Behavioral and neural studies of selective attention have consistently demonstrated that explicit attentional cues to particular perceptual features profoundly alter perception and performance. The statistics of the sensory environment can also provide cues about what perceptual features to expect, but the extent to which these more implicit contextual cues impact perception and performance, as well as their relationship to explicit attentional cues, is not well understood. In this study, the explicit cues, or attentional prior probabilities, and the implicit cues, or contextual prior probabilities, associated with different acoustic frequencies in a detection task were simultaneously manipulated. Both attentional and contextual priors had similarly large but independent impacts on sound detectability, with evidence that listeners tracked and used contextual priors for a variety of sound classes . Further analyses showed that listeners updated their contextual priors rapidly and optimally, given the changing acoustic frequency statistics inherent in the paradigm. A Bayesian Observer model accounted for both attentional and contextual adaptations found with listeners. These results bolster the interpretation of perception as Bayesian inference, and suggest that some effects attributed to selective attention may be a special case of contextual prior integration along a feature axis. It is an active process that is very sensitive to numerous factors, including attentional state and expectations. For example, explicit attentional cues can rapidly enhance perception of expected events  and will bias perception. We tested this prediction by way of a well-studied effect of auditory selective attention, the When listeners are asked to detect the presence of a tone (any tone) in noise, a high explicit attentional prior placed on a particular tone frequency via cueing causes an attentional band of perceptual sensitivity around that frequency. This means that tone detection rates fall off as a function of frequency distance from the expected tone(s) –23. SimiA second prediction of a Bayesian model of auditory scene analysis is that listeners collect and use contextual priors optimally. In a dynamic environment where context changes and the prior probabilities associated with different tone frequencies vary over time, a Bayesian model predicts that past evidence will be weighted in such a way as to provide the best estimates of the future. This prediction was tested by analyzing tone detection rates as a function of prior probability as computed over different windows sizes and decay parameters to determine how contextual priors are integrated over time.The final prediction of a Bayesian model of auditory scene analysis tested here is that listeners use explicit attentional cues and implicit contextual cues simultaneously and independently. Both attentional and contextual cues have been shown to bias perception, but (as will be demonstrated) Bayesian observer models predict contextual priors have the same effect on detection sensitivity whether there is an effective attentional prior, an ineffective attentional prior, or no attentional prior at all. This prediction was tested by explicitly cuing listeners to expect that a 250Hz frequency would regularly  occur, forming a simple and explicit attentional prior during the prior probability manipulation.likely to appear in that trial, with corresponding examples and practice. The specific instructions given were: “The cue sound will often be the same sound that appears in the trial. Occasionally the cue sound will not be the same sound that appears in the trial”. The cue, two intervals, and an opportunity for response were indicated by text appearing sequentially on a monitor at the appropriate times.The procedure closely followed the probe-signal method used by Dai and colleagues . Each truninformative cue version in which the detection tones were as described but an uninformative cue was presented at 777 Hz and related cue instructions were omitted, (2) the standard informative cue version for additional probe distances of 1.75 ERB (350 Hz) and 3.75 ERB (487 Hz) to more fully fit the attentional band.Cue sounds were presented at clearly audible levels, and the sound presented for detection was attenuated at its threshold level in each trial and with equal probability in the two intervals (550 ms or 1200 ms after cue offset). One of four different detection sounds were presented in each trial: a sound matching the cue in pitch, or a probe sound differing from the cue by one of three distances along the frequency axis. For all three stimulus classes tested , the cued pitch was 250 Hz and probe pitches were .75 Equal Rectangular Bandwidths  was presented in the remaining proportion of trials  in a given condition. Each condition was made up of blocks of 75 trials each, and prior probabilities were calculated over this length. In this way, each condition contained two detection sounds: one with a pitch of 250 Hz presented with some probability p and another with a different pitch presented with a probability of 1 –p  “Did you notice anything in particular about how often the different sounds were played? If so, what did you notice?” (2) “Did you use any particular strategy to detect the sound? If so, can you describe your strategy?” No listener responded in any way consistent with conscious awareness of the probability manipulation or any deliberate detection strategy related to tone probability.All stimuli had a duration of 300ms, a sampling rate of 16 kHz, began and ended at zero crossings, and were presented with 25ms cosine rise and fall times. Simple tones (single sinusoids) and harmonic spectra  were generated in MATLAB and vowels (/a:/ as in father) were generated with the Klatt Synthesizer. While the same formant parameters were used to generate all vowels , differing F0s resulted in slight variations in formants (on the order of 30 mels). Low-pass filtered white noise  was presented at a level of approximately 60 dB SPL (participants were permitted to adjust noise volume) continuously throughout threshold and target-probe procedures. Participants listened through Sennheiser HD595 headphones while seated in an ETS-Lindgren soundproof chamber and stimulus delivery and response collection was controlled from a Windows computer (ASUS Xonar Essence STX sound card) with custom software written in MATLAB.Seventy-seven naïve volunteers  self-identified as having no hearing difficulties successfully completed testing . The experiments were conducted in accordance with the declaration of Helsinki and after approval by the Johns Hopkins University Homewood institutional review board. All subjects gave written informed consent to participate in the study and were compensated $20 for participation in threshold and probe-signal procedures lasting two hours. Volunteers were assigned to one of several identical probe-signal tasks that used matched stimuli of different acoustic classes as summarized in s1 and s2 to an array of N tonotopically-organized neurons. Both s1 and s2 were N×1 vectors representing N possible frequency channels whose activity is randomly drawn from a standard uniform distribution. One of the two stimuli had an additional activation at channel k representing a tone embedded in noise background. Here, N was arbitrarily set to 100 and k set to 30 for target frequency and 40,55,70,80,95 for probe frequencies. The tone amplitude was set at signal to noise ratio -11.5dB. Both stimuli were processed through N neurons; with Gaussian fi(s) = N tuning curves . Variations of the number of channels and shapes of tuning curves (within reason) did not quantitatively change the function of the model.The model replicated the two-interval forced choice detection paradigm by presenting two stimuli sj, neuron i fired ri spikes following a Poisson process with mean fi(s). An estimated neural log likelihood function was obtained as rifi(s); then integrated across all N neurons to yield an estimate of the stimulus log likelihood π(s) by summing the log of both quantities. We also explored alternative implementations for integrating priors with neural responses; by multiplying the tuning curve of each neuron with a scaled version of the prior distribution π(s). This case would mimic changes to the tuning properties of the neural array as a result of contextual prior accumulation. Computationally, both implementations were equivalent and yielded comparable estimates of the posterior probability Θ(s|r).For each stimulus tions in ,28. The s1,s2} with highest mode (highest maximum a posteriori estimator) was chosen as the interval with correct target or probe tone. For the informative cue case, a weighted posterior mean estimator with a bell shaped weight function W(s) was centered on the location of the cue, and modeled as N. Again, the interval with the highest peak was flagged. For both informative and uninformative conditions, the location of the detected tone was used to update the distribution of priors . The update function used a temporal decay term (α = 0.12) such that For the uninformative cue condition, the interval {2p) values. All error bars displayed are 1 standard error of the mean calculated for repeated measures , further suggesting that contextual priors condition sensitivity independently from attentional priors.Prior-driven adaptation in the absence of cued attention. If attentional and contextual prior probabilities are genuinely independent factors, the effects of probe probability reported above should be closely replicated even in the absence of an attentional manipulation. To test this claim, the procedure was replicated with an uninformative cue (at 777 Hz). The uninformative cue replaced the informative cue in all trials, and instructions made no mention of using the cue to guide attention, effectively eliminating any attentional prior . In the absence of cued attention, there was a clear effect of contextual priors on detection accuracy   and vowels (Group 6), with target and probe pitches for these stimuli matched to the tone frequencies reported above. Significant main effects of probe prior were found for both classes of stimuli [harmonic: Fp = .56] . No attew) that yielded, on average, the most accurate (best correlated) estimates of the underlying probe priors used to generate trials in the informative cue tone paradigm. Using the actual trial sequences participants experienced, probe prior estimates were made for each trial, for each participant, and for each w between 5 and 150 preceding trials.Listeners collect priors near-optimally. Prior probabilities can be time-invariant, such that new information is integrated with the entire unweighted historical record. Alternatively, they can be time-dependent, such that prior distributions are either estimated for a discrete and limited historical window, or weighted to place more importance on recent experiences. A weighting of the past would be optimal if it gives the most predictive estimate of the future. Here, an optimal integration window size was defined as the number of preceding trials , to rectify for correctly scored guesses. In this way, prior estimates were updated only on a corrected proportion of correctly scored detections. In addition, since probe priors were inversely proportional to target priors (i.e. blocks with more probes had fewer targets), identical analyses were performed using targets to calculate prior probability. The model that computed priors based on only the truly detected probe trials (i.e. rectified correct probes) had the best overall agreement with the window size actually used by listeners , where t is a time point or trial, and f is a tone frequency: fπ = 1. Similar to the integration window results reported above, the optimal and functional α values were very similar: 0.132 and 0.122, respectively  to estimate probe priors and determine the optimal and functional decay weights. The coefficient α refers to the weight of the most recent trial relative to the historical prior distribution, such that smaller values closer to 0 correspond to slower decay rates (long memory) and higher values closer to 1 correspond to higher decay rates (short memory). This coefficient is used in computing prior probability, ectively .Because the discrete window-based and continuous decay-based models yielded almost identical best fits, these results cannot endorse one