This study presents the functionalization and characterization of graphene and electromagnetic interference (EMI) attenuation capacity in epoxy-nanocomposites. The modification of graphene involved both small molecules and polymers for compatibilization with epoxy resin components to provide EMI shielding. The TGA and RAMAN analyses confirmed the synthesis of graphene with a different layer thickness of the graphene sheets. Graphene samples with different layer thicknesses  were selected and further employed for epoxy coating formulation. The obtained nanocomposites were characterized in terms of EMI shielding effectiveness, SEM, micro-CT, magnetic properties, and stress-strain resistance. The EMI shielding effectiveness results indicated that the unmodified graphene and hexamethylene diamine (HMDA) modified graphene displayed the best EMI shielding properties at 11 GHz. However, the epoxy nanocomposites based on HMDA modified graphene displayed better flexibility with an identical EMI shielding effectiveness compared to the unmodified graphene despite the formation of aggregates. The improved flexibility of the epoxy nanocomposites and EMI shielding characteristics of HMDA functionalized graphene offers a practical solution for textile coatings with microwave absorbing (MA) capacity. Mmagnetic .MA materials can be classified into the following two categories: magnetic and dielectric absorbing materials ,7. A perPolymer-based composites could meet all these requirements ,16,17. FIn recent years, graphene has attracted lots of attention for its potential application in MA; the attractive potential properties of graphene for MA performance are probably attributed to the following reasons: the ultrathin thickness and optical transmittance property of graphene can not only decrease the density of the MA materials but also weaken the skin effect effectively; the resonance effect resulted from the layered and high porous morphology as well as the high specific surface area could be very beneficial to the absorption and attenuation of the microwave; the interfacial polarization caused by the large interface can also be helpful for the absorption of the microwave ,27,28,29Epoxy resins are the most important thermosets, widely used as adhesives, coatings, and as a polymer matrix to obtain polymer nanocomposites ,30,31,32Our study deals with the synthesis of new EMI shielding materials based on epoxy polymers and chemically modified graphene. The graphene has been functionalized for better dispersion in the epoxy resin (polymer matrix). In our case, the functionalization takes place with small molecules, respectively with polymers. Samples of graphene with different layer thicknesses  were selected and further employed for epoxy coating formulation. Thus, the EMI shielding attenuation capacity of the nanocomposites containing modified graphene was evaluated. Additionally, the mechanical properties of the epoxy composites were assessed given the final objective to obtain an efficient EMI shielding solution capable to be used in textile coatings. Thus, modified graphene-based materials were demonstrated to confer both EMI shielding effectiveness and suitable mechanical properties, making them one of the most appropriate materials for improving MA capacity in the future.3 and precipitation in methanol.Graphene nanoplatelets  (G)  were used as received. Nitric acid , sulfuric acid , glycidyl methacrylate , N,N’-dimethylformamide , lauryl peroxide , acetone (Sigma-Aldrich), methanol (Sigma-Aldrich), hexamethylene diamine , and acrylamide (Sigma-Aldrich) were used as received. Methyl methacrylate  was purified through vacuum distillation. Hexyl methacrylate  was purified by passing through an alumina column. Benzoyl peroxide  was recrystallized by dissolution in CHCl(a) Functionalization of Graphene with small moleculesGraphene oxide was prepared according to a modified method. Briefly, 1 g of graphene was dispersed in 30 mL of H2SO4 (95–98%), then 10 mL of HNO3 (68%) was slowly added and the reaction mixture was stirred for an additional 2 h. After, 100 mL H2O was added dropwise, and the reaction mixture was let to cool down under stirring before the addition of another 200 mL H2O. The reaction mixture was then neutralized using NaOH until pH 7 was achieved and the obtained GO was separated by filtration and washed thoroughly with water.Synthesis of Graphene-G-BP. In a round bottom flask, 1 g of graphene was dispersed in 60 mL of DMF using an ultrasonic processor  followed by the addition of 0.5 g benzoyl peroxide. The reaction mixture was heated to 80 °C under nitrogen and kept under stirring for 4 h. The product was separated by vacuum filtration and washed thoroughly with methanol and acetone.Synthesis of G-HMDA. In a round bottom flask, 1 g of graphene was dispersed in 60 mL of DMF using an ultrasonic processor  followed by the addition of 10 g HMDA. The reaction mixture was heated to 80 °C under nitrogen and kept under stirring for 4 h. The product was separated by vacuum filtration and washed thoroughly with water, methanol, and acetone before being dried.(b) Functionalization of Graphene with polymer chainsSynthesis of G-PAM. In a round bottom flask, 1 g of graphene was dispersed in 60 mL of DMF using an ultrasonic processor  followed by the addition of 10 g acrylamide and 0.1 g of LP. The reaction mixture was heated to 80 °C under nitrogen and kept under stirring for 4 h. The product was separated by vacuum filtration and washed thoroughly with DMF, water, and acetone before being dried.Synthesis of G-PGMA. In a round bottom flask, 1 g of graphene was dispersed in 60 mL of DMF using an ultrasonic processor  followed by the addition of 10 mL GMA and 0.1 g of LP. The reaction mixture was heated to 80 °C under nitrogen and kept under stirring for 4 h. The product was separated by vacuum filtration and washed thoroughly with DMF, water, and acetone before being dried.Synthesis of G-PHexylMA. In a round bottom flask, 1 g of graphene was dispersed in 60 mL of DMF using an ultrasonic processor  followed by the addition of 10 mL hexylMA and 0.1 g of LP. The reaction mixture was heated to 80 °C under nitrogen and kept under stirring for 4 h. The product was separated by vacuum filtration and washed thoroughly with DMF, water, and acetone before being dried.(c) Epoxy coating formulationEPOTEC YD 011X75  (a bisphenol-A based epoxy resin), dioctyl adipate (BASF) (as plasticizer), EPAMINE PC13 (PO.INT.ER) (as hardener), BP-183-B  (organic bentonite as rheology modifier), and IOX B-03 (LANXESS) (synthetic iron oxide-Fe3O4). The resin: hardener ratio was set at 5:1; a 1:1 toluene: acetone solvent mixture was used to facilitate the dispersion of the graphene and modified graphene either in the resin  or in the hardener component (G-HDMA) and other fillers (BP-183-B and IOX B-03). The concentration of the EMI active fillers is presented in Error! Reference source not found. The coating was applied on polyester woven material (used for EMI shielding efficiency) and on polyethylene sheets from which the epoxy films were recovered and used for the characterization of the mechanical properties. The deposition was performed by doctor blade technique to ensure a reproducible film thickness.The epoxy coating formulation involved the use of The polymer nanocomposite films were investigated by optical microscopy using an Olympus BX41 light microscope  equipped with live view E330 7.5 MP Digital SLR Camera and QuickPhoto Micro 2.3 software . The images were collected in transmission mode.The morphology of the nanocomposite films was also investigated by SEM (scanning electron microscopy) using a Tescan Vega II LMU SEM instrument  at 10 keV acceleration voltage.® 2D/3D Micro-CT Slice Visualization .The distribution of the graphene and iron oxide particles inside the epoxy matrix was examined via micro-CT technique. The SkyScan micro-CT attachment allowed for converting the Tescan Vega II LMU SEM to an X-ray microtomograph for non-destructive imaging and for measuring of the object’s internal microstructure of specimens. Analysis parameters: Exposure time—4 s per projection at electron beam currents of 100 nA; accelerating voltage—30 KeV; step size −1°; scanning time—24 min. Reconstruction was performed by the NRecon program, which used float-point data values for internal calculations during reconstruction and afterward allowed the operator to define the density window as a range of the reconstructed values. The full set of reconstruction results was visualized by the program DataViewerRaman spectra of the functionalized graphene were recorded on a DXR Raman Microscope  by 473 nm laser line.