and early diagnosis. In this work, SnO Nowadays, accurate detection of humidity is important for industrial production and home life ,2,3. Wit2 is a n-type semiconductor material with a wide band gap, which is considered as a promising material for humidity monitoring due to its intrinsic chemical property and low cost [2 in different nanostructures have been studied, such as mesoporous SnO2 nanostructures and SnO2 nanowires [2 humidity sensor under high relative humidity limits its practical application [2 with graphene or its derivatives to prepare nanocomposites for humidity sensing could increase the direct contact region between water molecules and nanostructures and reduce the energy barrier, which effectively improves the humidity sensing performance [2@G-GO composites using electrospinning method and solution evaporation method in succession. Compared with pure SnO2, the SnO2@G-GO was demonstrated better humidity characteristics with high sensitivity, fast response and good stability [2: Fe-graphene composites humidity sensor showed a higher sensitivity than Fe: SnO2 nanoparticles [Among the metal oxides, SnOlow cost ,15. In oanowires ,17. Nevelication . Graphenlication ,20. The formance ,22,23,242-SnO2 nanocomposites on the polyimide (PI) film by a drop cast method to prepare an ultrasensitive humidity sensor. The sensor revealed an unprecedented response up to 3,285,000% at 100 Hz, ultrafast response/recovery behaviors and outstanding repeatability [Traditional humidity sensors are usually prepared on rigid substrates ,26,27 witability . However2/rGO nanocomposites with different doping ratios of rGO were synthesized by a one-step hydrothermal method as sensing materials. The sensitive film of SnO2/rGO was formed on the PI film by an electrospinning method to prepare the humidity sensor. The influence of different rGO-doping ratios on the humidity sensing properties were studied in a range of 11%RH–95%RH at room temperature (20 °C). The sensing mechanism of SnO2/rGO nanocomposite was explored. The prepared sensor shows good performance including high sensitivity, good flexibility and repeatability. In addition, the flexible sensor represents good performance in human respiration monitoring. The prepared humidity sensor has great potential in medical care, wearable devices and other intelligent fields.Here, SnO2/rGO nanocomposites were synthesized by a simple one-step hydrothermal method. The GO aqueous dispersion  and 0.1 g SnCl4·5H2O  were added into 20 mL deionized water. The solution was dispersed by an ultrasonic method for 30 min. The sodium citrate solution  was added to adjust the pH value of the mixed solution. These chemicals are analytically pure in specification. Then, the mixed solution was transferred to an autoclave lined with Teflon and heated in an electric furnace at 180 °C for 12 h. The sample was then centrifuged and washed several times, and finally preserved in the form of a disperse solution. SnO2/rGO with different rGO-doping ratios  were prepared by the same method.SnO2). The Au (30 nm Cr thin layer before 300 nm Au layer sputtering) interdigitated electrode arrays (IDA) were fabricated by magnetron sputtering  combined with MEMS technology on flexible PI sheets. The number of electrode pairs was six. The width of every single electrode is 100 μm and the interfinger gap between the electrodes was 150 μm. Before deposition, the synthesized SnO2/rGO nanocomposites was sonicated to enable it to be properly dispersed on the IDA. Further, the samples were sprayed on PI sheets using electrospinning method . Lastly, the prepared sensor was dried in electrothermal blowing dry box at 60 °C for 1 h. 2/rGO humidity sensor.First, PI sheets (12 mm × 8 mm × 75 μm) were successively cleaned by ultrasonic in acetone, ethanol and deionized water for about 5–10 min. Then, the sheets were blown dry by nitrogen  and a field emission transmission electron microscopy . The Raman spectra were obtained by Raman spectrometer . The structure and crystallite size were analyzed by a powder X-ray diffraction  within the 2θ range of 20° to 80°. In terms of humidity test, specific RH conditions of 11%, 20%, 34%, 56%, 77%, and 95% were obtained in wide mouthed bottles with saturated solutions containing a variety of metal salts including LiCl, CH3COOK, MgCl2, NaBr, KCl, and CuSO4·5H2O. The capacitance of SnO2/rGO humidity sensor under different RH levels was measured with an impedance analyzer  at AC 0.5 V and 100 Hz. All the properties tests were carried out at 20 °C. The humidity sensor sensitivity is defined as S = (Cx − C0)/C0 × 100%, where Cx and C0 represent the capacitance at the specific humidity X%RH and initial humidity 11%RH, respectively. The time required for the capacitance value of the sensor to reach 90% of the final equilibrium value during the absorption and desorption process is defined as the response and recovery time, respectively.The microstructure and morphology of SnO2 are corresponding to the (110), (101), (211) and (301) diffraction of tetragonal cassiterite structure SnO2 (JCPDS 41-1445) [2 was formed. The diffraction peak of rGO is about 25°, which is close