of these representations of priors over the other; though both models agree that the average listeners appeared to be collecting a distribution of priors optimally over the course of this experiment. Furthermore, optimal window sizes and temporal decay coefficients were not manipulated, and so whether the functional window sizes or decay rates used by participants would track different optimums, and how robust these results are to different presentation paradigms, cannot be determined from this data.s, an array of N tonotopically-organized non-deterministic neurons produce ri,{i = 1, …, N} spikes over the duration of the stimulus. The firing rate of each neuron i follows Poisson statistics and reflects the match between the input and the neuron’s tuning curve fi(s). The ability of the perceptual system to detect a target in the stimulus depends on the fidelity of its sensory mapping; i.e. its ability to give the best estimate of the stimulus from a readout of an inherently noisy neural population. An estimate of the likelihood of stimulus s based on the population response of N neurons is modeled as a weighted integration of responses of individual neurons into a single observation, or likelihood function Lr(s). This function, or its equivalent log likelihood, was previously shown to yield an optimal decoding of sensory information given noisy measurements [ri weighted by its corresponding tuning function fi(s).A Bayesian Observer model accounts for attentional and perceptual adaptation. The model tests the intuition that the behavioral results reported here can be captured by an optimal decision rule based on a Bayesian regime. The model estimates the detectability of a tone embedded in a noise background . Given surements ,28,30; aπ(s) to yield a posterior estimate of the probability of s given the observed neural response:Θ(s|r) ∝ Lr(s)π(s).In a frequentist non-Bayesian setting, this function gives an estimate of likely stimuli that could have given rise to the observed population response. Its peak (i.e. location with highest likelihood) corresponds to the expected position of the target to be detected  ,32. It aOnce a posterior is estimated, a perceptual decision uses a selection rule to determine the presence of a target note embedded in the noise. In case of complete uncertainty (i.e. uninformative cue), the decision stage relies on a uniform decision function. Here, we use a standard 0–1 loss function  of a probe tone is proportional to its frequency distance from the expected or target tone . Additioperceptual bands. Perceptual bands—perceptual attenuation proportional to the distance from relatively expected feature levels—can be understood as a simple consequence of the kind of attentional or contextual prior integration along a feature axis  described in this report and others [Alternatively, the attentional band in auditory frequency listening could be characterized as a special case of modality-general d others ,45,46, aTo illustrate this concept, consider a simple attentional prior represented as some bell-shaped distribution along the frequency axis. According to the distribution, prior probability falls off proportional to the distance from the most likely frequency. Integrating this distribution with incoming sensory information results in a bell-shaped perceptual band of tone frequency sensitivities. Certainly prior distributions will often be more complex than this simple attentional prior, but perceptual bands can form at any local maximum in the prior distribution through a variety of mechanisms. For example, high priors associated with particular levels of a feature can act as perceptual magnets or attractors in feature estimation , producior priors they are naïve to, it may not be necessary to appeal to selective attention  to explain these results. What’s more, it may be necessary to control for the effects of implicit contextual priors when interpreting studies of selective attention.Others have interpreted perceptual bands as evidence for explicit attention, but as suggested here, perceptual bands may occur without observer awareness. In their study of priors associated with direction of motion, Chalk and colleagues  reportedThe effect of priors on perceptual sensitivity likely extends beyond relatively simple perceptual bands. Although we used prior probability distributions limited to a single perceptual dimension, studies of selective attention suggest that auditory perception is sensitive to multimodal and joint prior distributions. Perceptual bands have been observed when priors are spread across four different tone frequencies , suggestWhile there are many indications that complex, multidimensional prior distributions can be represented and used to improve perception, several issues must be addressed before this work can be extended to perception of real-world or natural scenes. First, the vast majority of related studies have measured prior-based perceptual adaptation only at near-threshold levels. As a result, the degree to which priors influence the speed and accuracy of perception at more typical levels is unknown. Second, to fully exploit the statistical structure in the environment, encoded prior distributions should incorporate contextual information . Context-specific statistical learning has been widely demonstrated ,55, but This research was supported by National Science Foundation grant IIS-0846112, National Institutes of Health grants 1R01AG036424 and T32 DC 23–27, and Office of Naval Research grants N00014-12-1-0740 and N000141010278. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors thank research assistants Katherine Simeon and Nicha Apichitsopa for their valuable suggestions and contributions to data collection.


L1-norm and their results are not good enough for sparsity and interpretation and are asymptotically biased, especially in genomic research. Recently, we gained a large amount of molecular interaction information about the disease-related biological processes and gathered them through various databases, which focused on many aspects of biological systems. In this paper, we use an enhanced L1/2 penalized solver to penalize network-constrained logistic regression model called an enhanced L1/2 net, where the predictors are based on gene-expression data with biologic network knowledge. Extensive simulation studies showed that our proposed approach outperforms L1 regularization, the old L1/2 penalized solver, and the Elastic net approaches in terms of classification accuracy and stability. Furthermore, we applied our method for lung cancer data analysis and found that our method achieves higher predictive accuracy than L1 regularization, the old L1/2 penalized solver, and the Elastic net approaches, while fewer but informative biomarkers and pathways are selected.Identifying biomarker and signaling pathway is a critical step in genomic studies, in which the regularization method is a widely used feature extraction approach. However, most of the regularizers are based on  Identifying molecular biomarker or signaling pathway involved in a phenotype is a particularly important problem in genomic studies. Logistic regression is a powerful discriminating method and has an explicit statistical interpretation which can obtain probabilities of classification regarding the class label information.L1-norm type regularization methods. However, L1 regularization may yield inconsistent selections when applied to variable selection in some situations [L1 regularization has a gap to meet these requirements. Thus, a further improvement of regularization is urgently required. Lq (0 < q < 1) regularization can assuredly generate more sparse and precise solutions than L1 regularization. Moreover, L1/2 penalty can be taken as a representative of Lq (0 < q < 1) penalty and has demonstrated many attractive properties which do not appear in some L1 regularization approaches, such as unbiasedness, sparsity, and oracle properties [A key challenge in identifying diagnosis or prognosis biomarkers using the logistic regression model is that the number of observations is much smaller than the size of measured biomarkers in most of the genomic studies. Such limitation causes instability in the algorithms used to select gene marker. Regularization methods have been widely used in order to deal with this problem of high dimensionality. For example, Shevade and Keerthi proposed the sparse logistic regression based on the Lasso regularization , 2. Meietuations  and oftetuations . In manyoperties –8.So far, we observed dense molecular interaction information about the disease-related biological processes and gathered it through databases focused on many aspects of biological systems. For example, BioGRID records collected various biological interactions from more than 43,468 publications . These rL1/2 penalty following the framework established by [L1/2  regularization, which is more efficient than the old half thresholding approach in the literature [Inspired by the aforementioned methods and ideas, here, we define a network-constrained logistic regression model with ished by , where tterature , 15, 16.L1/2 regularization. In L1/2 regularization and the corresponding coordinate descent algorithm. In The rest of the paper is organized as follows. In D has n samples, D = {, ,…, }, where Xi =  is ith sample with p genes and yi is the corresponding variable that takes a value of 0 or 1. Define a classifier f(x) = ex/(1 + ex) and the logistic regression is defined asβ =  are the coefficients to be estimated. We can obtain β by minimizing the log-likelihood function of the logistic regression. Following [G = , where V is the set of genes that meet p explanatory variables and E is the set of edges. If gene u and gene v are connected, then there is an edge between gene u and gene v, which is denoted by Euv = 1; else Euv = 0. wuv denotes the weight of edge Euv. The normalized Laplacian matrix L for G is defined bydu and dv are the degrees of genes u and v, respectively. The degrees of gene u (or v) describe the number of the edges that connected with u (or v). For λ ≥ 0, the network-constrained logistic regression model is presented asβ on the graph.Generally, assuming that dataset ollowing , to combsented asLλ,β=−∑i=p ≫ the sample size n. Therefore, the regularization approach is vitally needed. When adding a regularization term to  which has the regularization term P(β) = ∑j=1p|βj| to penalize (L1) regularization is not good enough for interpretation, especially in genomic research. Besides this, L1 regularization is asymptotically biased [L1 regularization to Lq penalties. In mathematics, Lq  (0 < q < 1) type regularization |β|q = ∑|β|q with the lower value of q would lead to better solutions with more sparsity and gives asymptotically unbiased estimates [L1/2 penalty can be taken as a representative of Lq  (0 < q < 1) penalty and has permitted an analytically expressive thresholding representation [L1/2 net approach based on L1/2 regularization to penalize the network-constrained logistic regression model, as shown in β|1/2 = ∑j=1p|βj|1/2.Directly computing  performs term to , the spag et al. , the autterm to , to partially optimize the target function with respect to βj, and fix the remaining elements βk at their most recently updated values. The specific form of updating β depends on the thresholding operator of the penalty.gorithms  for solvgorithms , MCP [21gorithms . Since tL1/2 thresholding operator for the coordinate descent algorithm:φλ(ω) = arccos((λ/8)(|ω|/3)−3/2), π = 3.14, βj.In this paper, we present an enhanced Remark. This enhanced L1/2 thresholding operator L1/2 thresholding (3/4)(λ)2/3 introduced in [λ. Based on this enhanced L1/2 thresholding operator, when λ is chosen by some efficient strategies for the parameter tuning, such as cross validation, the convergence of algorithm  ← 0  and y∗, X∗, and set m ← 0, γ chosen by cross validation.Step 2. Calculate Z(m) and W(m) and approximate the loss function (β(m).function  based onStep 3. Update each βj(m) and cycle over j = 1,…, p, until βj(m) does not change.Step 3.1. Compute Zij)((m) ← ∑i=1nWi(m)(Zi(m) − ∑k≠jxik∗βk(m)) and Step 3.2. Update βj(m) ← Enhanced_Half(ωj(m), γ).Step 4. Let m ← m + 1, β(m + 1) ← β(m).