−1; samples mass, ~3 mg; temperature range, room temperature −900 °C; and heating rate, 10 °C min−1 in an alumina crucible.The thermogravimetric analyses (TGA) were performed using Netzsch TG 209 F3 Tarsus  equipment considering the next parameters: nitrogen atmosphere flow rate, 20 mL minStress-strain curves were obtained using an Instron 3382 testing machine . The samples were prepared for the tensile tests by cutting the nanocomposite films (without woven polyester). The tests were carried out according to the international standard ASTM D3039/D3039M-08. For each specimen, the rate of the extension was set at 500 mm/min, and the separation of the initial jaws was set at 50 mm (plain jaw faces). For each type of nanocomposite film, five tensile tests were carried out and the average of the measured values and the standard deviation for each point was registered. A comparative multigraph containing all the true stress/true strain values characteristic for each synthesized material was plotted to evaluate the influence of the nanofiller on their mechanical properties. This multigraph was designed to show only the curves with the closest parameters to the mean values from each set of specimens. −7 emu. For magnetic measurements, the epoxy thin film strips used were closed in small sample holders.Hysteresis loops at 300 K were measured using a SQUID magnetometer  running under the RSO (Reciprocating Sample Option) mode. This apparatus allows performing measurements with a magnetic moment resolution up to 10The ability of the synthesized materials to attenuate radar waves in the domain of 8–12 GHz was investigated as follows: A full-anechoic chamber , consistSE) is the result of three mechanisms, reflection loss, absorption loss, and multiple reflections loss.The EMI shielding efficiency  was used where iE and tE are the incident and transmitted electric fields, respectively.To calculate the The most utilized method to modify G involves obtaining GO, which was then mixed with epoxy resin and deposited on the textile material. This first stage can be used to highlight the physical compatibility between the textile and the modified epoxy resin. Thus, the textile material prior to and after the application of the modified epoxy resin coating was subjected to optical microscopy analysis.From the comparative analysis of the images in SEM analysis was performed on the two samples for a more precise analysis.From the images in Addressing the filler capacity to disperse in the resin matrix is a viable solution to further improve the coating quality. For this reason, several methods for graphene functionalization by the introduction of functional groups or polymer grafting were explored. The efficient dispersion and filler compatibility with the epoxy resin are critical aspects that affect the mechanical and microwave absorbing characteristics of the coating. The introduction of functional groups involved the oxidation of graphene, grafting of radical species obtained by the degradation of benzoyl peroxide, and the addition of hexamethylenediamine units to the graphene backbone. The polymer grafting assumed the polymerization in the presence of graphene. The selected monomers were methyl methacrylate (PMMA), glycidyl methacrylate (PGMA), hexyl methacrylate (PhexylMA), and acrylamide (PAM).Thermogravimetric analyses (TGA) were performed to confirm the functionalization of the graphene .In RAMAN spectroscopy was performed on the modified graphene to confirm the chemical modification of the graphene .−1) and G (1575 cm−1) bands. Thus, based on the ID/IG ratio  bands. Using the IG/I2D ratio . Similarly, for the G-PHexylMA derivative, a similar process is observed, despite the lower functionalization degree. This can be explained by the steric effect of the hexyl unit. In contrast, in G-PGMA and G-PMMA a higher thickness of the graphene stacks was observed. Thus, although G-PMMA and G-PHexylMA display a similar functionalization degree, the thickness of the graphene sheets is different. Another explanation for this aspect resides in the competition during the functionalization process between the radical trapping by the graphene and the homopolymerization of the monomer units. Thus, MMA is more susceptible to homopolymerization compared to hexyl methacrylate and the radical species are more stable than in the case of acrylamide. Surprisingly, in the case of the HDMA, although a high degree of functionalization was obtained, from the IG/I2D ratio a multilayer graphene structure was observed. This can be explained by the bifunctionality of the HMDA, which can be intercalated between the graphene sheets and react with both layers. A graphical representation of the obtained structures is presented in RAMAN spectra also offer information on the thickness of the graphene sheets and their variation induced by the functionalization technique can be ascertained using the ratio between the G and 2D  has both an electric and a magnetic component. Magnetism is defined as the physical phenomena produced by the moving electric charge. A magnetic field can also induce the movement of the charged particles producing an electrical current, which is the proof of the dependence between magnetism and electric conductivity. The magnetic properties of the samples were assessed to explain their electromagnetic shielding capacity . The valMB015 and MB016, respect the order for both magnetism and EMI attenuation. These low values could be explained by a higher amount of doping agent which leads to the formation of larger aggregates in the case of MB015, therefore larger “multilayer” structures by reaction of the amine group of HMDA with the oxirane of the epoxy component. In the case of MB016, although initially G-PGMA displays a “few layers” structure, the introduction in the epoxy resin could lead to aggregates by reaction of the oxirane present in PGMA with the amine component of the resin. The enhanced magnetic properties of MB014 can be explained by the increased amount of dopant presenting magnetic characteristics. Based on these explanations, a clear correlation about the determining factor for EMI shielding improvement cannot be made. Therefore, the next step consisted in the highlighting of aggregates formation by micro-CT analysis , while in the case of MB013 the good results good be attributed to an improvement of the electrical conductivity. Therefore, the electroconductive properties of the fillers counterbalance the lack of uniformity of the sample leading to a good EMI shielding efficiency. Thus, it can be concluded that G-HMDA presents an enhanced electrical conductivity compared with the unmodified graphene which is confirmed by similar examples in the literature data [The largest aggregates can be observed for sample ure data .MB013 is the most elastic sample, but it also presents the lowest mechanical resistance. This could be explained by an improved load transfer due to the presence of amino-functionalized graphene [MB014) probably due to a reinforcing effect of the filler. On the other hand, a minor influence on the improvement of the mechanical performances of these thin films was observed when doubling the concentration of G-HDMA (sample MB015). In contrast with these first three materials, the samples containing G-PGMA (MB016) displayed superior mechanical performances, reaching a maximum value of approximately 1.5 × 107 Pa tensile stress at 1.64% strain, probably due to the interactions established between the two polymers chain, the epoxy resin and the PGMA, grafted on the G surfaces, respectively. The samples containing neat G (MB017) resisted up to 1.4 × 107 Pa tensile stress, the value measured at 1% strain. The mechanical behavior of MB018 to tensile load is comparable to that of MB014. The analysis of the mechanical properties of the materials was also performed considering the application of the materials. graphene ,49. The This study highlights the synthesis and characterization of epoxy-nanocomposites containing nanofillers of unmodified and functionalized graphene. The graphene modification involved both functional groups or small molecules and polymers for compatibilization with the epoxy resin components to provide electromagnetic interference (EMI) shielding effectiveness. The first part of the study was dedicated to the modification of graphene and its characterization by RAMAN and TGA. The results confirmed the obtaining of modified graphene with different thicknesses of the graphene sheets. Samples of graphene with different thicknesses  were selected and further employed for epoxy coating formulation. The obtained nanocomposites were characterized in terms of EMI shielding effectiveness, SEM, micro-CT, magnetic properties, and stress-strain resistance. The interesting aspect is that the unmodified graphene and HMDA-modified graphene displayed the best EMI shielding properties, although the ununiformed characteristic of the nanocomposites was confirmed by the micro-CT. In addition, the epoxy nanocomposites based on HMDA-modified graphene displayed better flexibility with little difference in the EMI shielding effectiveness. The improved mechanical properties and EMI shielding characteristics make HMDA-graphene a good candidate for textile coatings with MA capacity.