to 26° and coincides with the diffraction peak of (110) crystal plane of SnO2, so additional characterization is required for further analysis of rGO.XRD patterns, as shown in 41-1445) , which i−1 and 1580 cm−1. The D band is attributed to the defects and disordered structures in graphene, and the G band is derived from the in-plane vibration of carbon atoms in the sp2 -hybrid orbital. The intensity ratio of D band to G band (ID/IG) stands for the density of defects for the graphene structure [D/IG value of 1 wt% SnO2-rGO is 0.96 which is greater than that of GO (0.87), indicating that GO was reduced to rGO upon the hydrothermal method and the defect structures of the composites increased. The structural defects could provide more active sites for adsorption and desorption of water molecules. In addition, it can be seen that the ID/IG value of 1 wt% SnO2-rGO is the largest and that of 4 wt% SnO2-rGO is the smallest, which also corresponds to the subsequent humidity test results.The Raman spectra were displayed in tructure . As show2/rGO composites reveals the interaction between rGO and SnO2. From the Raman spectra, the G band shift of the SnO2/rGO composites indicated the formation of SnO2/rGO composites rather than individual presence during the process of the hydrothermal synthesis, which is attributed to the charge transfer between rGO and SnO2 [The position of G band for SnOand SnO2 .2/rGO nanocomposites at low magnification. There are some differences in microscopic morphology of samples with different doping ratios. Compared with other samples, the 2 wt% SnO2/rGO film prepared by electrospinning is more uniform, while the 4 wt% SnO2/rGO film is most affected by the electric field and therefore has the worst uniformity. 2/rGO nanocomposites at higher magnification. For 1 wt% and 2 wt% SnO2/rGO, SnO2 nanoparticles are stacked on graphene to form a porous and loose structure, which is more beneficial for effective surface reactions. As the proportion of graphene doping increases, most of the nanoparticles are sparsely dispersed on the multi-layer structure of graphene, especially the micromorphology of 8 wt% SnO2/rGO.2/rGO was achieved by using TEM and HRTEM as shown in 2 nanoparticles were scattered on the rGO nanosheets. Among the four samples with different doping ratios, the distribution of nanoparticles of 4 wt% SnO2/rGO is the most uneven, which is consistent with the results of SEM images in 2 nanoparticles were around 8 nm. Furthermore, it could be calculated that the d spacings of the nanoparticles is 0.34 nm and 0.26 nm from 2 [Structural analysis with more details of SnOm from 2 .2/rGO humidity sensor under different RH levels is measured by a LCR analyzer. The parallel capacitance equivalent circuit of digital bridge is used to measure the capacitance by constant voltage mode.The method of saturated salt solution is used to build the different humidity environments . The humThe influence of operating frequency on humidity response characteristics were investigated. The sensor was transferred from low humidity environment to high humidity environment (11%–20%–34%–56%–77%–95%) successively and the capacitance response values were measured at 100 Hz, 1 KHz, 10 KHz, 100 KHz and 200 KHz, respectively. The capacitance decreased with increasing frequency. The results showed that the capacitance change of the sensor at 100 Hz is significantly better than that at other working frequencies. Especially, there is a sudden increase of the capacitance in high humidity. Therefore, 100 Hz is chosen as the operating frequency of performance test.2/rGO sensor is found to be about 24,866 pF at 95%RH, which is about 616 times of that (40.36 pF) at 11%RH. For the purpose of comparing the humidity sensing property of four samples, the sensitivity is calculated as shown in 2/rGO humidity sensors increases sharply by more than three orders of magnitude from 11%RH to 95%RH, indicating that water molecules have a great effect on the capacitance of SnO2/rGO nanocomposites. Especially, 1 wt% SnO2/rGO sensor corresponds to a higher sensitivity  to water molecules than other samples due to the honeycomb-like structure on the film. The honeycomb-like structure with large pore size increases the specific surface area of the sample, which provide more available active sites for the adsorption of water molecules.The capacitance value curves of four samples with relative humidity at 20 °C are shown in 2/rGO humidity sensor during adsorption and desorption, respectively. Within the whole humidity range, the difference of capacitance values between adsorption and desorption process varies slightly at medium and high RH as shown in 2/rGO sensor has the highest sensitivity, the consistency of its adsorption and desorption curves is the lowest, and the capacitance value varies greatly in high RH. By comparison, the adsorption and desorption curves of other samples  are closer to each other.Different types of sensitive materials and preparation methods will lead to the different respond speeds of humidity sensors to water molecules during the process of absorption and desorption, resulting in a certain response