β(m) dose not converge, then repeat Steps 2 and 3.If L1 regularization (L1 net), L1/2 regularization with old thresholding value (3/4)(λ)2/3 (L1/2 net) and with the enhanced thresholding value L1/2 net), and the Elastic net regularization approach (Elastic net). We first simulated the graph structure to mimic gene regulatory network: assuming that the graph consists of 200 independent transcription factors (TFs) and each TF regulates 10 unlike genes, so there are a total of 2200 variables, X = , p = 2200. The training and the independent test data sets include the sample sizes of 100, respectively. Each TF xn and its regulated genes xm were generated by the normal distribution N. We set the correlation rate between xn and its regulated gene xm as 0.75, xm = (1 − 0.75) × xm + (0.75) × xn. The binary responder yi (1 ≤ i ≤ 100), which is associated with the matrix X of TFs and their regulated genes, is calculated based on the following formula and rule:ε ~ N.We evaluate the performance of four methods: the network-constrained logistic regression models with Model 2 was defined similar to Model 1, except that we considered the case when the TF can have positive and negative effects on its regulated genes at the same time: L1/2 net, L1/2 net, and L1 net. Both penalized parameters for L1 and ridge regularization in the Elastic net were tuned by the 10-fold cross validation on the two-dimensional parameter surfaces. We repeated the simulations over 100 times and then computed the misclassification error, the sensitivity, and the specificity averagely for each net model on the test datasets.In these two models, the 10-fold cross validation approach was conducted on the training datasets to tune the regularization parameters of the enhanced L1/2 net model achieved the smallest misclassification errors in Models 1 (9.22%) and 2 (10.76%) compared with the other regularization methods including the old L1/2 thresholding method (9.85% for Model 1 and 10.83% for Model 2), L1 net (11.81% for Model 1 and 13.21% for Model 2), and the Elastic net (13.12% for Model 1 and 14.14% for Model 2). Meanwhile, the enhanced L1/2 net resulted in the highest sensitivity in Model 1 (98.5%) compared with the other methods. Moreover, the enhanced L1/2 net obtained the best specificity in Model 2 (98.7%) amongst the other approaches. To sum up, the enhanced L1/2 net outperforms the other three algorithms in terms of prediction accuracy, sensitivity, and specificity.http://thebiogrid.org/) with a lung cancer (LC) gene-expression dataset [L1/2 net method. The gene-expression dataset contains the expression profiles of 22284 genes for 107 patients, in which 58 had lung cancer. To test the generalization ability of the proposed method, we divided the dataset into the training set  which covered 2/3 samples of the dataset and the test set  which covered the other 1/3 specimens of the dataset. The 10-fold cross validation approach was conducted on the training dataset to tune the regularization parameters. By combining the gene-expression data with the PPI network, the final PPI network includes 8619 genes and 28293 edges.In this section, we merged the protein-protein interaction (PPI) network , and x-axis at the top (degrees of freedom) means the number of nonzero coefficients of beta. y-axis is the values of the coefficients beta which measure the gene importance. The predictive model builds from the training set and then tests its predictive performance on the test set. The detailed results were represented in Figures L1/2 net selected the fewest number of genes and edges compared to L1/2 net, L1 net, and the Elastic net. Meanwhile, the predictive performance of the enhanced L1/2 net outperforms the other three regularization net algorithms.As shown in L1/2 net procedure, we report its capacity of identifying the biomarkers related to lung cancer. NK2 homeobox 1 (Nkx2-1) protein regulates transcription of genes specific for lung. It is used as a biomarker to determine lung cancer in anatomic pathology. It also has a critical role in maintaining lung tumor cells [L1/2 net . As these gene express significantly and continuously, it promotes lung cancer progression. On the other hand, this group is linked to ENO1. We are unable to get a clear testimony to sustain this relationship by looking at PPI database. However, a recent report [Except to identify these two significant biomarkers (EGFR and Nkx2-1), the enhanced t report  has demoL1/2 net is more reliable than L1/2 net, L1 net, and the Elastic net approaches for selecting key markers from high-dimensional genomic data. Another advantage of our proposed method is that it has the ability to recognize novel and potential relationships with biologic significance. It is mentionable that our proposed method is inclined to identify fewer but more informative genes (or edges) than L1 net and the Elastic net approaches in genomic data and that means the proposed method has allowed the researcher to more easily concentrate on the key targets for functional studies or downstream applications.All these results reveal that the enhanced L1/2 solver to penalized network-constrained logistic regression model to integrate lung cancer gene-expression with protein-to-protein interaction network. We develop the corresponding coordinate descent algorithm as a novel biomarker selection approach. This algorithm is extremely fast and easy to implement. Both simulation and real genomic data studies showed that the enhanced L1/2 net is a ranking procedure compared with L1/2 net (using the old thresholding operator), L1 net, and the Elastic net in the selection of biomarker and subnetwork.In biological molecular research, especially for cancer, the analysis of combining biological pathway information with gene-expression data may play an important role to search for new targets for drug design. In this paper, we use the enhanced We successfully identified several important clinical biomarkers and subnetwork that are driving lung cancer. The proposed method has provided new information to investigators in biological studies and can be the efficient tool for identifying cancer related biomarker and subnetwork.“Sub-networks identified by the L_1 net and the Elastic net for lung cancer datasets (only those genes that are linked on the PPI network are plotted). Nodes colored based on higher (red) to lower (green) coefficients in the model.”


Photoreceptor cGMP Phsophodiesterase 6 (PDE6) is the effector molecule of visual signal transduction and mediates fast response of light signals. The rod holo-PDE6 comprises catalytic  and two identical inhibitiory  subunits. The catalytic subunits comprise N-terminal tandem GAF domains followed by C-terminal catalytic domains and isoprenylations for membrane-association. Contrary to activation of other tandem GAF comprising PDEs, PDE6 activation does not occur via cGMP-induced concerted conformational changes. Rather two copies of the α-subunit of retinal G-Protein (Gα*), transducin, activate PDE6 by partially displacing the inhibitory subunits. The activation of PDE6 has therefore been described as a “de-inhibition”. The affinity of Gα* to PDE6 and the enzymatic activity of the intermediary 1:1 complex is highly disputed, therefore a conclusive activation model is lacking so far.Our combined structural, enzymatic and computational investigations deal with the activation-mechanism of PDE6. Our cryo electron microscopy (EM) structure of PDEαβ catalytic core shows an elongated bell-shaped structure with symmetric side-by-side arrangement of the two subunits with flexible membrane-binding domains. A comparison with nearly full-length inactive PDE2A structure  suggests


Humidity measurement is one of the most significant issues in various areas of applications such as instrumentation, automated systems, agriculture, climatology and GIS. Numerous sorts of humidity sensors fabricated and developed for industrial and laboratory applications are reviewed and presented in this article. The survey frequently concentrates on the RH sensors based upon their organic and inorganic functional materials, e.g., porous ceramics (semiconductors), polymers, ceramic/polymer and electrolytes, as well as conduction mechanism and fabrication technologies. A significant aim of this review is to provide a distinct categorization pursuant to state of the art humidity sensor types, principles of work, sensing substances, transduction mechanisms, and production technologies. Furthermore, performance characteristics of the different humidity sensors such as electrical and statistical data will be detailed and gives an added value to the report. By comparison of overall prospects of the sensors it was revealed that there are still drawbacks as to efficiency of sensing elements and conduction values. The flexibility offered by thick film and thin film processes either in the preparation of materials or in the choice of shape and size of the sensor structure provides advantages over other technologies. These ceramic sensors show faster response than other types. To have a desirable surrounding atmosphere, it is essential to monitor, detect and control the ambient humidity under different conditions ranging from low temperature to high or in mixtures with other gases by precise and provident sensors ,22. Utili.e., composite or single-species in nano-scale processing system are among the principal considerations. Synthesis and preparation of metal oxides as ceramic type hygrometers and film based humidity sensors are further assessed. To provide a clear comprehension of operation principles, the universally observed sensing mechanism of porous matters which is of an ionic type, will be detailed. Nanocomposites incorporating ceramics, ceramics/polymers and polymers/carbon nanotubes with nanoporous, nanofiber and nanowire forms are amongst the most promising materials for future applications, and their preparation processes deserve mention.This paper reviews reported studies on different humidity sensor fabrication technologies, principles of materials, applications, and moisture sensing mechanisms. Further, recent developments of dampness-sensitive materials, The rest of this article is organized as follows: in Section 2, the fundamental definitions of humidity and the procedures for parameter measurement are described. Then, a classification of humidity sensors and the related applications are given in Section 3. The principle of ceramic humidity sensor operation based upon hydrogen permeation and water protonation is detailed in Section 4. Whilst Section 5 provides a survey of impedance type sensors, Section 6 covers capacitive type transducers. A summary of the review is given in Section 7.2.Humidity is defined as the amount of water vapour in an atmosphere of air or other gases. Humidity parameters are stated in diverse ways and the corresponding units are based on the measurement technique used. The most commonly used terms are “Relative Humidity (RH)”, “Parts Per Million (PPM)” by weight or by volume and “Dew/Frost Point (D/F PT)”, in which the two latter are subclasses of “Absolute Humidity (AB)”. Absolute Humidity units are applicable for the primary measurement results inasmuch as one is able to directly measure the value of the water vapour content. In contrast, Relative Humidity is true for the secondary measurement results, since measurement of the water vapour values is mediated in some fashion.1/7000 pound lb) and expressed as:AB is the absolute humidity (g/m3 or grains/ft3), wm is the mass of water vapour (gram or grain) and v is the volume of air (m3 or ft3).Absolute Humidity (vapour density) is defined as a ratio of the mass of water vapour in air to the volume of air, with the unit of grams per cubic meter or grains per cubic foot .Relative Humidity (abbreviated as RH) is defined as ratio of the amount of moisture content of air to the maximum (saturated) moisture level that the air can hold at a same given temperature and pressure of the gas. RH is a temperature dependent magnitude, and hence it is a relative measurement. The RH measurement is stated as a percentage and determined by the expression:SH is the saturation humidity (g/m3), wsm is mass of water vapour at saturation (g) and v is the volume of air (m3). The saturation humidity is a function of temperature and can provide the maximum amount of moisture content (mass) in a unit volume of gas at a given temperature.Saturation Humidity is defined as the ratio of the mass of water vapour at saturation to the volume of air:According to Dew point is defined as a temperature (above 0 °C) at which the water vapour