The HeartPy Python toolkit for analysis of noisy signals from heart rate measurements is an excellent tool to use in conjunction with novel wearable sensors. Nevertheless, most of the work to date has focused on applying the toolkit to data measured with commercially available sensors. We demonstrate the application of the HeartPy functions to data obtained with a novel graphene-based heartbeat sensor. We produce the sensor by laser-inducing graphene on a flexible polyimide substrate. Both graphene on the polyimide substrate and graphene transferred onto a PDMS substrate show piezoresistive behavior that can be utilized to measure human heartbeat by registering median cubital vein motion during blood pumping. We process electrical resistance data from the graphene sensor using HeartPy and demonstrate extraction of several heartbeat parameters, in agreement with measurements taken with independent reference sensors. We compare the quality of the heartbeat signal from graphene on different substrates, demonstrating that in all cases the device yields results consistent with reference sensors. Our work is a first demonstration of successful application of HeartPy to analysis of data from a sensor in development. Wearable sensors are an expanding field of research, with growing applications in telehealth , fitnessHeart rate signals from photoplethysmogram (PPG) measurements are known to contain noise, large amplitude variations and a broad variation of peak morphology . The HeaThe discovery of graphene  has spurHeartbeat signals contain the value of numerous physiological parameters that are important health indicators. The most commonly extracted value, heart rate, is defined as the number of heartbeats per minute. Heart rate variability (HRV) is the fluctuation in the time intervals between adjacent heartbeats . AnotherThe paper is organized as follows. In LIG was produced by scanning a e, as in . The lasScanning electron microscopy (SEM) with energy dispersive X-ray spectroscopy (EDS) was done with a PhenomProX electron microscope . Reported elemental compositions are obtained as the average over several positions recorded with a magnification of ×1000, where EDS was performed over the entire field of view. The Raman spectra of the samples were recorded with a DXR Raman microscope . The samples were excited with a diode laser at a wavelength of 532 nm and a power of 10 mW focused on a 2.1 Graphene devices were attached by adhesion to the arm of a still subject at the position of the median cubital vein, as in The signals were analyzed with the HeartPy Python Toolkit . Two difThe obtained signals differ in quality from one measurement instance to another, depending on two factors—on the exact sensor positioning on the subject’s arm, and on the stillness of the subject. In the case of an extremely noisy signal, preprocessing was performed before applying the HeartPy process function. Noisy input signals were normalized to zero mean and unit variance. Filtering techniques such as low pass filtering and high pass filtering were applied to eliminate high frequency noise from the signal. Specifically, 8th order Butterworth band-pass filtering was performed .In order to identify peaks in the heartbeat with HeartPy, the process function starts by calculating the moving average using a window of 0.75 s on both sides of each data point. Next, regions of interest (ROI) are defined as regions between the two points of intersection where the amplitude of the signal is larger than the moving average. The process function uses two approaches to identify the location of a peak. In the first approach, the maximum value in the marked ROI is taken as the peak position, while in the second approach a univariate spline is used to upsample and interpolate the ROI, and solved for its maximum . A speciSEM imaging of LIG on PI reveals a highly developed porous network a,b. EDS −1, and a very well developed 2D region  is an aggregate measure, calculated as the average beat–beat interval across the entire analyzed signal (segment), resulting in a very robust algorithm. The BR by HeartPy is obtained by exploring the property of the heart that the frequency of the heartbeat is strongly influenced by breathing. There is a strong relationship between the breathing and the intervals between the heartbeats . HeartPy upsamples the list of detected R–R intervals by interpolation, followed by extraction of breathing peaks in the signal .The HeartPy process function also returns 11 more parameters—7 are in the time domain, and 4 are non-linear measurements. The time-domain parameters are computed on the detected and accepted peaks in the R–R intervals. These time-domain indices of HRV quantify the amount of variability in measurements of the inter-beat interval (IBI), which is the time period between successive heartbeats. SDNN is the standard deviation of IBI, measured over normal sinus beats , while SDSD is the related standard deviation of successive R–R interval differences, only representing short-term variability. RMSSD is the root mean square of successive differences between normal heartbeats. It is obtained by calculating and squaring each successive time difference between heartbeats and averaging the result before the square root of the total is produced. Parameters pNN50/pNN20 represent the percentage of adjacent normal sinus beats that differ from each other by more than 50/20 ms . The funBPM—the number of heartbeats per minute, the HR;Breathing rate in Hz, that multiplied by 60 gives the number of breaths per minute, the BR.In a nutshell, the used HeartPy process function produces the following parameters:Time-domain measuresIBI—inter-beat interval, the mean distance of intervals between heartbeats;SDNN—the standard deviation of intervals between heartbeats ;SDSD—the standard deviation of successive differences between adjacent R–R intervals;RMSSD—the root mean square of successive differences between adjacent R–R intervals;PNN50/PNN20—the proportion of differences greater than 50 ms/20 ms;MAD—median absolute deviation of R–R intervals;Non-linear measuresSD1, SD2, S, SD1/SD2—Poincare plot analysisThe experiments were conducted with a graphene sensor attached to the forearm of a still sitting subject. Reference values were measured with a smartphone app that uses the device camera to record HR, applying optical methods. In this study experiments were conducted with 3 different versions of the graphene patch. The presented results are representative examples of signals obtained from the three different patch versions. The results were reproducible in 90% of the cases. The reproducibility and the quality of processed signals mainly depend on the precise positioning and fixture of LIG-based sensors. The tighter the sensor is applied to the cubital vein, the higher the signal quality.First experiment—pure laser-induced graphene on PI;Second experiment—graphene on PI protected by a PDMS layer on top;Third experiment—graphene completely transferred to PDMS.The experiments are classified as follows:The measurements from the first experiment are depicted in The measurements and the analysis for the second experiment are presented in An overview of the 13 extracted parameters from HeartPy analysis toolkit, with the corresponding values for the all three experiments are presented in It is evident that the values of the HRV time-domain and non-linear measurements from the process function mostly have risen as the graphene is further treated. The standard deviations SDNN, SDSD, and the median absolute deviation (MAD), show less reliable data in the second and the third experiment, compared to the first one. Nevertheless, the obtained values for HR and BR are within the acceptable range, as presented in The as-made LIG devices (first experiment) yield signals of the best quality of the three experiments, whereas the LIG devices completely transferred to PDMS (third experiment) yield signals with highest indicators related to noise. Since the Raman spectra measurements have indicated that the quality of the graphene does not decrease with treatment, the origin of the decrease of signal quality in the composite sensor is likely due to imperfect conformity of graphene to the PDMS layer and the increased mechanical stiffness of the samples that include PDMS.The obtained HRV time-domain measures and the Poincare analysis measurements are within the range expected from heartbeat analysis .In the above sections, we have shown that LIG wearable sensors can be used to reliably collect physiological data that can be processed with an easily accessible HeartPy open source toolkit. Wearables can be used to perform several basic functions: sense, analyze, store, transmit, and utilize data . ProcessOn their part, the graphene sensors have shown to be easy to make, inexpensive, and operable on various substrates, including PDMS which is often used as a platform for wearable electronic health patches. LIG heartbeat sensors are very sensitive, and can suffer from noise that is induced when the subject moves. This high-frequency noise needs to be removed in post-processing with functions such as filtering, which introduces extra processing steps.The main contribution of our work is that it demonstrates that a free, open-source toolkit for heartbeat analysis such as HeartPy can be used in conjunction with any heartbeat sensor, including ones in development, such as the one we developed from graphene. This will allow laymen to delve into developing sensor applications, by using, e.g., commercial off-the-shelf piezoelectric thin film sensors in conjunction with HeartPy. The combination of easy to make, inexpensive graphene heartbeat sensors and a widely available open access HeartPy toolkit for data analysis has potential for use by medical researchers or in finalized devices.To conclude, we have demonstrated effective heartbeat sensors that consist of graphene-based sensing elements on several flexible and biocompatible substrates, in conjunction with analysis with the open-source package HeartPy toolkit for Python. The LIG signals were processed with two HeartPy modules—heartpy and heartpy.filtering. The HeartPy package is an excellent tool that was designed mainly for evaluating heart rate signals from PPG data. Nevertheless, we have shown that HeartPy analysis can be conducted on the LIG signals as well, especially in obtaining the HR and BR parameters.This combination of hardware and software presents a relatively low barrier of entry for novice developers of heartbeat sensors, opening a path for widespread experimentation and application. Future work may address the biocompatibility, reusability, and price of the substrates and materials used, in order to bring solutions closer to the market. In addition, future research may exploit the advantages of big data to optimize the use of HeartPy with graphene-based sensors.