hysteresis. Typically, the hysteresis characteristic is reflected by the difference in response values during water molecule adsorption and desorption. The sensitive film prepared by electrospinning was transferred from low humidity environment to high humidity environment (11%–20%–34%–56%–77%–95%) and then back to low humidity environment (95%–77%–56%–34%–20%–11%) successively. The red and black curves in The testing of the response–recovery characteristics of the sensor was performed. The dynamic response–recovery characteristics curves of the sensors for the different rGO doping ratios in a test circle (11%RH–95%RH–11%RH) are shown in 2/rGO sensor are approximately twice the response (80 s) and recovery time (4 s) of 2 wt% SnO2/rGO sensor. As observed in 2/rGO sensor is the lowest, the response time is not shortened correspondingly and still reaches 120 s. 2/rGO sensor is the longest, reaching 280 s. 2/rGO humidity sensor. The changes of capacitance response value were recorded by exposing the sensor to 11%RH and 95%RH repeatedly. Under the same condition, the capacitance changes in five repeated cycles are almost negligible, indicating that SnO2/rGO humidity sensor exhibits good repeatability and practical value.2/rGO film surface was shown in 2 and rGO and undergo chemical dissociation to release protons to participate in the conduction process. Since the number of protons is limited and the transfer of protons requires more energy, it results in poor conductivity and small capacitance response. When the adsorption of water molecules starts to increase, more protons are dissociated and combine with other water molecules to form H3O+ (H2O + H+→H3O+). As the relative humidity further increases, lots of water molecules gather to form a continuous water film on the humidity-sensitive film, leading to the enhancement of Grotthuss chain reaction (H2O + H3O+→H3O+ + H2O). This process greatly increases the dielectric constant of the sensitive film, which results in a sharp increase in the capacitance value [The process of water molecules adsorption on SnOce value .2 makes contribution to the rapid transmission of water molecules, which shortens the response/recovery time. Compared to pristine GO, the surface of SnO2/rGO nanocomposites possesses a larger specific surface area, which could provide more available active sites for water molecules and accelerate the processes of adsorption and desorption. Thus, the ability of humidity sensing performance is enhanced greatly by the combination of rGO and SnO2. It is worth noting that the higher proportion of graphene doping does not mean the better sensing performance. According to the characterization and test results, 2 wt% SnO2/rGO shows better humidity sensitivity, which may be attributed to the more uniform distribution of SnO2 nanoparticles on rGO layers and the optimal synergetic effect produced by SnO2 and rGO.The nanostructure of SnOThe flexible humidity sensor is required good conformal feature, which can effectively prevent the cracking of humidity sensitive materials from the substrate and the damage to the sensing structure. Therefore, the bending test of flexible humidity sensor is of great significance to its practical application.2/rGO sensor on tubes with different radius. 2/rGO film increases during the bending process, which allows the water molecules penetrate more deeply into the sensitive material. The bending radius of 4 mm is chosen as the typical extreme bending condition in practical applications. Therefore, the repeatability test was performed under the condition of a bending radius of 4 mm. 2/rGO humidity sensor. Bending test results suggest that the prepared sensor has practical potential in wearable devices and applications related to human health.In order to explore the bending effect on the performance of the sensor, the bending test was carried out by attaching 2 wt% SnO2/rGO humidity sensor in 11–95%RH humidity environment, it has a good performance in the human breathing test. The 2 wt% SnO2/rGO humidity sensor was selected as the test sensor because of its high sensitivity, fast response/recovery speed. As shown in Generally speaking, the air humidity exhaled by the human nose and mouth is much higher than 11%RH. That is to say, there is no need to wait for the complete absorption and desorption of water molecules when the humidity sensor is actually applied in breathing monitoring . Therefo2/rGO humidity sensor for real-time human breathing monitoring.2/rGO nanocomposites was fabricated. The methods of XRD, SEM, TEM and Raman spectroscopy were used to characterize the morphology and composition of the SnO2/rGO nanocomposites prepared by hydrothermal method. Electrospinning method is used to spray SnO2/rGO sensitive film on PI film for improving the adhesion. More importantly, the nanoparticles could be uniformly dispersed on the film and increase the active sites for water molecules. Humidity test results demonstrated that the SnO2/rGO sensor with different doping ratios of rGO show different humidity sensing behaviors. Although 1 wt% SnO2/rGO has the highest sensitivity to water molecules, the response/recovery characteristic as well as hysteresis characteristic of 2 wt% SnO2/rGO humidity sensor is better. Besides, the sensor exhibits a good response to humidity at different bending radius and the performance remains stable after bending 1000 times. Results of human respiration show that the 2 wt% SnO2/rGO humidity sensor could clearly reflect the different breathing states of different adults, making it more suitable for human respiratory monitoring. These results pave a new path to realize SnO2/rGO flexible humidity sensor in the application for personal real-time health monitoring as well as other health applications.In summary, a high-sensitive and flexible humidity sensor based on SnO