content of the gas begins to condense into liquid water, and Frost point is the temperature (below 0 °C) at which the water vapour in a gas condenses into ice. D/F point parameters are functions of the pressure of the gas, but independent of temperature and are amongst the absolute humidity measurements. In other words, dew point is the temperature at which the saturation water vapour pressure is equal to the partial pressure of the water vapour (in an air atmosphere). The difference between the ambient temperature and the dew point temperature is a measure of the ambient relative humidity ,39.3.Humidity sensing studies have progressed rapidly and humidity sensors—regardless of fabrication technique—have been widely employed in industrial and household applications as instrumentation equipment or for human comfort issues. Due to the different operating conditions of moisture sensors in different areas of application ranging from indoor to open air uses, various types of humidity sensing instruments have been developed based on different work principles and diverse hygroscopic sensing materials –45. AmonAs most of the commercially available humidity sensors in use are relative humidity sensors, these can be grouped based upon their sensing material types and according to their operating principle. In the 1980s the different sensing elements were proximately classified into three main groups of electrolytes, porous ceramics and organic polymers conforming to the Yamazoe and Shimizu classification . Around At the present time, plenty of different brand humidity sensors on the market or under development in laboratory studies are RH sensors, which are further categorized into three classes, including ceramic type (semiconductor), organic polymer-based sensors, and organic/inorganic hybrid sensors (polymer/ceramic). By reviewing the published results in this field, the authors have determined that well-nigh 80% of these three types are based on the electrolytic properties of the sensing matter, by virtue of the inner water electrolytes. Humidity sensors using nanowires, nanofibers, nanorods and p-n heterojunctions are subclasses of the ceramic (inorganic) type. Regarding the intrinsic properties of sensing elements, ceramic types can be designed by utilizing either semiconducting or dielectric metal oxide composites. Furthermore, polymeric types in turn can be based on conducting or non-conducting (dielectric) polyelectrolytes.All three categories of such sensors , utilize changes in the physical and electrical properties of the sensitive elements when exposed to the different atmospheric humidity conditions of the surrounding environment, and provide a measure of the humidity due to some amount of adsorption and desorption of water vapour molecules. It has been found that porous films exhibit higher humidity sensitivity than the nonporous counterparts . The preIn 1937 a electrolytic humidity sensor based on lithium chloride (LiCl) developed by Dunmore , became Impedance-sensitive type humidity sensors which encompass all three classes, are further classified into ionic and electronic (charge carriers) conduction type sensor devices. This classification is determined by the mechanism of the electrical transport. Electrolyte-based sensors being special cases that only follow the ionic transport mechanism. Later we present and classify the different types of sensing materials, working principles (impedance ionic or impedance electronic or capacitance type) with humidity and temperature ranges of various hygrometer sensors.Traditionally, the organic polymer film humidity sensors fall into the fundamental categories of resistive type (impedance type) ,54 and cCeramic type humidity sensors based on metal oxides have exhibited some superior advantages in comparison to polymer films from the viewpoints of their mechanical strength, thermal capability, physical stability and their resistance to chemical attack, which reveals them to be the most promising materials for electrochemical humidity sensor applications . This clBasically, metal-oxide ceramics used in humidity sensor applications are prepared by conventional  and adva4.i.e., ionic conduction, electronic conduction, solid-electrolyte and capacitive type, relies on the superficial water vapour adsorption based on chemical adsorption (chemisorption), physical adsorption (physisorption) and capillary condensation processes  trimethylammonium chloride) to an interdigitated gold electrode (pretreated with 3-mercaptopropionic acid) on a PET substrate by a peptide chemical protocol. N-(3-dimethylaminopropyl)-N'-ethylcarbodiimide hydrochloride (EDC) was employed as a peptide coupling reagent. The sensors have shown good humidity dependence over wide humidity ranges (20%–90% RH) with good long term stability. The water stability of the anchored poly-MMA-MAPTAC to the MPA/Au contact area was influenced by the amount of added EDC  trimethyl ammonium chloride) resistive humidity sensors were fabricated by Su et al. from composite thin films on a flexible polyester substrate through in situ photo-polymerization of precursors  (BCB) dielectric material (0.8 μm thick) was recently designed and configured on a flexible polyimide substrate (8 μm thick). To optimize the sensing performance, the electrode structure has been sketched based on numerical simulations. In testing at 10% to 90% RH, the sensor showed an excellent linear response (0.996) to capacitance variations .6.2.2O3) with a porous structure is the most applicable material for these type of sensors [2O3 with small pore radius is very sensitive to very low humidity levels [2O3 only the two phases γ-Al2O3 (amorphous) and α-Al2O3 (corundum) are commonly used in humidity sensing applications; further the prior is more sensitive than the latter one owing to its higher porosity [2O3 humidity sensor fabrication, due to its low cost and process simplicity.Aluminium oxide TiO3 on silicon substrate [x Ti1−xO3 [3 [2. The (Sr1−x Lax)TiO3 humidity sensor reported by Li et al. with MIS structure consisted of a silicon substrate, aluminium evaporated interdigitated top electrodes, thick film of (1 μm) Sr1−xLaxTiO3 and SiO2 as an insulator layer. The n-type semiconducting Sr1−xLaxTiO3 ceramic film was deposited on the SiO2 oxide layer via argon ion-beam sputtering and annealed at 400 °C in nitrogen [xTi1−xO3). The presented film has shown a higher grain boundary barrier which could be explained because of substitution of Ti4+ by Nb5+ in the B sites instead of Sr2+ by La3+ in the A sites, and consequently a higher activation energy [Other groups of ceramic-type capacitive moisture sensors are based on perovskite metal oxides ,323 suchubstrate , SrNbx T Ti1−xO3  and BaTi1−xO3 [3 . The comnitrogen . To furtn energy .Based on the parallel plate  or inter2 core/shell nanorod capacitive thin film humidity sensors were fabricated on glass substrates by Gu et al. Shells of sol gel processed anatase titanium oxide (TiO2) were deposited on hydrothermally grown zinc oxide (ZnO) nanorod cores. Morphological characterization showed that the primarily formed zinc oxide nanorods were coated with anatase titanium oxide shells as a second layer. Compared with those sensors based on individual ZnO and TiO2, the ZnO/TiO2 nanocomposite (ZTNA) sensors exhibited considerably enhanced sensitivity at 95% RH . Further, the core/shell arrays' capacitance varied from 101 to 106 pF over the whole humidity range of 11% to 95% RH at room temperature [Highly sensitive ZnO/TiOperature .i.e., cubic sillenite and monoclinic types synthesized through a hydrothermal method were investigated by Sheng and co-workers with respect to their capacitive characteristics [3+ cations incorporated with moderate PO dopants. The sensing mechanisms have been reported to be of two main types: “non-Debye” types at low RH and “proton transport” mechanisms at high RH.Recently, the humidity sensing properties of two types of bismuth phosphates eristics . Based oLike the sensing mechanism of previous types, the pore properties of capacitive type sensors are responsible for their humidity sensing performance and influence the physisorption and chemisorption of water vapour molecules as well as capillary condensation .Proton-exchange membrane fuel cells (PEMFCs) are one of the pragmatic applications of humidity sensors . The ambet al. were motivated to design and fabricate a home-made MEMS-based novel flexible multi-functional micro sensor integrated on a stainless steel foil substrate which was able to sense temperature, humidity and voltage. The proposed multi-functional micro sensors could be embedded in the cathode flow channel of a proton-exchange membrane fuel cell (PEMFC) to measure the variations of internal voltage, temperature and humidity. Fabricated micro sensors have benefited from numerous desirable advantages, encompassing high flexibility due to their stainless steel foil substrate, high temperature resistance, high sensitivity, tiny size, precision of measurement, and capability to operate in high moisture (high RH) conditions along with long term stability. To reduce the temperature dependence of the humidity sensor, a capacitive principle of construction based on an interdigitated structure with a polyimide sensitive film has been adopted instead of a resistive type. Furthermore, to eliminate a long time response of the micro humidity sensor, it has been integrated with a micro heater [Lee o heater .7.The moisture sensing properties, manufacturing technologies and operating mechanisms of various humidity sensors based on applications and different sensing elements consisting of ceramic, organic polymer and hybrid composites have been classified and comprehensively described. Synthesis and preparation methods of precursor metal oxides and polymers for hygrometric applications were also explained. To provide an overall perspective, a review of state-of-the-art humidity transducers has been rendered in table form. Regarding the mechanism of sensing, a protonic conduction type has been discussed in detailed as a generally accepted mechanism in the majority of sensors (more than 85%) at room temperature. The key factors such as design configuration, structural characteristics and applied precursor quantities that influence efficiency, stability and sensitivity have been discussed.Among all humidity sensor design configurations, the impedance- (resistive) and capacitive-based sensors are revealed to be the best suited and most popular ones in advanced applications such as laboratory research or automated industries. This popularity could be due to their ability to fulfil the general requirements such as simpler structure, free choice of sensing elements among various available types, cost, adaptability to different types of circuits, and ease of fabrication and measurement setup.Nowadays, film-based humidity sensors are widely applied because of their advantages such as cost-effectiveness, acceptable gasketing, design flexibility and quick deposition rate. Among the various types of the humidity sensors, semiconducting metal oxide and metal oxide/polymer based sensors, dominantly produced by thick film and thin film deposition techniques, are noteworthy due to their variety of sensitive element choices, post-processing and greater response characteristics. In comparison with polymer-based thin or thick film humidity sensors, the synthesis process of the ceramics is simpler and they frequently reveal very fast response times, however polymers are lower in cost. These sensors are operational over a wide range of humidity with instrumental response characteristics, and possess the potential of being commercially available for medium to high volume demands.Recent developed humidity sensors in both university research and industry, especially those incorporated with nano-scale elements, provide promising performance with the high impact contribution to an improvement of accuracy, reliability, and economic efficiency. However, in the real conditions of practical environments, there still remain challenges to be solved for enhancing the sensor efficiency and response characteristics. In the future outlook of the humidity sensors design process, the nanocrystalline composites incorporating ceramics and ceramic/polymer are among the most promising materials, which may be applied for humidity sensing applications due to the better performance prospects they exhibit.