Automated question classification worked well for classifying the interviewers’ questions allowing interventions to improve interview quality.Previous research has shown that simulated child sexual abuse (CSA) interview training using avatars paired with feedback and modeling improves interview quality. However, to make this approach scalable, the classification of interviewer questions needs to be automated. We tested an automated question classification system for these avatar interviews while also providing automated interventions (feedback and modeling) to improve interview quality. Forty-two professionals conducted two simulated CSA interviews online and were randomly provided with no intervention, feedback, or modeling after the first interview. Feedback consisted of the outcome of the alleged case and comments on the quality of the interviewer’s questions. Modeling consisted of learning points and videos illustrating good and bad questioning methods. The total percentage of agreement in question coding between human operators and the automated classification was 72% for the main categories (recommended vs. not recommended) and 52% when 11 subcategories were considered. The intervention groups improved from first to second interview while this was not the case in the no intervention group (intervention x time:  Given tIn order to support children’s reporting accuracy, it is important to avoid leading questions as these can lead to inaccurate statements . A studyUnfortunately, most attempts at training interviewers have not been effective. An exception is the work of Michael E. Lamb and colleagues e.g., , showingvia computers or smartphones. This allows both remote and face-to-face training. In addition, so far, Avatar Training is the only training with evidence of the changes in interviewer behavior obtained within the simulations transferring to interviews with actual children both in a mock-victim  and has convincing support for its efficacy, a problem remains. In all of the studies described above, an operator has coded the questions asked by the participants and input this information into the software after which the response algorithms have taken over. In order for the training approach to be truly scalable, it is necessary to automate this process, that is, the operator needs to be replaced by an automated AI classification of the questions asked. Given that the approach to classify questions developed by Most research on training with avatars has so far used university student samples. However, given that professional groups may be expected to perform better given their education and experience  while using automated question classification.We had two hypotheses:We expected it to be possible to classify interviewer questions at a higher than chance level using an automated classifier developed based on training data taken from previous experiments.We expected there to be an interaction between time and intervention as a result of participants in the intervention groups improving from first to second interview while this was not expected to happen in the control group. Planned comparisons were used to investigate in which groups’ improvements occurred and which of the groups differed significantly during the second interview.2.2.1.Mage = 37.1, SD = 7.1) who completed the experiment for a compensation of 2,000 JPY (13 participants were not compensated due to their employer’s regulations prohibiting this). They consisted of clinical psychologists (n = 16), police personnel (n = 8), child guidance office staff (n = 4), hospital workers (n = 4), educational facility workers (n = 3), and others who preferred not to specify their affiliations (n = 7). Twenty-four (57%) had taken training course(s) in child interviewing, and 30 (71%) had experience of interviewing children. They were randomly allocated into either the no intervention (n = 14), the feedback (n = 15) or the modeling group (n = 13). The Research Ethics Committee of the Faculty of Psychology of Meiji Gakuin University (Japan) approved the study (20210031) before the data collection commenced.The participants were 42  professionals (2.2.n = 14), the feedback (n = 15), or the modeling (n = 13) group.The present study employed a 2  x 3  mixed design. Participants were randomly assigned to either the no intervention (2.3.2.3.1.https://youtu.be/hb9knAMzrds). The simulated interview was conducted in a question-and-answer format between the interviewer and the AI Avatar. The interviewer started recording a question by clicking the recording button presented below the avatar and ended the recording by clicking the end recording button. The recorded question was then transcribed using the Google Cloud Speech-to-Text API and sent to the N-gram extraction step. Next, the extracted N-gram was processed by a question classification algorithm based on a machine learning model and the answer selection algorithm then chose and played an avatar answer to the interviewer.To create an avatar that automatically responds to the interviewer, we developed an AI model (hereafter referred to as AI Avatar) that classifies the interviewer’s questions and selects appropriate answers without human assistance. The interview simulation application was built on the Microsoft Azure server and it was available 24/7 during the study period. 2.3.2.Of the total of 16 avatars available in the AI Avatar system, two avatars were allocated to each of the eight patterns of features with different ages (4 or 6 years old), gender (girl or boy), and abuse status (abused or non-abused). Two avatars (a 4-year old non-abused boy and a 6-year old abused girl) were used in the modeling intervention as examples of good and bad interviews and were not used in the simulated interviews.2.3.3.The question classification algorithm of the AI Avatar categorized the transcribed questions into one of 11 possible types as described in previous studies see . The algIn addition to machine learning, a series of additional rules were implemented in the question classification algorithm to improve the accuracy of question classification. For example, questions that could be classified with high accuracy by a specific character pattern, such as a greeting  at the beginning of an interview were processed by a specifically set if-then rule instead of a machine learning model. In addition, there are questions that should have different classification results depending on keywords that suggest abusive experience even if the question otherwise resembles a recommended question type. For example, the question “Tell me about someone hurting you” should be classified as suggestive-open rather than as an invitation-focus question in case the child has not mentioned anything about a painful experience. To obtain an accurate classification for these questions, a list of suggestive keywords was created based on the training data and was used to determine the presence of suggestive keywords in a question. Furthermore, to detect the repetition of questions, a Jaccard coefficient was calculated as the similarity of the N-gram patterns between consecutive questions for determining whether a question was a repetition of the immediately preceding question. The relationship between the two questions can be represented by a 2×2 contingency table. The Jaccard coefficient is a measure of similarity that does not consider cases where attributes (N-gram patterns in the present study) were not present in either of the two sample sets (questions in the present study) as similarity.This characteristic of the Jaccard coefficient fits the data structure of the present study, in which the frequency of each N-gram pattern is 0 for most of the questions contained in the training data. The threshold value of the Jaccard coefficients was set considering the classification accuracy of the repetition of questions in the training data. In addition, to make the avatar’s responses resemble those of a real child, topic keywords  corresponding to predefined responses  of each avatar were tagged on the input question  when contained in a recommended question. AI Avatar had lists of avatar responses with their presentation order number corresponding to each keyword tag. An avatar response with the lowest number among unplayed ones on the response list of a tagged keyword was played to give the interviewer an answer that met the context of the question.2.3.4.The answer selection algorithm implemented in the AI Avatar adopted the method used in previous research on Avatar Training , 2021. IEach avatar had nine relevant details in their memory, and these were provided one at a time. The relevant details were provided in a fixed order with the last four containing the information necessary to reach the correct conclusion about both abused and non-abused cases. Therefore, the difficulty of reaching the correct conclusion was comparable between abused and non-abused cases. Incorrect details (details that contradicted the information held by the avatar) could be created by the interviewer by asking not-recommended questions. The information held by the avatar is predefined as a set of responses, and inconsistencies with them were defined as erroneous information. For example, if the interviewer asked an avatar that does not have information about any abusive experience “Did your dad hurt you?,” and the response algorithm returns “Yes,” an incorrect detail has been created. The answer selection algorithms were developed based on findings of experimental research on children, analyses of records of child interviews in legal settings, and theoretical research on children’s memory. Previous research using these algorithms , 2021 ha2.4.The mean length of experimental sessions was 52.1 min (SD = 14.4). After completing the informed consent and demographic information forms, the participants read short instructions see  on best Participants conducted simulated interviews with randomly selected two avatars out of the 14 available. Before each of the interviews, the participants first read a background scenario  the presence of the abuse (“present” or “absent”), (2) confidence in their assessment on a 6 point scale (“50%: guessing” to “100%: completely sure”), and (3) a description of what, according to them, had happened to the avatar. Unfortunately, the answers to these questions were missing for 27 (64%) participants due to a system error. Therefore, the correctness of conclusions was not included in the statistical analyses.Between the two interviews, participants received either no intervention or either feedback or modeling as a training intervention. As the feedback intervention, the participants were provided two types of feedback after the first interview: (1) feedback consisting of the outcome of the case and (2) feedback on the questions (two recommended questions and two not recommended questions) asked in the interview. For feedback concerning questions, the AI avatar chose questions randomly from the questions recorded during the interview and then provided automated feedback regarding them. The modeling intervention included (1) reading a series of learning points of good and bad questioning methods and (2) watching a total of four 2.5-min videos of good and