Duchenne Muscular Dystrophy (DMD) is a fatal X-linked disorder with a birth prevalence of 19.8:100,000 males worldwide. Elevated concentration of the muscle enzyme creatine kinase-MM (CK-MM) allows for presymptomatic screening of newborns using Dried Blood Spots (DBS). We evaluated imprecision and carryover of the FDA-approved PerkinElmer GSP Neonatal CK-MM kit over multiple runs, days, and operators, followed by quantification of CK-MM loss in stored newborn, contrived, and non-newborn patient DBS resulting from exposure to ambient versus low humidity , and high humidity and high temperature . Imprecision %CV was ≤14% for all verification comparisons and over 6 months of testing. On average, the mean CK-MM recovery after 50 days was >80% of initial concentration for all sample types stored in low humidity and <80% in ambient humidity. After 8 days of storage in high humidity and high temperature, the mean recovery for newborn samples was <80%. Verification results for the GSP Neonatal CK-MM assay were concordant with kit parameters and the assay performed consistently over 6 months. CK-MM degradation in ambient storage can be mitigated by reducing exposure to humidity. Assessment of DBS shipping and storage conditions is recommended prior to implementing DMD screening.  Duchenne Muscular Dystrophy (DMD) is an X-linked degenerative neuromuscular disorder causing severe progressive muscle loss and premature death by the mid-30s . It is cDMD symptoms usually manifest between the ages of 2 and 3 with mean age of diagnosis at 4 years (± 2.3 years) . A majorThe creatine kinase (CK) enzyme leaks from muscle cells into the bloodstream after muscle damage, including damage caused by muscular dystrophies. Several newborn screening (NBS) pilot studies have used quantitative detection of CK in DBS to identify newbrons with DMD ; howeverSample storage conditions affect the stability of some biomarkers used in NBS ; therefoTesting was performed on one Genetic Screening Processor (GSP) #2021-0010) using the FDA-approved GSP Neonatal CK-MM kit , as previously described ,20. 21-0010 un = 500 and n = 30, respectively). Only samples within the kit specifications  were included. NB samples were transported from birthing hospitals to NCSLPH, located in Raleigh, NC, in mailing envelopes or boxes without desiccant or cold packs. Once received, specimens were processed for routine NBS and stored in airtight bins with desiccant at ambient temperature until use. At study commencement, NB specimens were no more than 10 days old from date of collection. Muscular Dystrophy (MD) DBS were created from liquid venous blood (EDTA) of 15 non-newborn deidentified donors, aged 9 to 25 years, with neuromuscular disorders known to be associated with elevated CK-MM from Duke Children’s Neuromuscular Clinic. Lab-Contrived (LC) DBS included prototype specimens from the Centers for Disease Control and Prevention (CDC) (n = 5) (manuscript in preparation), and DBS prepared in our lab (n =11) using whole blood  with CK-MM ) serially diluted, then applied to filter paper. Quality Control (QC) materials included kit calibrators and kit controls  , blank Whatman 903 filter paper, an LC normal control (QC4), and a CDC prototype DBS, implemented as a positive control in the stability studies (QC5). QC4 DBS were prepared using venous blood (EDTA) from a healthy individual ). DBS cards were prepared in a biosafety hood, as described previously .In the 50-day study, after initial testing at timepoint 0, MD and LC samples were stored in low or ambient humidity and tested every 10 days for 50 days. Because of the limited amount of each NB sample, the 500 NB samples used in the trial were tested at timepoint 0, then split into four groups, stored in low-humidity conditions, and tested in a staggered design spanning the duration of the 50-day time course, such that each NB sample was tested at three timepoints and 200 samples were tested per timepoint. Group 1  were used to model the population-averaged mean proportion of initial concentration, with storage condition , time (in days), sample type , and initial concentration as main effects. A separate model was fit for the interaction of time with each of storage condition, sample type, and initial concentration. A Gaussian distribution with a log link function was used, along with an unstructured correlation structure. Statistical significance was evaluated using the robust Z-score. Estimation was performed