The correlation of mechanical strains in graphene layer with the substrate morphology is established via Raman mapping. Apparent vertical piezoresponse from the single-layer graphene supported by underlying SiO2 structure is observed by piezoresponse force microscopy. The calculated vertical piezocoefficient is about 1.4 nm V−1, that is, much higher than that of the conventional piezoelectric materials such as lead zirconate titanate and comparable to that of relaxor single crystals. The observed piezoresponse and achieved strain in graphene are associated with the chemical interaction of graphene's carbon atoms with the oxygen from underlying SiO2. The results provide a basis for future applications of graphene layers for sensing, actuating and energy harvesting.Electromechanical response of materials is a key property for various applications ranging from actuators to sophisticated nanoelectromechanical systems. Here electromechanical properties of the single-layer graphene transferred onto SiO Pristine graphene is not piezoelectric, but adsorbing dopant atoms can induce a piezoresponse by breaking the material's inversion symmetry. Here the authors demonstrate strong out-of-plane piezoelectricity in graphene on a silica substrate, providing a system for two-dimensional sensing and actuation. Recent discovery of piezoelectricity in two-dimensional 2D) materials1D materia5729101112et al.2 grating  was periodically localized doping induced by chemical interaction of graphene with SiO2 surface141516Lee 2 calibration grating substrates by piezoresponse force microscopy (PFM) and confocal Raman spectroscopy. We experimentally confirm the existence of electric polarization induced in graphene deposited on SiO2 via direct measurements of converse piezoelectric effect by PFM. Piezoelectric activity was mainly observed on the supported graphene regions where van der Waals and/or chemical interaction between the SiO2 surface and graphene layer can induce an anisotropic strain and detectable PFM signal. The in-plane strain in graphene was evaluated by polarization-dependent Raman spectroscopy.In this work, we present the results of experimental study of single-layer graphene (SLG) deposited on SiO2 calibration grating TGZ4 by the company 2D-Tech (UK) The substrate was a commercially available standard calibration grating for atomic force microscopy (AFM) purchased from NT-MDT (Russia). It consisted of a grid of parallel rectangular pitches with the height 1,317±10 nm and period 1,500±10 nm. The thickness of the thermally grown SiO2 layer was 1,400±10 nm.SLG was grown by chemical vapour deposition on a copper substrate and then transferred by the wet process on top of the Si/SiO−1) and CSG30/Pt . Standard topography mapping was carried out in a tapping mode. PFM and conductive AFM were performed using CSG30/Pt cantilevers in the contact mode. PFM measurements were carried out in a quasi-static regime at the frequencies <100 KHz with the 0–5 V amplitude of a.c. excitation signal under a vacuum ∼10−1–10−2 torr. Frequency-dependent measurements of PFM amplitude were carried out in the 10–900 kHz frequency range. Raman measurements were performed using a confocal Raman microscope WiTec alpha300AR . A 488-nm solid-state excitation laser with polarization along the grating stripes was focused by a 100 × objective . Spectral resolution of the used diffraction grating was 3.19 cm−1. Raman spectra were acquired by a charge-coupled device camera with 0.2 s integration time.Topography and PFM measurements were performed using a Ntegra Prima AFM  equipped with the lock-in amplifier  and function generator . The measurements were carried out using Pt-coated cantilevers purchased from NT-MDT (Russia): NSG30/Pt  were performed using a conventional AFM tapping mode . In thisBy contrast, scanning in a contact mode required by PFM a deleterious effect on the structure of both supported and suspended areas could be seen. Graphene layer could be destroyed after a single scan even at zero deflection level . Such behaviour demonstrated sufficiently low stability of graphene under an influence of lateral (in-plane) force and special care  was taken to prevent early degradation of the SLG during measurements.2 calibration grating substrate with the SLG on top is shown in Representative topography of the Si/SiOThe mechanical strain state of graphene layer was studied by Raman spectroscopy. Analysis of G-band frequency shift allowed estimating the value and spatial distribution of mechanical strains, as well as strain gradients .Spatial distribution of the strains (strain map) is shown in Tensile strains occur in defect-free regions of the graphene. The largest values of such strains fall on supported graphene  applied between the tip and the substrate to detect tiny (pm level) piezoelectric vibrations of the graphene surface. Despite the partial damage, we could find areas where the topography did not change during scanning. The cross-section of the PFM (mixed) signal across such an area is shown in 2 substrate and, thus, provides a natural reference to the PFM signal observed on graphene.Zero PFM signal on the right part of It is seen that clear negative piezoresponse is observed on both supported (shaded areas) and suspended graphene, and the signal on the supported graphene is four times higher than on the suspended one. The amplitude of the electric field-induced vibrations (first harmonic) was about 0.02–0.03 nm. The maximum strain attained and corresponding piezoelectric coefficient will be estimated below.To avoid further degradation of the SLG, voltage spectroscopy measurements18191920To further prove the piezoelectric nature of the response, we measured the frequency dependence of the signal under different a.c. voltage levels. Resonance amplification at the cantilever fundamental resonance was used to get a.c. voltage dependence at low biases and to verify the linearity of the response. Conventional resonance spectra were observed with the fundamental resonance at 262 kHz . Signal 2 substrate2 complexes on the surface layer, the C+–O− dipoles partly deviate from the substrate , we can excite and detect surface deflection directly under the tip. When the tip is in contact with the suspended area (with higher conductivity), the electric field is applied over the area covered by the graphene sheet, reaches the dipoles in the supported regions and excites mechanical vibrations. These mechanical vibrations from the supported regions are transferred by the graphene membrane and drive the tip.2. It is known that the strain in graphene or/and additional oxygen-containing functional groups can transfer the graphene into semi-conducting or insulating state122 surface can also induce the gap opening2.Significant frequency shift of the G-band seen in the Raman spectra on the supported regions  associatE perpendicular to the surface plane can be described as follows:Strain induced by the electric field P is the sum of the induced, Pi, and spontaneous, Ps, polarization: P=Pi+Ps=(ɛ−1)ɛ0E+Ps. Then Here the electric polarization Q33(ɛ−1)ɛ0PsE corresponds to the piezoelectric effect, with the piezoelectric coefficient d33=2Q33(ɛ−1)ɛ0Ps. The last term, E (first and last terms):The first term 2E is a combination of a.c. Ea.c. and d.c. Ed.c. components: E=Ed.c.+Ea.c.. Since the applied Ea.c.=E0 cos (ωt+φ0), then normal to the surface displacement, h is thickness of the carbon-oxide dipole layer. The amplitude of the first harmonic thus takes the form:The applied electric field E0 is the magnitude of a.c. electric component, Ea.c.. For thin carbon-oxide dipole layer with h<<R0, where R0 is the tip radius, we can consider electric field below the tip in the layer as uniform. Using the relation for tip potential  out-of-plane converse piezoelectric effect on supported graphene induced by underlying SiO2. This effect is directly related to a huge number of possible applications such as motors, actuators, resonators and micro- and nanomechanical systems based on graphene. Naturally, the displacement level can be increased by fabricating thin SiO2 membrane or bridge structures. As such, a high value of the out-of-plane piezoelectric coefficient observed in our work provides a basis for such applications.When this manuscript was prepared for the submission, the paper by Wang 2 calibration grating substrates. Mapping the strain distribution in graphene was performed via confocal Raman measurements and converse piezoelectric effect was measured locally by PFM. The piezoelectric activity in graphene layers was attributed to the chemical interaction of graphene atoms with underlying oxygen from SiO2 substrate. Piezoelectric effect is sufficiently high . We foresee a number of emergent applications when using graphene/SiO2 structures as a platform for future sensors and actuators.In summary, we observed a strong piezoelectric activity of the SLG deposited on Si/SiOHow to cite this article: da Cunha Rodrigues, G. et al. Strong piezoelectricity in single-layer graphene deposited on SiO2 grating substrates. Nat. Commun. 7:7572 doi: 10.1038/ncomms8572 (2015).Supplementary Figures 1-5, Supplementary Notes 1-2 and Supplementary References


Based on the first-principles calculations, we here clearly reveal how an excess of negative charge density affects the variation of Gibbs free energy (ΔG) and the corresponding HER behavior. It is demonstrated that the electron plays a crucial role in the HER routine. To verify the theoretical analyses, the MoS2 and reduced graphite oxide (rGO) composite with well defined 3-dimensional configuration was synthesized via a facile one-step approach for the first time. The experimental data show that the HER performance have a direct link to the conductivity. These findings pave the way for a further developing of 2-dimension based composites for HER applications.The MoS Recently, massive efforts have been devoted to producing hydrogen by electrochemical or photoelectrocatalytic processes from water splitting23568In recent years, the demands for the renewable and clean energy resources gradually become urgent for the growing problems of environmental pollution. Hydrogen, as a clean and efficient fuel source, has been vigorously pursued as a promising candidate for future energy carrier. The traditional way to produce hydrogen involving CO2, WC, Mo2C and MoS2112 nanostructure is a very promising alternative due to its widely distribution, high chemical stability in acid environment and excellent electrocatalytic properties after a certain form of modification2 mainly derives from the edges of its 2D layers. So far, the main difficulties that impede the extensive use of this material attribute to the restricted surface area, few exposed active edge, and low hierarchy conductivity2 by resorting to nanoscale or amorphous structures and improving the systematic conductivity through loading the catalyst on highly conductive matrixes. Previously, MoS2 cooperated with Au15Inspired by the previous discoveries in the hydrogenation and dehydrogenation of petroleum chemicals, a certain amount of metal non-oxides with nanostructures, such as WS2 cooperated with graphene. Graphene is consisted of sp2-hybridized carbon atoms and has attracted many attentions for its excellent conductivity, stability and single-layer configuration. Based on the above features, the combination of MoS2 with graphene should be a promising approach to enhance the durability and integral conductivity for the application in HER2/rGO composite nanostructures were synthesized by a facile hydrothermal method. The experiment data revealed that the HER performance have a link to the conductivity.In this work, we studied the HER performance of MoS2 and MoS2/graphene composite. The charge density difference map represents the charge re-distribution of different portions. All atoms were kept at the same positions as they were in the integral structure. The yellow regions represent charge accumulation while the blue denote charge depletion. 