bad interviews with both an abused and a non-abused avatar. The contents of the modeling intervention were the same as those in 2.5.The fourth and fifth authors of this paper recoded 48%  of completed interviews in the present research. They were also involved in coding questions in all three data sets  x 3  mixed design two-way MANOVA was performed on the number of recommended questions, the number of not recommended questions, the proportion of recommended questions, number of relevant details, and number of incorrect details. Planned comparisons were conducted to identify where exactly the significant differences appeared.3.3.1.p < 0.001, 95% CI . This can be considered moderate agreement , with a Cohen’s kappa (κ) of.42, greement .p < 0.001, 95% CI . This can also be considered moderate agreement  of questions between operators and the AI Avatar was 72% (chance level = 33%), with a Cohen’s kappa (κ) of.49, greement . These r3.2.For the first interview (before the interventions), the overall means were 9.8 (SD = 5.4) for the number of recommended questions, 19.9 (SD = 8.1) for the number of not recommended questions, 34.4 (SD = 17.6) for the proportion of recommended questions, 1.2 (SD = 1.5) for the number of relevant details, and 5.2 (SD = 2.8) for the number of incorrect details.3.3.r = −0.08, p = 0.482.3.4.F  = 9.90, p < 0.001, pη2 = 0.59, 1−β = 1.00, and for the interaction between intervention and time, F  = 2.72, p = 0.007, pη2 = 0.28, 1−β = 0.95, while there was no significant main effect for intervention, F  = 1.01, p = 0.444, pη2 = 0.13, 1−β = 0.49. The significant interaction supports our second hypothesis.Significant effects on the combined dependent variables were found for time, F  = 46.95, p < 0.001, pη2 = 0.55, 1−β = 1.00, the proportion of recommended questions, F  = 24.45, p < 0.001, pη2 = 0.39, 1−β = 1.00, and the number of relevant details, F  = 6.65, p = 0.014, pη2 = 0.15, 1−β = 0.71, but not for the number of not recommended questions, F  = 2.36, p = 0.132, pη2 = 0.06, 1−β = 0.32, and the number of incorrect details, F  = 0.51, p = 0.479, pη2 = 0.01, 1−β = 0.11.Univariate level significant effects of time emerged for the number of recommended questions, F  = 3.50, p = 0.040, pη2 = 0.15, 1−β = 0.62, but not for the number of recommended questions, F  = 1.38, p = 0.264, pη2 = 0.07, 1−β = 0.28, the number of not recommended questions, F  = 0.40, p = 0.674, pη2 = 0.02, 1−β = 0.11, the proportion of recommended questions, F  = 0.71, p = 0.499, pη2 = 0.04, 1−β = 0.16, and the number of incorrect details, F  = 0.30, p = 0.742, pη2 = 0.02, 1−β = 0.09.At a univariate level, significant effect of intervention emerged for the number of relevant details, F  = 9.62, p < 0.001, pη2 = 0.33, 1−β = 0.97, the number of not recommended questions, F  = 12.25, p < 0.001, pη2 = 0.39, 1−β = 0.99, the proportion of recommended questions, F  = 12.10, p < 0.001, pη2 = 0.38, 1−β = 0.99, and the number of incorrect details, F  = 3.97, p = 0.027, pη2 = 0.17, 1−β = 0.68, but not for the number of relevant details, F  = 1.43, p = 0.251, pη2 = 0.07, 1−β = 0.29. These interactions partly support our second hypothesis.Univariate level significant interaction between time and intervention emerged for the number of recommended questions, Significant statistical differences in the planned comparisons between groups for the first and second interviews are also shown in 4.4.1.The present study examined the accuracy of question classification by AI and the effectiveness of CSA interview simulations using AI-based question classification combined with two interventions (feedback and modeling) shown to be effective in previous research. Regarding the question classification, higher-than-chance-level classification accuracy was obtained both for 11 question types and for their two supra-categories (recommended and not recommended), indicating that AI worked well for classifying the interviewers’ questions. Question classification accuracy is an important factor in mimicking the experience of interviewing a real child, where recommended questions elicit informative utterances from a child, while not recommended questions elicit shorter responses sometimes even containing erroneous details. In the AI Avatar interviews, randomly chosen questions were used for the feedback intervention. Therefore, the accuracy of question classification was also required to provide accurate feedback. Importantly, the number of recommended questions significantly increased in the feedback group, suggesting that feedback improved participants’ questioning skills. Given that the discrimination between recommended and not recommended questions is essential for feedback to be effective in enhancing use of recommended questions, the classification accuracy of over 70% for these two categories is an important result of the present study.4.2.Importantly, the simulated interviews with AI Avatars could be used to improve interviewing skills and quality of the information elicited from the avatars when combined with feedback and modeling interventions. The increase in the number of recommended questions shown in the feedback group has also been demonstrated robustly in a mega-analysis  that comModeling resulted in more robust improvements than feedback, including a decrease of not recommended questions and increase of relevant details, with many significant differences compared to the no intervention group. These improvements are consistent with a previous study that examined modeling , which svia voice call  found here is also consistent with ice call , 2021 inice call , additiovia texts. However, in the current study, the AI Avatar chose the questions and provided feedback in text format. A systematic review of the effects of online learning from automated feedback , future improvement is desirable. As a potential approach to improve the question classification accuracy, examining more advanced models such as the Bidirectional Encoder Representations from Transformers BERT;  and compIt should also be noted that only the results of a single interview  could show intervention effects. This may not have been a problem for the modeling intervention as previous research supports the efficacy of a single administration . HoweverAlthough the present research showed robust improvements using modeling, a need to test the transfer of this learning effect is necessary separately for modeling interventions. As described above, the interviewer was free to ask questions without restrictions and avatar responses were chosen from a set of responses with predefined selection probabilities following each question type. This environment, which provides a variety of contexts, encourages interviewers to customize the questions learned from the model flexibly and may develop questioning skills that can be applied to interviews with real children. Given that several factors  were pointed out to maximize transfer regarding behavioral modeling , the traUnfortunately, we could not use conclusion correctness in our analyses. However, this is less of a problem than it may seem given that a correct conclusion is made possible by uncovering the memories of the avatar while not eliciting incorrect information from it. These latter variables were measured and shown to be impacted by the interventions.Moreover, automated and scalable training needs to be investigated using a larger sample. Previous research on interview training has generally only used up to a hundred participants in total due to the need to conduct individual training sessions for each participant. However, the automated training protocol used in the present research can train several participants in parallel by adjusting the available number of accesses and processing capacity of the server accessed by the participants. To provide potential trainees with more training opportunities regardless of time and place, future research is needed to further improve the AI Avatars.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Research Ethics Committee of the Faculty of Psychology of Meiji Gakuin University. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.SH and PS contributed to the conception, design of the study, and wrote the first draft of the manuscript. SH and TI collected the data. SH performed the statistical analyses. All authors contributed to the interpretation of the analyses, revision of the manuscript, edited and gave final approval for publication, and were accountable for this work.This research was supported by grants from HAYAO NAKAYAMA Foundation for Science & Technology and Culture (grant number: R1-A1-29) and the NYU Shanghai Start Up Fund.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.


PFKP, STMN2, CFP, KIAA1324 and TRHDE were common targets and were also differentially expressed in TDP-43-immunonegative neurons. DEG enrichment analysis revealed diverse biological pathways including immune and synaptic functions. Common DEU events in human datasets included well-known targets POLDIP3 and STMN2, and novel targets EXD3, MMAB, DLG5 and GOSR2. Our interactive database (https://www.scotterlab.auckland.ac.nz/research-themes/tdp43-lof-db/) allows further exploration of TDP-43 DEG and DEU targets. Together, these data identify TDP-43 targets that can be exploited therapeutically or used to validate loss-of-function processes.TDP-43 proteinopathy is the major pathology in amyotrophic lateral sclerosis (ALS) and tau-negative frontotemporal dementia (FTD). Mounting evidence implicates loss of normal TDP-43 RNA-processing function as a key pathomechanism. However, the RNA targets of TDP-43 differ by report, and have never been formally collated or compared between models and disease, hampering understanding of TDP-43 function. Here, we conducted re-analysis and meta-analysis of publicly available RNA-sequencing datasets from six TDP-43-knockdown models, and TDP-43-immunonegative neuronal nuclei from ALS/FTD brain, to identify differentially expressed genes (DEGs) and differential exon usage (DEU) events. There was little overlap in DEGs between knockdown models, but This article has an associated First Person interview with the first author of the paper. Summary: A meta-analysis and interactive database of TDP-43 target genes and splicing events identified in publicly available RNA-sequencing datasets from six TDP-43 knockdown models and TDP-43-immunonegative neurons in amyotrophic lateral sclerosis/frontotemporal dementia. TARDBP) is a predominantly nuclear DNA- and RNA-binding protein first discovered to bind to the trans-active response element in the human immunodeficiency virus (HIV)-1 sequence  that almost all inherited ALS including minantly ; (2) thaminantly ; and (3)minantly . Recogniminantly . Nuclearminantly  Fig. 1)TARDBP-muminantly . Howeverminantly . These iminantly . Nuclearminantly , so lossminantly  minantly . Notablyminantly . Thus, lRANBP1 and POLDIP3, are clearly reproducible in multiple studies ; GSE116456 . An additional RNA-seq dataset was identified in which neuronal nuclei had been sorted from ALS/FTD tissue according to nuclear TDP-43 immunoreactivity [GSE126543 ]; however, only six of these met quality control thresholds and were fully processed . Of thesTARDBP/Tardbp was examined in