using the gee package.The mean, standard deviation, and Coefficient of Variation (CV) for the QC materials were calculated using the SpecimenGate QC module (PerkinElmer) and Excel. Statistical analyses for the stability study were conducted with R version 4.0.4. The Shapiro–Wilks test of normality, from the n = 2) and 6-month evaluation (n = 82) (carryover concentration range 0–7 ng/mL). Within-run, plate-to-plate, and inter-operator precision comparisons were ≤14% CV and were concordant with the kit parameters  PerkinE as a parwww.timeanddate.com, accessed on 11 January 2022). Over the 50-day stability trial, the mean difference between the two humidity storage conditions was 27.2% and the storage temperatures remained within 2.3 °C for both humidity conditions. In low-humidity storage, the mean temperature was 18.7 °C (range 17.7–19.4 °C), and the mean humidity was 16.5% (range 12–23%). In ambient humidity storage, the mean temperature was 19.4 °C (range 18.5–20.0 °C) and the mean humidity was 43.7% (range 30–55%).The 500 samples used in the 50-day study had been transported from collection sites to the NCSLPH in February, where the mean temperature and humidity in Raleigh were 5.6 °C and 76%, respectively ; thus, the initial CK-MM concentration was balanced among the groups. The 200 NB specimens tested at each timepoint are representative of the whole 500 NB specimen population. The NB DBS in each group were tested at three timepoints and 200 DBS from one or more groups were tested at each timepoint B. No sign = 84), indicating that the risk of significant CK-MM degradation over time remains even if humidity is removed with desiccant. The combined proportions of samples with <80% CK-MM recovery after 50 days were 33.2% in low humidity  and 70.4% in ambient humidity (MD and LC)  .GEEs were used to investigate whether CK-MM degradation over time was related to the storage conditions, initial concentration, or sample types . The obswww.timeanddate.com accessed on 11 January 2022) In the 8-day trial, the mean storage temperature was 29.1 °C (range 23.4–34.9 °C) and the mean humidity was 69.0% (range 55.1–82.8%). In only 8 days at these conditions, CK-MM degradation occurred to an extent not seen until much later in the 50-day trial. After 8 days in high temperature and humidity, the mean percent recovery was 79.4 ± 1.8% NB, 76.4 ± 2.6% MD, and 84.7 ± 3.4% LC (n = 22/30) NB, 66.7% (n = 10/15) MD, and 60.0% (n = 9/15) LC. A high-humidity, high-temperature trial was conducted over 8 days in mid-August in central North Carolina using 30 NB, 15 MD, and 15 LC DBS to simulate conditions that samples might be exposed to during transportation . The 30  3.4% LC . At day Several DMD screening pilot studies worldwide have used quantitative detection of CK in DBS to identify DMD cases presymptomatically . More reAs for many other analytes and as reported previously for CK-MM-based screening for DMD ,20, undeThis stability study builds on the 34-day study by Moat et al. that evaluated four levels of contrived DBS samples at four temperatures  and three humidity levels  . In our Substantial loss of analyte due to heat and humidity occurs for several other analytes already tested by various NBS programs. Summarizing results from 30 ± 5-day stability trials, Adam et al. reported >20% analyte loss for 27 and >90% analyte loss for 7 of the 34 studied markers of inborn errors . The losLimitations: Due to limited specimen availability, NB specimens were not evaluated at storage in ambient temperature and ambient humidity; however, the MD and LC comparisons suggest that the difference in CK-MM degradation in NB specimens stored at the two humidity conditions would be similar to the non-NB samples. Only one GSP instrument was used in the study; thus, multi-instrument variability was not assessed. While the manufacturer indicates that instrument-to-instrument variation is well controlled [PerkinElmer GSP Neonatal Creatine Kinase -MM kit 3311-001U instructions for use version 1] instrument comparison should be included as a part of assay verification by programs that use multiple units. NB specimens were not tested at timepoint 0 due to shipping from birthing hospitals and the need to perform routine state NBS first. The study results suggest that some degradation likely occurred during transport, particularly for specimens collected in the summer. Upon arrival to NCSLPH, all specimens are kept in sealed boxes with desiccant; thus, degradation during short-term storage was likely minimal.We show that the performance of the GSP-based CK-MM assay is similar to other NBS tests and, like some other NBS analytes, degradation of CK-MM is a concern for NBS for DMD, particularly in locations with seasonal high heat and humidity. Thus, evaluation of program-specific DBS storage and shipping conditions—namely humidity, temperature, and storage/shipping time—and implementation of appropriate methods to mitigate the risk for false-negative results due to CK-MM degradation prior to implementing NBS for DMD are advised. If CK-MM degradation is substantial, separate cutoffs may be necessary for seasonal effects or delayed testing.