2 layer and it proximate 2 nd layer, while the 3 rd MoS2 layer is relatively unscathed. This is easy to understand since the absence of strong bonding interactions between MoS2 layers and the weak van der Waals (vdW) interactions are expected to play a leading role. To model the MoS2/rGO hybrid, we consider the composite system of graphene on bulk structured MoS2 surface as presented in 2 keep their original planes and hexagonal atomic networks. For the MoS2/graphene composite, an obvious difference is that the existence of graphene layer noticeably affect the charge density distribution of the three MoS2 layers, in which the charge transfer also appears around the 3 rd layer. Besides, a more remarkable charge transfer between the graphene and the adjacent MoS2 layer are found, indicating the presence of a weak ionic interaction more than just the van der Waals that between the stacking layers in the pristine structure. 2 layers and there is scarcely any accumulation or depletion around the 3 rd layer. In comparison with the MoS2/graphene composite, it shows a much wider interaction scope for the incorporation of graphene layer and the obvious accumulation/depletion regions are also demonstrated around the 3 rd MoS2 layer. 2 and composite structures which are plotted as a function of distance along z-direction. The charge density difference of the pristine structure almost equal to zero around the 3 rd MoS2 layer as marked in pink, however, the difference of composite structure fluctuates all along from nearly zero up to the graphene layer, indicating a sustained interaction between the MoS2 layers on account of the combination with graphene layer. As illustrated above that the significant charge density difference between the two structures, distinct difference is also reflected in the change of Bader charges. It is calculated that almost ignorable charge transfer of 0.004 e form the 2 nd and 3 rd layers to the 1st MoS2 in pristine structure, while this value is 0.03 e from MoS2 to graphene, suggesting a stronger interaction between MoS2 and graphene. One aspect to note here is that the transfer direction of the electrons in MoS2/graphene composite. Experimentally, the work function of the graphene is determined to be 4.57 ± 0.05 eV22 is higher than that of graphene, the electrons will flow from MoS2 to graphene if they contact. Consequently, MoS2 will be positively charged and graphene will be negatively charged for the involvement of electrostatic. When the two phases acquire an equalized Fermi level, a built-in electric field directed from MoS2 to graphene will be established at the same time. Finally, this electrical coupling to the graphene will facilitate the following rapid electrons transfer from electrode to rGO and then to MoS2, forming an excessive negative charge density to enhance the HER performance in the corresponding electrochemical processes.Firstly, charge density difference maps are plotted in et al.2 with controlled growth of atomically thin films. Different from the conventional wisdom, they considered that the key to develop the high-efficient catalysts was to increase the hopping efficiency of electrons in the vertical direction based on layer dependent experiments. This is because the potential barriers exist in the interlayer gap of MoS2 and the electrons transfer in the z direction is through hopping mechanism2 and MoS2/graphene composite, in which two oscillation patterns of the average potential correspond to the periodicity of the supercell. The averaged potential for the pristine MoS2 and MoS2/graphene composite are −5.47 and −5.91 eV, respectively. It clearly shows that the averaged potential of composite structure is lower than that of pristine MoS2, implying an easier transfer of electrons form electrode to MoS2 in the direction perpendicular to the MoS2 basal plane for the MoS2/graphene composite and an increase of hopping efficiency for electrons in the vertical direction. Therefore, based on the above analyses, it is safe to conclude that an excess of negative charge density can form for the MoS2/graphene composite due to the achievement of an increased hopping efficiency for the composite structure when in the corresponding electrochemical processes.Previously, Yu 2/graphene composite as analyzed above is that electrons can transfer from graphene to MoS2 directionally and the increased hopping efficiency of the system can form an excessive negative charge density. This motivates us to further study what role the excessive negative charge plays other than the enhanced conductivity. The considered atomic structures of basal plane, Mo and S edges are shown in the insert figures of (b–d). One thing to note is that the Mo edge is terminated with single S atom and that the Mo edge is not just a simple truncation of the bulk MoS2 structure2 would be difficult2 for HER is Mo-edge site that approaching to 0 eV. To verify the above analyses, we designed experiments based on MoS2/rGO system and intended to change the conductivity of the different structures to simulate various electron-rich levels through the control of hydrothermal reaction temperature.The main feature of MoS2/rGO and elementary composition information. We are aim to synthesize the nanoflowers structured MG composite. The morphology of the pristine MoS2 can be clearly observed from 2 sphere shows an average diameter of 1 to 2 m. The surface of the pristine MoS2 has a flowerlike morphology that mainly consisted by 2D nanopetals and the synthesized nanopetals grow in high density, which are freely and tightly aggregated together as exhibited in 2 spherical structure, it can be seen that the petals (2D nanosheet) disorderly intersected together. Insert 2 spherical structure grows in a high density way and the pristine MoS2 petal tends to stack with a 0.619 nm (3.097 m of 5 layers) interlayer distance.2. The insert 2 and grown in a more free and loose way. As analyzed above, the ΔG of M-edge is more closer to zero than that of S-edge and basal site. The sharply increased petals can thus provide much more activity edges and enhance the HER performance. The MoS2 in these hybrid agglomerates are mainly consisted by limited-layer MoS2 structures and tightly coupled with underlying rGO sheets. In contrast, the TEM image of the MG3 nanostructure shown in 2 grown on rGO sheets characterized by varied brightness. These results show that the incorporation of rGO can dramatically suppress the stacking of MoS2 to a great extent during its growth progress. Next, the crystallinities of the pristine MoS2 and MG3 composite are further characterized by FFT patterns. The FFT pattern of pristine MoS2 shown in the insert of 2. As for the MG3 composite shown in 2 along (002) direction. One thing to note here is that the FFT pattern of MoS2 in MG3 composite turns into arcs, which is clearly different from the well-defined crystal structure of pristine MoS2 and this indicates the formation of a quasi-crystalline structure of MG3 composite. We consider the nucleation mechanism on graphene was affected by oxygenated functional groups of GO. In the process of the GO reduced to rGO, MoS2 might grow on these sites. Graphene itself is 2D structured, while MoS2 has the nature of growing into flower. Both sides result in the confined growth of the corresponding MoS2 structure and form this special morphology and the petals are almost parallel to the graphene. 2 and rGO. The upper left fringes of d = 0.62 nm is in agreement with the (002) plane of MoS2, indicating the synthesized petals grow in a crystallization way. The lower right exhibits lattice fringes of 0.34 nm which can be well indexed to (002) plane of 2H graphene (PDF#41-1487). It is apparent that the heterostructure formed between MoS2 and rGO in the MG3 composite. From the elemental mapping of 2 nanoflowers are clearly showed from 2 XRD spectrum are observed, indicating the crystallized MoS2 can be obtained through the hydrothermal method at 180 °C with the absence of GO. The three diffraction peaks around 2θ = 14°, 34° and 57° index to (002), (100), and (110) plane of MoS2 (JCPDS#37-1492), respectively. The four composites show a progressive crystallinity degree. For MG1-3, the broad peak 1 corresponds to the diffraction of MoS22 synthesized through the same conditions shows a better crystallinity and this is also accord with the above FFT observation. The significantly decreased diffraction peaks of MoS2 for MG3 imply that the incorporation of rGO can restrain the aggregation of natural stacked MoS2 during hydrothermal process to a certain degree, leading to a poor crystallinity with a few-layered MoS2 nanosheet. Previous reports have proven that the amorphous MoS2 presented higher HER activity than that of crystalline MoS2 within a given range since the amorphous structure can provide a mass of exposed edges2 with restrained crystallinity synthesized here can also provide ample edges as active sites due to their confined environment based on rGO network. A clear trend for MG composites is that the intensities of all diffraction peaks of MoS2 significantly increase with the increment of temperature and this also accompanies with the appearance of a new peak around 24° ascribed to rGO sheets. Based on the above analyses, it is credible to conclude that the synthesized MG composites at different temperatures have a progressive crystallinity.