HeLa, SH-SY5Y, ihMN, mouse striatum, mouse mammary gland and rat astrocytes .There were three upregulated differentially expressed genes (DEGs) and five downregulated DEGs shared by all human-derived TDP-43 knockdown models, and nine upregulated and three downregulated DEGs shared by all rodent-derived TDP-43 knockdown models B. There KIAA1324, CFP and ITGA4. The shared downregulated DEGs were TRHDE, PFKP and MASP2 <0.05] in each model are listed in Tables S2 and S3 (only genes with log2FC between −0.5 and >0.5), or can be explored interactively at https://www.scotterlab.auckland.ac.nz/research-themes/tdp43-lof-db/ , filtering by model system or gene of interest. Overall, DEGs were highly variable between datasets, but the sharing of DEGs between model systems validates them as conserved TDP-43 targets, and sharing of DEGs with ALS/FTD TDP neg neuronal nuclei validates those DEGs as being disease relevant.The upregulated DEGs shared by the largest number of TDP-43 knockdown models and with ALS/FTD TDP neg neuronal nuclei were nd MASP2 D. DEGs [Table S4.Having identified certain genes with conserved patterns of regulation by TDP-43 between cell types and species, we examined the effect of TDP-43 knockdown on biological processes (BPs), molecular functions (MFs) and cellular components (CCs) using gene ontology (GO) enrichment analysis. The top three significant terms for BPs, MFs and CCs for both upregulated genes , left anTwo patterns emerged after redundancy was eliminated from the GO terms: many terms were related to immune response  using the blastn algorithm, to examine sequence homology of DEU loci between human and rodent datasets. Only two genes showed a DEU event that was shared between ALS/FTD TDP neg neuronal nuclei and a rodent dataset, and that also showed sequence homology of the region between human and mouse: STMN2 and POLDIP3 , the 3′ UTR  and a lncRNA (TBL1XR1-AS1)  . We consTARDBP mRNA targets drives pathogenesis and phenotype remains unclear. Central to this is the need to identify the complement of TARDBP mRNA targets in distinct cell types and model systems, and to verify which of these targets is regulated by TDP-43 in human ALS/FTD neurons. We approached this problem by assessing TARDBP mRNA targets in human and rodent models, in neuronal and non-neuronal cell types, and identifying shared and unique targets and biological pathways under TDP-43 control. TDP-43 regulates certain common targets across myriad cell types, implying that TDP-43 proteinopathy in non-neuronal cells initiates processes that at least partially overlap with those occurring in neurons. Notably, TDP-43-dependent changes identified in multiple model systems were validated to change in human ALS/FTD neuronal nuclei with TDP-43 nuclear depletion, supporting TDP-43 loss of function as being pathomechanistic.As the common neuropathological feature in ALS with or without FTD, aggregated TDP-43 and its acquired toxic functions represent tractable molecular targets for therapy. Yet, increasing evidence suggests that successful therapies will also require rescue of normal physiological functions of TDP-43 that are compromised in disease. Loss of gene expression and splicing regulatory function are now widely accepted features of ALS with TDP-43 proteinopathy, but how the specific complement of PFKP mRNA has been identified as a TDP-43 loss-of-function marker by several studies in addition to those in our meta-analysis . This cryptic exon may therefore be preferable to overall PFKP transcript levels as a TDP-43 loss-of-function marker.Certain transcripts were robustly regulated by TDP-43 in multiple datasets and in human ALS/FTD neurons with nuclear TDP-43 loss. Decreased analysis . PFKP en miR-520 . Yet, decontrols . Howeverdirectly , and TAReraction . Indeed,STMN2 expression following TDP-43 depletion is associated with the emergence of a cryptic exon after exon 1, which introduces a premature polyadenylation site  is an M1 family metallopeptidase enriched in brain regions  and endosome/lysosome-associated apoptosis and autophagy regulator (ELAPOR1) in humans (Elapor1 or insulin inhibitory receptor (Iir) in mice  the cell types in which the transcript is expressed, and whether those cell types are subject to TDP-43 dysfunction in the tissue sampled; (2) whether the transcript is subject to secondary regulation, aside from regulation by TDP-43; and (3) whether regulation by TDP-43 is direct or indirect via other mediators also subject to change. Overall, the ability of DEGs to report upon TDP-43 function is likely to differ across different transcriptomic profiles .POLDIP3). The skipping of exon 3 in POLDIP3 is a frequently reproduced marker in TDP-43 knockdown studies  of ALS/FTD tissue, in conjunction with cell-type-specific immunohistochemical markers, to identify additional cell types with TDP-43 loss of function. This panel of DEU events could also act as primers/probes for quantitative RT-PCR to assess the fidelity of ALS models or the restoration of TDP-43 function by therapeutics.Half of the DEU events common to all human datasets were modifiers of 3′ UTR usage. The 3′ UTR is an important regulatory region, of mRNA stability , localizPOLDIP3)  (GOSR2) (IL18BP)  (EXD3) (USP31)  , mitocho NDUFA5) , endosom (GOSR2) , immune (IL18BP) , microtu3, DLG5) , exonucl) (EXD3)  and deub (USP31) . How theSeveral of our data suggest our DESeq2 and DEXSeq analyses to be conservative methods for identifying DEGs and DEU events, and comparative studies of DEG packages have also demonstrated DESeq2 to err on the conservative side . It is eSTMN2 that has attracted significant attention in ALS/FTD research . The search was performed using the keyword ‘TDP-43’, and the results were filtered by setting Entry Type as ‘Series’ to capture all potential samples that belonged to a common study, and setting Study Type as ‘Expression profiling by high throughput sequencing’. Inclusion criteria for re-analyzing these datasets were as follows: (1) raw RNA-seq data available; (2) at least three TDP-43 knockdown samples and two appropriate control samples; (3) experimental depletion of TDP-43. In addition, an RNA-seq dataset was identified in which neuronal nuclei had been sorted from ALS/FTD tissue according to nuclear TDP-43 immunoreactivity; either TDP pos  or TDP neg (no nuclear TDP-43) (A repository search was conducted in September 2020 to identify TDP-43 knockdown studies with available RNA-seq data using the Gene Expression Omnibus (GEO) from the NCBI ( TDP-43)  Fig. 2)http://nchttp://www.ncbi.nlm.nih.gov/sra), and quality control was applied using FastQC v0.11.9 software (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Details of each dataset including study design, GEO accession number and library information are supplied in Table S1. Code is deposited at https://github.com/mcao051/TDP_LOF. Raw data that still contained adapter sequence content were trimmed with Trimmomatic v0.39  was used for GO enrichment analysis using genes with Padj<0.05 and log2FC of either <−0.5 or >0.5. The genes detected after low-count filtering for each dataset were used as the background gene list. Redundant GO terms were eliminated using the ReViGO tool available at http://revigo.irb.hr/.The data processing pipeline is shown in ic v0.39 . If feweic v0.39 . Referenic v0.39  to perfoic v0.39  and DEU ic v0.39 . DEU is ic v0.39 . DESeq2 https://www.scotterlab.auckland.ac.nz/research-themes/tdp43-lof-db/. They enable the reader to visualize and interact with all differential gene expression and exon usage analyses described herein, including genes of interest not highlighted in our study.Interactive graphical reports were generated from the packages Glimma  and DEXShttps://ggplot2.tidyverse.org/), ggvenn (https://CRAN.R-project.org/package=ggvenn), the plotCounts function from within DESeq2 (https://www.r-project.org/), Prism 9.0 software  and Integrative Genomics Viewer (Data were visualized using R software with ggplot2 (n DESeq2 , DEXSeq s Viewer . Adobe P


An optical fiber Fabry–Perot interferometer (FPI) is constructed for relative humidity measurement by fusion splicing a short hollow core fiber (HCF) to the end of a single-mode fiber and coating the tip of the HCF with a layer of gelatin. The thickness of the gelatin film changes with ambient humidity level and modulates cavity length of the FPI. Humidity measurement is therefore realized by measuring the wavelength shift of the interreference fringe. RH sensitivity of 0.192 nm/%RH is achieved within a measurement range of 20–80%RH. Dynamic measurement shows a response and recovery time of 240 and 350 ms, respectively. Sensor performance testing shows good repeatability and stability at room temperature but also reveals slight dependence of the RH sensitivity on environmental temperature. Therefore, a fiber Bragg grating is cascaded to the FPI sensing probe to monitor temperature simultaneously with temperature sensitivity of 10 pm/°C. Humidity measurement is widely needed in the many fields such as food, medicines, storage and environmental monitoring. High-performance humidity sensors with wide measurement range, high accuracy and fast response speed are desired. Since conventional humidity sensors based on electronic technologies are hardly used for long-distance measurement or in strong electromagnetic-interference environments, optical fiber humidity sensors have attracted a growing number of research interests in recent years because of their long-distance signal transmission capability and immunity to electromagnetic interference.An optical fiber humidity sensor is usually composed of an optical fiber structure interacting with a humidity-sensitive material. The optical fiber structure can be a fiber grating ,2,3,4, fIn this paper, an optical fiber interferometric humidity sensor by using a short hollow core fiber (HCF) interacting with a gelatin film is demonstrated. It is constructed by fusion splicing a short length of HCF to the end of a single-mode fiber (SMF) and coating the tip of the HCF with a layer of gelatin to form a Fabry–Perot interferometer (FPI). Humidity changes the thickness of the gelatin film that modulates the interference cavity length and hence the dip wavelength of the interference spectrum of the FPI in turn. A relative humidity (RH) measurement with a sensitivity of 0.192 nm/%RH is therefore achieved within a measurement range of 20–80%RH and with the response and recovery times of 240 and 350 ms, respectively. It is found that the RH sensitivity shows a slight dependence on environmental temperature. A fiber Bragg grating (FBG) is therefore cascaded in the FPI sensing probe to monitor temperature changes simultaneously.A schematic diagram of the proposed optical fiber FPI humidity sensor probe is shown in The working principle for humidity measurement is based on the humidity-induced thickness change in the gelatin film that changes the cavity length of the FPI formed by the two reflective surfaces, as shown in Based on the theory of optical interference, intensity of the reflected light of the FPI probe can be derived by using the two-beam approximation and expressed as :(1)I=I0[When the external humidity level increases, the gelatin film will absorb more water molecules from the