−1 corresponding to the E12g and A1g vibrational modes for pristine MoS2, respectively. It has been reported that the frequency of A1g mode would present a red-shift as the layer number of MoS2 decreases1g mode is observed for MG3 composite in comparison with pristine MoS2, implying a decreased layer number after combination with rGO, which is also consistent with the above TEM and XRD analyses. The intensities of these two vibrational modes reflect the terminated structure of MoS2 and a lower intensity of E12g compared to A1g is associated with an edge-terminated characteristic−1. The two bands correspond very well to the D and G-band of graphene. As previously reported, intensity ratio ID/IG is the quality indication of grapheneD/IG agrees well with that of rGO, implying GO was indeed reduced to rGO with some defects or/and disordered structures2 is exhibited in 2. The two main peaks of Mo 3d5/2 (229.3 eV) and Mo 3d3/2 (232.4 eV) are characteristics of MoS2. Sulfur species of pristine MoS2 are shown in 3/2 and S 2p1/2 of MoS2, respectively2. This result indicates that there exists a certain interaction between MoS2 and rGO sheets in the composited structure, driven by a probable electrons transfer between firmly contacted MoS2 and rGO sheets. In the composite, the co-electron cloud could form between S atom and its adjacent carbon layer. The interaction of electrons between MoS2 and rGO could dramatically enhance the conductivity of the composite and is conducive to the corresponding HER process. Generally, there exists the strong Coulomb interaction between the inner electrons and nucleus in the atom, while the outer electrons can also apply the shielding effect to the inner electrons. Therefore, the decrease of electron concentration of outer electrons will lead to the reduction of the corresponding shielding effect and thus increase the binding energy of the inner electrons for the existence of penetration effect, resulting in the corresponding XPS peaks shift to a higher energy direction. Obviously, the above XPS surveys reveal that electrons can transfer form MoS2 to rGO, which is also consistent with the calculated results that the directional transfer of electrons from MoS2 to graphene network and a built-in electric field establishment directed from MoS2 to graphene. A further quantitative elements analyses by XPS reveal that the oxygen percentages decreased from 5.31, 4.83, 4.11 to 3.27% and the atomic ratio of S:Mo also reduced from 2.27, 2.23, 2.12 to 2.07 as the synthesis temperatures increasing for MG1 to 4. These changes mean that the synthesis temperatures have a direct link to the incorporated oxygen and the unsaturated sulfur atoms, which will inevitably influence the electronic structure of MoS2 and exert a direct influence on the HER activity. This will be discussed in details in the following sections.The chemical states of Mo and S in the pristine and MG3 are further investigated by XPS. The XPS spectrum of Mo 3d in pristine MoSads coverage. The HER process on the Pt surface generally follows the Volmer-Tafel mechanism as described below. The rate-limiting step is corresponding to recombination step and it has the Tafel slope of 31 mV/decade. The polarization curve of GCE is also plotted, while the trivial current indicates that the GCE is catalytically inactive for the HER. It can be seen that much lower onset overpotentials (η) are obtained for all MG catalysts compared to pristine MoS2. Among composite structures, MG3 presents the lowest onset η of about 110 mV, implying a more superior HER activity and the cathodic current density rises rapidly under a more negative potential. The optimal catalytic performance of MG3 composite is attributed to the synergetic effect of the optimal intrinsic conductivity and the excellent conductive network provided by rGO network. The corresponding Tafel plot indicates that the suitably crystallized MG3 possesses the smallest Tafel slope than the poorly crystallized MG1 and MG2 or highly crystallized MG4. This proves that the crystallinity of MoS2 should be controlled in a reasonable level for a higher HER performance, since a poor crystallinity leads to the decreased conductivity for the suppressed electron transfer along the basal surfaces, which ends in a weakened HER activity. However, even if the improved crystallization can enhance the conductivity with increase of temperature, the inherent incorporated oxygen and sulphur atoms will gradually decrease as analyzed by XPS. In this case, the decrease of the two atoms that play promotion roles in the corresponding HER routine will lead to a subdued intrinsic conductivity in reverse which will be discussed later. Hence, a moderate crystallinity degree with quasi-crystallinity structure is more beneficial for HER.The kinetic of the HER depends upon the electrochemical conditions and the corresponding Tafel slope is often utilized to elucidate the dominant mechanisms involved in the HER routine. There are two possible reaction steps in acidic aqueous. First, the discharge reactionsecond, the combination reaction:or the desorption reaction:−1. When (1) is fast and followed by a slow electrochemical desorption reaction (3), a Tafel slope of ∼40 mV dec−1 is obtained. If (1) is rate-limiting or the surface coverage is close to one, the Tafel slope is ∼120 mV dec−1. The Tafel slope values obtained from −1 for pristine and MG1 to 4 composites, respectively. It is noteworthy that the Tafel slope of ∼41 mV dec−1 for MG3 composite suggests that desorption is the rate-limiting step.Generally, a fast discharge reaction (1) followed by a rate-limiting combination reaction (2) leads to a Tafel slope of ∼30 mV dec2 and MG composites in HER processes. The Nyquist plots of different structures are given in ct) for the MG3 electrode. The obtained semicircles are also in accord with the HER activity shown in ct corresponds to a faster reaction rate. In this case, a faster charge transfer during HER reaction with a moderate crystallinity and rGO incorporation contribute to a superior HER activity of the MG3 composite for the achievement of both structural and electronic synergistic effect for the prominent HER performance. We also cycled the catalysts continually for 2000 cycles to probe HER stability. In this process, the catalyst poisoning or delaminating from the electrode may cause a certain activity loss of catalytic. As shown in −2 at 200 and 300 mV, respectively. Besides, the potential values of various structures recorded at the same current density of 5 mA cm−2 for 2000 sweeps are displayed in 2. We consider that the hybrid structures of MG can enhance the durability, which can efficiently hinder the degradation of catalysts in HER routines.EIS can be further utilized to study the interface reactions and electrode kinetics of pristine MoS2 exhibits a narrower bandgap (1.03/ 0.97 eV) in comparison with pristine structure (1.12 eV), implying an easier electron transfer from valance band maximum (VBM) to conduction band minimum (CBM) and thus the superfluous of S/O can lead to an easier electrons transfer. Also, a heavier incorporation can further reduce bandgap and thus regulates band structure. The reduced bandgap ascribes to the incorporation of S/O atoms can enhance hybridization effect between Mo d-orbital and S/O p-orbital. This means that the impurities levels from the absorbed S/O can effectively reduce band gaps. According to the present work, the effective directional transfer of electrons can form a built-in electric field directed from MoS2 to graphene. This electrical coupling will facilitate the following rapid electrons transfer from electrode to rGO and then to MoS2, forming an excessive negative charge density which will effectively prompt ΔG shift to the thermo-neutral state and finally facilitate the overall HER routine.From the above XPS and EIS analyses, it is shown that the incorporation of oxygen and sulfur atoms with moderate crystallinity can realize the modulation of conductivity for a better HER performance. Next, we will analyze how it influences the electronic structure of the specific structures when in S/O rich conditions. For the purpose of excluding the effect of the different adsorption sites between the pristine and S/O incorporated structures, the following adsorption analyses are conducted based on the most stable adsorption configurations for the two structures. The related adsorption sites considered include the S/Mo top and hollow top sites as shown in the insert 2. We attribute the optimized performance of MG composites to the strong chemical and electronic synergetic effects between rGO network and MoS2. On one hand, the confined growth of highly dispersed MoS2 on rGO is free of aggregation2 on rGO afford a large amount of accessible edges that can be used as active sites for HER. For another, electrical coupling to the graphene also make a rapid electron transfer from electrodes to rGO and then to MoS2, forming an excessive negative charge density to improve the Gibbs free energy and facilitate the HER routine  with Cu-Kα radiation (λ = 0.1541 nm) was used to characterize the different samples. The diffraction data were collected in the 2θ scanning ranging from 10° to 70° with 0.02° per step. The morphologies of the samples were obtained by field emission scanning electron microscopy  at an accelerating voltage of 20 kV and transmission electron microscopy  at an accelerating voltage of 200 kV. The sample used for TEM characterization was prepared by dropping the colloidal solution to a holey carbon-coated copper grid, which was then dried in air. Raman spectra analyses were carried out by a Jobin-Yvon LabRAM HR 800 micro-Raman spectrometer. X-ray photoelectron spectrometry (XPS) analyses were performed by ESCALAB 250Xi.2SO4 solution. 2 mg MG composite was dispersed in a 1 mL 4 : 1 v/v water–ethanol, and then blended with 20 μL 5 wt% Nafion. After 30 min sonicated, a homogeneous ink formed and the mixture was casted onto the GCE for the following electrochemical measurements. 5 μL of the dispersion was loaded onto a glassy carbon electrode. Different loadings of the catalyst were obtained by repeatedly adding mixture until it reached the required values. Linear sweep voltammetry (LSV) method with scan rate of 5 mV s−1 was used to evaluate the HER performance of different catalysts. Catalytic modified GCE acted as the working electrode. Pt wire served as the counter electrode and saturated calomel electrode (SCE) acted as the reference electrode. Prior to all electrochemical measurements, the electrolyte solutions were purged with N2 for 1 h to remove the oxygen completely.Electrochemical measurements were performed on a glassy carbon electrode (GCE) in 0.5 M H4s5d4), S (s2p4), O (s2p4) and C (s2p2), respectively. The generalized gradient approximation (GGA) functional of Perdew, Burke and Ernzerhof (PBE) was used to deal with the exchange and correlation potentials. Basal calculations were performed with a 3 × 3 × 1 supercell. The spacing of 15 Å between two dimensional single layers of pristine MoS2 and MoS2/graphene was used to avoid interlayer interactions. A 5 × 5 × 1 Monkhorst-Pack k-point sampling for the Brillouin zone k-point mesh and a 500 eV cutoff energy were used for the calculations.In this section, first-principles calculations were performed based on the DFT together with the projector-augmented wave (PAW) potential implemented in the Vienna ab initio simulation package (VASP). The valence electrons considered were: Mo .