surrounding air and expand in volume. Its thickness will be increased accordingly, which will reduce cavity length of the FPI and shift the dip wavelengths of the interference spectrum to the short wavelength direction. When the external humidity level decreases, the gelatin film will release water molecules to the surrounding air and shrink in volume. Its thickness will be decreased and the cavity length of the FPI will become larger to make the dip wavelengths of the interference spectrum move to longer wavelengths. The changes in dip wavelengths of the FPI can be expressed by:The experimental setup of the humidity measurement is shown in Three FPI probes with different HCF lengths of 73, 110 and 220 μm were fabricated, and their interference spectra are shown in The humidity level was increased at a step of 5%RH within the range of 20 to 80 %RH. We tested the responses of the FPI probe in both ascending and descending orders of RH level. The measured wavelength shifts in the FPI probe against RH in both orders as shown in −2, indicating quite good repeatability for the proposed FPI humidity probe. The minor errors among different measurements are most possibly caused by the nonuniform RH distribution and unstable airflows in the THCC.To further study the repetitive characteristics of the FPI humidity probe, two more iterations of repetitive testing were carried out. The measured results of dip wavelength shift against RH level in both ascending and descending orders are shown in To study the effect of environmental temperature on the response of the FPI humidity probe, the testing was carried out under two more different temperatures of 15 and 25 °C. The measured results, together with that measured at 20 °C, were shown in Then, the stability test of the FPI probe against time was carried out at three different RH levels of 20, 40 and 80%RH under a fixed temperature of 20 °C. The dip wavelength was measured every 5 min within a period of 30 min, and the results are shown in The dynamic response of the FPI probe to humidity change was then carried out by changing the humidity level from 50 to 80%RH quickly and then changing it back after 10 s. We set the OSA to automatic acquisition mode to record the wavelength shift of interference spectrum of the FPI probe at a high speed. The measured results, as shown in The fast response and recovery time may be attributed to the compact structure of the FPI probe and the small thickness of the gelatin film, which allow the fast diffusion of water vapor molecules. Based on the fast response time, the FPI probe provides a near-real-time humidity measurement technique which can be used in areas such as human breathing monitoring for the improvement of healthcare, as the response time of current commercial humidity sensors is in the order of several seconds.2 = 0.99723. Since the FBG is not sensitive to RH variations, it can provide the accurate information of environmental temperature for reference when the FPI humidity probe works.Finally, we tested the sensitivity of the FBG to temperature within a temperature range of 10 to 50 °C. The measured wavelength of the FBG’s reflection peak against temperature are shown in To better evaluate the proposed FPI humidity sensor, we compare the key performances including sensitivity, measurement range and dynamic response time of our sensor, with other reported ones also based on optical fiber interferometers. The results, as shown in An optical fiber FPI-based humidity sensor has been demonstrated by fusion splicing a short HCF with a single-mode fiber and coating the tip of the HCF with a layer of gelatin. The working principle is based on the humidity-induced thickness change in the gelatin film that changes the cavity length of the FPI. A humidity measurement with a high sensitivity of 0.192 nm/%RH and a wide measurement range of 20–80%RH has been realized by measuring the wavelength shift of the interreference fringe. Good repeatability and stability have been obtained, and the response and recovery times are only 240 and 350 ms, respectively. In addition, simultaneous measurement of temperature with a sensitivity of 10 pm/°C has been achieved by cascading an FBG sensor in the FPI probe.


P-type ternary switch devices are crucial elements for the practical implementation of complementary ternary circuits. This report demonstrates a p-type ternary device showing three distinct electrical output states with controllable threshold voltage values using a dual-channel dinaphthothieno-thiophene–graphene barristor structure. To obtain transfer characteristics with distinctively separated ternary states, novel structures called contact-resistive and contact-doping layers were developed. The feasibility of a complementary standard ternary inverter design around 1 V was demonstrated using the experimentally calibrated ternary device model. MVL provides significant efficiency gains in terms of the number of transistors and the interconnection length required to perform equivalent functions designed with binary logic. In particular, the ternary logic that is composed of three logic states—0, 1, and 2—has several advantages over Boolean logic design using the conventional complementary metal–oxide–semiconductor (CMOS) logic, with the possibility of the lowest power consumption among MVLs9.Recently, multi-valued logic (MVL) technology has attracted interest as an alternative architecture to address the rapid increase in energy consumption required for massive data processingVth) in the form of integrated circuits8. More recently, a quantum dot gate FET was also proposed for ternary logic operation, but the process of fabricating two layers of well-aligned quantum dots is difficult to control and further scalability can be a serious challenge because the size of the quantum dots and the separation distance between them cannot be modulated flexibly9. Ternary devices using transition metal dichalcogenide (TMDC) heterojunctions have also been proposed to utilize the negative differential resistance or negative differential transconductance13. Shim et al. reported a ternary device based on a BP/ReS2 heterojunction forming a broken-gap band structure10. Nourbakhsh et al. demonstrated a MoS2/WSe2 ternary device with band-to-band tunneling mechanism11. Although these devices show promising functional feasibility, large-scale device integration remains quite challenging for TMDC materials. The wafer scale growths of TMDCs above oxides such as crystalline SiO2 or sapphire have been recently researched, but the direct growth on the oxide when the device structure exists is not desirable because of high growth temperatures over 800 °C15.Owing to the apparent merit of architectural simplicity, various kinds of ternary devices have been investigated for many decades. Among them, carbon nanotube field-effect transistors (CNTFETs) have been the most widely investigated, but all studies using CNTFETs have been theoretical because it is difficult to arrange various types of CNTs with different threshold voltages , where external doping processes have been used in graphene channels to customize various PN junctions in the channel region21. However, the low noise margins because of the low on/off current ratios of GFETs, which are less than ~ 10, constitute a serious drawback in ternary circuits.Graphene FETs are also used for ternary logic applications because of their high mobility, linearly controllable Fermi levels, and large area growth via thermal chemical vapor deposition (TCVD)n-type ternary graphene barristor by forming dual junctions with different Vth22. The graphene barristor, composed of graphene–semiconductor heterojunction, is the Schottky barrier triode modulating by control of the Fermi level of graphene using an electrical gate field. For implementing a ternary logic device, a graphene barristor is a better option, because of its linear drivability of current and large on/off ratio than GFETs. However, the benefits of ternary logic could not be fully accomplished by the resistive-load approach; a p-type counterpart ternary device is necessary to overcome this limit. Unfortunately, there are not many candidate materials for the p-type semiconductor that can be deposited at low temperatures, without damaging the graphene channel at low temperatures. However, several methods of fabricating p-type “binary” graphene barristors have been reported using pentacene and dinaphthothieno-thiophene (DNTT), despite the relatively low field-effect mobility ~ 2 cm2/Vs26. In addition, DNTT showed better air stability than pentacene28. Thus, it is worthwhile to pursue a p-type ternary graphene barristor utilizing the works on air-stable DNTT–graphene barristors.Kim et al. demonstrated an p-type ternary graphene barristor using a DNTT–graphene heterojunction. Multi-Vthp-type DNTT–graphene barristors with dual-channel structures were successfully demonstrated by modulating the carrier transport through the DNTT and metal contact interface. Subsequently, the functionalities and benefits of a standard ternary inverter (STI) were examined using the complementary ternary graphene barristors (ZnO–graphene barristor for an n-type device and DNTT–graphene barristor for a p-type device).In this study, we investigated a dual-channel p-type graphene barristor with the DNTT–graphene heterojunction device. In this study, a buried gate structure was adopted to control the Fermi level of graphene more stably  metals were deposited using an e-beam evaporator to fill the oxide trench. The buried gate pattern was formed in the trench region by applying a combination of lift-off and chemical–mechanical polishing processes. The buried gate structure was chosen because it can provide a more uniform electric field in the channel region than the bottom gate structure that is commonly employed in the early stages of graphene device studies29. This step was followed by atomic layer deposition of 30 nm of Al2O3 gate dielectric at 200 °C and annealed at 300 °C in a high vacuum (~ 10−6 Torr) for 1 h to improve the quality of the gate dielectric  for certain devices, whereas for other devices, a contact-doping layer (CDL) was formed with 10 nm of a 7:1 co-evaporation layer of DNTT and 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane  deposited on the drain region using a shadow mask , which is a ask Fig. f. Upon cn-doped graphene channels were 5.1 and 5.3, respectively, which confirmed that the graphene channel was a monolayer. Subsequently, the undoped graphene channel was slightly p-doped with impurity charges at the graphene–Al2O3 interface and the redshifts of the G and 2D peaks indicated that the PEI doping process successfully changed the doping state of the graphene channel to n-type31.During device fabrication, the quality of the graphene channel was examined using Raman spectroscopy, as shown in Fig. The electrical properties of the fabricated devices were characterized using a semiconductor parameter analyzer (Keithley 4200) at room temperature.p-type graphene barristor, where the arrow indicates the direction of carrier flow. The DNTT partially overlaps the graphene channel, and the Schottky junction is formed at the DNTT–graphene interface. The flux of charge carriers through