Graphene is one of the ideal nanomaterials to be paired with silicon, and their complementary properties can be exploited in field emission (FE) devices. We reported an efficient way to produce and adjust the dimension of uniform protrusions within graphene. First, a multistep template replication process was utilized to fabricate highly periodic and well-aligned silicon nanowires (SiNWs) of different diameters . Then, large-scale and uniform graphene, fabricated by chemical vapor deposition (CVD), was transferred onto these size-controlled SiNWs to obtain the nanoscale and uniform undulations. As compared to the nanowires alone, the hybrid structures lead to higher FE performance due to electron conductivity enhancement, high-density emmison protrusions and band bending. These hybrid SiNWs/graphene structures could provide a promising class of field emission cathodes. The silicon-based field emission devices are potential electron sources1It is well known that graphene has a large surface area, flexible structure, sharp edges, unique geometry and favourable electric properties56In general, field emission properties of nanostructured emitters depend on many factors−1, corresponding to the D, G and 2G modes. The D band arises from backscattering of phonons by edges and defects like corrugation, twisting and edges. The G band is associated with doubly degenerate E2g phonon modes of the sp2 hybridized carbon network of graphite. A symmetric 2D peak originates from a second-order process, indicating the graphene layers1819ID/IG value of SiNWs/G is 0.69, lower than that (0.90) of graphene on silicon. These signals confirm that good quality graphene was maintained after transfering to SiNWs. Additionally, the intensity ratio of G to 2D bands (IG/I2D) is 1.58, which indicates it’s not the monolayered graphene.The SiNWs/G hybrids and graphene on the Si substrate were investigated through Raman spectroscopy . The graThe TEM and HRTEM were performed to study the formation of protruded graphene layers on top of the SiNWs. From J) produced by a given electric field (E) is described as the following equationField electron emission is a quantum tunnelling phenomenon where electrons are emitted from a solid surface due to the effect of a strong electrostatic field. The field emission characteristics of SiNWs with different diameters  and their corresponding heterostructures with graphene are shown in A and B are constants, Φ is the work function of the emitting materials, β is field enhancement factor. Usually, β was calculated from the slope of F–N plotk = −BΦ1.5d/β. Here, Φ is 4.85 eV for n-Si (100) and 4.53 eV for graphened is the anode–cathode distance. Usually, the lower work function and larger tip radius of curvature contribute to the higher current density. The F–N plots corresponding to the samples under investigation were depicted in β was found to be 3727, 2142, 1875. The field enhancement factor can be estimated by β ∝ aspect ratio, thus SiNWs of 400 nm have a higher field enhancement factor than the other samples. It appears that electric field shielding effects from closely packed nanowires adversely affect their field emission characteristics. By reducing the diameter of nanowires and increasing the space between the SiNWs, field shielding effects can be efficiently reduced, and the FE performance was enhanced.Where For SiNWs/G, the turn-on field values with diameters of 400, 500 and 600 nm are 2.59, 2.33, and 2.01 V/μm, respectively. The slopes of F–N plot of SiNWs/G  in the l24β values, suggesting that a combination of graphene and SiNWs can effectively improve the FE properties. The 600 nm SiNWs tailored graphene composite displayed the best field emission with a threshold electric field of only 3.22 V/μm at 1 mA/cm2 emission current. Long-term stability of field emission is aslo a critical issue for continuous operation. To check the robustness of the graphene on 600 nm SiNWs, the field emission stability of G/SiNWs was recorded at a fixed electric field of 3.15 V/μm for 3 h. It shows an average emission current density of ~932 μA/cm2, without showing any sign of degradation  is shown in β value as the SiNWs diameter increased. The graphene supported on the SiNWs of 600 nm in diameter have an ultra-low turn-on field (2.01 V/μm) and threshold electric field (3.22 V/μm), and they are observed to have predominant FE stability. The highly efficient and stable field emission performance is because of electron conductivity enhancement, high-density emission spots and band bending. These high-density protrusions localize and enhance the local electric field. Furthermore, the band bending at the SiNWs–graphene junctions level their dissimilar work functions, allowing for easy electron transfer from the SiNWs to the graphene. Therefore, the graphene protrusions, with a controlled period in the graphene sheet, may act as excellent field emitters.In conclusion, we report an effective approach to control the uniform SiNWs with precisely tunable diameters and proximity, relative to the PS sphere size and catalyzed etching time. Then, large scale, uniform graphene was transferred onto well-aligned SiNWs. On one hand, SiNWs provide the graphene sheet with uniform and nanometer-scale protrusions. On the other hand, the emitter density and inter-protrusion distance can be manipulated by the size of the nanowires. For bare SiNWs, the FE properties were significantly increased as the SiNWs diameter decreased and proximity increased. Conversely, the hybrid structure of stretched graphene on the tips of the SiNWs exhibited lower turn-on electric field and higher 2SO4/H2O2) for 1 h. Subsequently, monodispered PS nanosphere template of 960 nm in diameter was prepared by the air-water interface self-assembly method. Next, pretreated silicon substrates were placed and pushed to the below side of the compact PS spheres, and the monolayer of the PS nanosphere template could be transferred onto the Si substrate upon completion of the water evaporation.The n-type (100) Si pieces were degreased by successive ultrasonic cleaning in acetone, ethanol, and deionized water for 15 min each, and then immersed into boiling acid solution (4:1 (v/v) H2 flow rate of 40 sccm, basic pressure of 2 Pa, and a radio frequency power of 30 W. After that, the Ag film was deposited onto the Si substrate by magnetron sputtering, forming a porous Ag film as catalyst. Subsequently, the Ag film coated Si substrate was etched in a mixed solution of HF and H2O2 at 30 °C for 15 min, where the concentrations of HF and H2O2 were 4.8 and 0.3 M, respectively. Finally, the PS sphere templates were removed by ultrasonication in toluene, and the retained Ag film was dissolved with HNO3The diameter of PS spheres was reduced by applying a different time of reactive ion etching (RIE) with an OThe graphene used in our work was originally grown on copper foil by the CVD methodThe morphology and microstructure of the heterostructures were observed by field emission scanning electron microscopy , and transmission electron microscopy . Raman-scattering spectra were collected using an HORIBA Jobin Yvon HR800 Spectrometer (laser of 562 nm). For testing the field emission properties, the samples were fixed onto the cathode, and the phosphor was deposited on a transparent conductive material (indium-tin-oxide) to serve as the anode electrode in the vacuum system. The distance between the cathode and anode was kept to ~300 μm. A voltage was applied between the anode and cathode to supply an electric field. The emission current was measured by increasing a voltage.How to cite this article: Lv, S. et al. Optimizing Field Emission Properties of the Hybrid Structures of Graphene Stretched on Patterned and Size-controllable SiNWs. Sci. Rep.5, 15035; doi: 10.1038/srep15035 (2015).