the DNTT–graphene Schottky barrier can be modulated using the buried gate bias (Vg), which modulates the Fermi level of graphene and thereby adjusts the Schottky barrier height (SBH).Figure p-type graphene barristors are shown in Fig. Vg is low (more positive Vg), the graphene barristor is turned off because the SBH is too high to allow the flow of hole carriers, as shown in Fig. Vg increases (more negative Vg), the graphene barristor is turned on as the SBH is reduced, as illustrated in Fig. Vth. Therefore, the surface of the graphene channel region was treated with PEI solutions of various concentrations, viz., 0 (no doping), 0.0025, 0.005, and 0.01 wt%. As the concentration of the PEI solution increases, Vth of the graphene barristor shifts toward negative Vg; Vth is − 6.8 V in the case of 0.01 wt% PEI concentration. The n-type dopant, PEI, induces additional electron charges in graphene, which shifts the Fermi level of graphene upward and increases the SBH. The effects of graphene doping are schematically illustrated by comparing the band diagrams of undoped and n-doped graphene barristors in Fig. Vg ranging from − 15 to 15 V, the extracted SBHs are modulated from 0.19 to 0.34 V for the undoped device and from 0.21 to 0.39 V for the n-doped device, indicating that the Vth shift of the graphene devices is caused by the change in the SBH due to chemical doping. , as shown in Fig. p-type ternary device is presented in Fig. 2, which is sufficient to divide the current level between ternary states. This is the first demonstration of a p-type ternary logic switch with a saturated intermediate state. Figure Id–Vg of the p-type ternary device on a linear scale. The saturation of the intermediate current is distinctly observed even on this scale, which is important for the ternary logic gate circuit. Another positive result of this study is that the electrical characteristics of the p-type ternary device can be maintained for more than 45 days, confirming that this device is air-stable , and the other was the  channel , as show2O3). In principle, further performance enhancement is quite feasible because there are several structural parameters, such as the electrical thickness of the gate dielectric, thickness and doping state of the DNTT, and area of the Schottky junction, that can be optimized further. Because the experimental optimization requires a rather extensive material-based study, theoretical analysis of the future scalability and performance was performed based on the fitting result shown in Fig. The performance of the ternary device is not yet competitive with CMOS technology on a similar scale. The operation voltage range is quite large, and the subthreshold swings remain very high due to the thickness of the gate dielectric (30 nm of Alp-type ternary device was well-fitted by a capacitor-based ternary device model (Supplementary information, Fig. The electrical operation of the n-type dual-channel ternary graphene barristor, the ZnO–graphene barristor model reported in the literature22 was used (Supplementary information, Fig. Then, a complementary STI was designed using the device model developed for 1 V operation, as shown in Fig. SPICE for Vdd = 1 ± 0.1 V. Well-behaving STI transfer characteristics were obtained around Vdd = 1 V. For STI, four noise margins were defined, as demonstrated in Fig. dd, in which the minimum noise margin was 71 mV at Vdd = 1.06 V. The very narrow peak region in Fig. n-type and p-type ternary switches are necessary. The gains of the two-state transitions were 2.3 and 2.8, respectively, for Vdd = 1.06 V (Fig. 7. However, the large static power dissipation in the intermediate state due to the half turn-on of both n- and p-type ternary devices remains an issue for improvement for low-power systems (Supplementary information, Fig. Figure 6 V Fig. d. As a rp-type ternary device is the last essential element for the operation of complementary ternary logic circuits. By combining the demonstration of this device with the recent progress related to n-type ternary devices, reasonable projections of the performance and functionality of a complementary ternary inverter were achieved, and the feasibility of low Vdd operation at 1 V was confirmed. Although further optimization to scale down the device is necessary, this report confirms the achievement of a major milestone in ternary logic technology for extremely low-power computing.A fully functional Supplementary Information.


We report an ultra-broadband and compact TM-pass polarizer based on graphene-buried polymer waveguides. The characteristic parameters of the polarizer were carefully designed and optimized. The standard microfabrication processes were employed to fabricate the device. The presented polarizers exhibit high polarization-dependent transmission imposing a TE mode cutoff while leaving the TM mode almost unaffected. We experimentally demonstrated the polarizer that has an ultra-high extinction ratio of more than 22.9 dB and 41.9 dB for the monolayer graphene film placed on the surface of core layer and buried in the center of core layer, respectively, and as low insertion loss as ~4.0 dB for the TM mode with the bandwidth over 110 nm. The presented polarizer has the advantages of high extinction ratio, ultra-broadband, low cost, and easy integration with other polymer-based planar lightwave devices. Polarization state control of the light has important applications in optical communications and photonic integrated circuits, especially for the systems with only one operating polarization . OpticalHere, we report an ultra-broadband and compact TM-pass polarizer based on graphene-buried polymer waveguides. The polarization mechanism is mainly due to the differential attenuation of the two polarization modes in the presented hybrid waveguides. The change of the modal characteristics for the TE mode is more than that of the TM mode, which is derived from the fact that the electric field of the TE mode interacts with graphene more effectively than that of the TM mode. Moreover, thanks to the flexible fabrication process of the polymer waveguide, the graphene film can be easily placed anywhere inside the waveguide by using the standard microfabrication process. The presented polarizers have the advantages of low insertion loss, high extinction ratio, and easy integration with other polymer-based photonic integrated devices.λ0 is 1550 nm. The relations between the waveguide core width a and mode effective indices effN of the strip waveguide without a graphene layer is shown in b = 0.8a. In order to realize single-mode transmission in the waveguide, the core width a should be selected in the range of 2~7 µm, meanwhile, the core thickness b was chosen in the range of 1.6~5.6 µm. The simulated intensity patterns of the fundamental mode with the optimized core dimension for TE and TM polarizations are shown in Polarizers with the graphene layer buried in different locations are designed to examine the effects of the buried location on their loss performance. The schematic structures of the demonstrated two types of polarizers are shown in MPA) can be calculated with the imaginary part of the effective refractive index according to the following formula [effN) is the imaginary part of the effective mode index of the waveguide. The modal loss induced by the graphene was affected by the core dimension, which is mainly due to the effect of the core dimension on the modal field intensity distributions in the waveguide. As shown in The complex effective indices of the hybrid polymer waveguide embedded with graphene layer were calculated based on the interface model by using the finite-element method with the commercial full-vector mode solver COMSOL. In the simulation, we have established triangle meshes with a maximum mesh length of 500 nm and applied perfect matched layers as the boundary condition, which can help us get an accurate numerical result since electromagnetic waves are perfectly absorbed in the boundary without reflections. The modal loss MPA=The designed polarizers were fabricated by using standard microfabrication techniques, wherein polymer materials EpoCore and EpoClad  were chosen as the core and cladding materials, respectively. To characterize the performance of the fabricated polarizers based on the graphene-buried polymer waveguides, we used the polarization optics to control and monitor the polarization states of the input light and the output light. The input signal light from a semiconductor tunable laser  is coupled directly into the input port of the polarizer through a lensed single-mode fiber (SMF) with a mode-field diameter of 2.5 µm. The output light beam from the polarizer can be collected with a lens, which images the output near-field patterns, and observed by an infrared charge-coupled device (CCD) camera. The images can be displayed on a video monitor during the testing process. After finding the output pattern, the output light was coupled into an optical power meter through a SMF to measure the output optical power from the polarizer and calculate the loss induced by graphene film and the insertion loss of the polarizer. By comparing the losses measured for the graphene-buried waveguide and the reference waveguide without graphene film, the losses induced only by the graphene films can be obtained. The variation of graphene-induced losses of the polarizers with the operating wavelength is indicated in In fact, we should get a larger extinction ratio, however, this is limited by the measuring system rather than the graphene layer. Therefore, we dissociated the samples and further experimentally measured the loss performance of the polarizers embedded with a shorter length of graphene film. As shown in n is the number of graphene layers and t is the thickness of EpoCore between graphene sheets, where the t is 100 nm in our design. The variation of the graphene-induced loss of the polarizer with the numbers of graphene layers is shown in Moreover, we further theoretically verified the effect of the number of the buried graphene layers on the fundamental mode losses. In conclusion, to develop the polymer integrated waveguide polarizer in the optical communication system applications, we have proposed and experimentally demonstrated an ultra-broadband TM-pass polarizer based on polymer waveguide embedded with graphene layer. Polarizers with the graphene film buried in different locations are designed and fabricated to demonstrate the effects of the buried locations as well as the layers of the graphene on their loss performance. Our typical fabricated polarizer presents an ultra-high extinction ratio of more than 22.9 dB and 41.9 dB for the monolayer graphene placed on the surface of core layer and buried in the center of core layer, respectively, and as low insertion loss as ~4.0 dB for the TM mode with the bandwidth over 110 nm at telecommunication wavelengths. Furthermore, we can increase the graphene layers buried in the center of core to develop a polarizer with compact size and higher extinction ratio. The proposed polymer waveguide polarizers with high extinction ratio, low insertion loss, ultra-broadband, and easy fabrication process have potential applications for on-chip photonic integrated circuits.