Current studies on nanocomposites have focused on their multifunctional properties and their industrial production. In this work, polyetheretherketone (PEEK)/graphene nanoplate (GNP) composites were produced by a direct semi-industrial process. Different percentages of untreated GNP  were added to PEEK by employing melt-compounding followed by injection-moulding. Despite the semi-industrial approach used, the modulus, strength, and Poisson coefficient of the nanocomposites (1 and 5 wt.%) were not significantly affected by the addition of GNP. However, there was a slight decrease in the strength at 10 wt.% GNP. Our study also shows that the thermal conductivities of PEEK/GNP composites are up to 2.5 times higher than that of pure PEEK. Materials science; Nanotechnology; Aerospace engineering; Nanomaterials; PEEK; Graphene; Composites; Characterisation; Thermoplastic HConsequently, over the last decade, investigations have been conducted to improve the thermal properties of PEEK through the incorporation of carbon nanofillers such as carbon nanotubes, graphene, and graphene-based materials such as graphene nanoplates (GNPs)  or graphene oxides , 10, 11.Considering the high cost of graphene, GNPs, which are formed from several stacked graphene layers bound to each other by Van der Waals forces, are an interesting option for large-scale production  of nanocThe mechanical and thermal properties of a nanocomposite depend on the dispersion of the filler, its size distribution, and the interface between the matrix and the nano-reinforcements , 20, 21.Relative to other organic/inorganic filler nanocomposites with a PEEK matrix, there are few studies about PEEK/GNP composites available in the literature. In these studies, PEEK/GNP nanocomposites were prepared by two different methods: solution intercalation (with or without a compatibilising agent) , 5, 21 aDespite the inferior mechanical properties obtained by melt-compounding, the process is still preferred for industrial applications. The absence of solvents, easy-to-scale method, ready availability of industrial equipment among other qualities makes this technique a preferred choice for nanocomposite production , 24, espIn this work, PEEK/GNP composites were produced by a semi-industrial, two-step process of melt compounding followed by injection moulding. We studied the effects of GNPs  on the mechanical and thermal properties of the as-produced PEEK matrix. The fracture surfaces and the dispersion of graphene within the matrix of samples were analysed by scanning electron microscopy (SEM).22.13, which were dried at 150 °C prior to be used for production of nanocomposites. Pristine graphene nanoplates (avan-GRAPHENE) were provided in powder form by Avanzare Innovación Tecnológica, SL (Spain). SEM analysis of the received graphene was performed by using a Helios Nano Lab 600i scanning electron microscope.PEEK 90P grade was provided in pellets by Victrex plc, UK, with glass transition temperature = 143 °C, melting temperature = 345 °C, density at 25 °C = 1.30 g/cm2.2PEEK nanocomposites were melt-blended using an extrusion-compounding machine, Coperion ZSK 26. This machine is equipped with a 26-mm diameter co-rotating twin-screw and two Brabender gravimetric feeders. Two different temperatures were set for this process: 330 °C for the extruder and 360 °C for the nozzle. A high shear rate screw profile, which was specifically designed for this process, was used to ensure proper dispersion. The rotor speed set to process all the nanocomposites was the same, 250 rpm. Two feeders were required for this process: one for feeding PEEK at a speed of 10 kg/h and a lateral one for feeding graphene. Graphene was fed in order to achieve a final concentration of the nanocomposites of 1, 5, and 10 wt.% raw GNP. The diameter of the die used for this process is 2 mm. Once the molten material was extruded through the die, it was quenched in a water bath at room temperature, then dried and cut into small pellets. Pure PEEK was also processed under the same conditions in order to use it as reference material.2.3D790-02, were injected in a mould made of tool steel (1.2790) at a constant temperature of 180 °C. The pellets were dried at 150 °C for 3 h prior to processing. The specimens were produced by injection moulding through a JSW 85 EL II injection machine with a 35-mm diameter reciprocating screw at a screw speed of 120 rpm. The dosage used in each specimen was 20 mm with an injection time of 0.36 s, the compaction pressure was 1000 bar, and the cooling time was 3 s. Pure PEEK and the specimens that contained different loadings of GNPs were prepared by following this protocol. At least 15 samples were produced for each composition.Dog-bone shaped specimens (type V) for tensile tests, under ASTM D638-02a , and spe2.43 for PEEK-90G and 2.2 g/cm3 for GNPs [The density of the injection-moulded specimens with different percentages of graphene were measured in accordance with ASTM D792-13 standard. At least four cubic samples for each level of GNP content were produced and weighed in air and then in distilled water at room temperature (≈23 °C). The theoretical density was also calculated by applying the rule of mixtures with assumed densities of 1.3 g/cmfor GNPs , and wer2.5Differential Scanning Calorimetry  was used to obtain information about the thermal properties of the nanocomposites. The samples (5–10 mg) of PEEK with different percentages of graphene  were heated from 20 °C to 400 °C at 10 °C/min and held at 400 °C for 5 min to remove the thermal history of the samples. Then the samples were cooled to 20 °C at 10 °C/min, held for 0.5 min and heated again to 400 °C at 10 °C/min. The DSC samples were cut from the bulk of the injection specimen (dog-bone) with the help of a hammer and a knife. One test per sample was carried out.2.6D790-02 in a three-point bending configuration with a distance between supports of 60 mm . At least 5 specimens (79 mm × 10 mm × 4 mm) were measured for each composite.The characterisation of the flexural properties  was carried out in accordance with ASTM 1 and ε2) of each specimen during the test was performed by digital image correlation (DIC). One side of every ‘dog bone’ specimen was painted white and then carefully and lightly sprayed with black paint to get the random speckle pattern required for DIC analysis. The images (36 mm × 4.5 mm) were taken every 2 s during the test [Tensile tests were carried out under ambient conditions with an Instron 3384 by using a load cell of 10 kN. Tests were carried out in accordance with ASTM D638-02a by using type V specimens at 1 mm/min of crosshead speed . The axithe test , 27. Fin2.7Thermal conductivity of PEEK/GNP composites was carried out by Hot Disk TPS  in accordance with ISO 22007-2:2008 standard. Two identical samples (30 mm × 10 mm × 4 mm) were polished, cleaned, and a heater/sensor element was placed between them. Full details of this process have been reported , 29. The33.1The morphology of the as-received graphene nanoplatelets was characterised by using SEM. Agglomerates of graphene are observed in 3), 3.3% more dense than pure PEEK. For all the samples, the differences among theoretical and the experimental density measures were lower than 1%, which indicates that the injection method works well, and the samples have low porosity. This low porosity matches the results of the SEM analysis and voids.For the composite samples, the addition of GNP within the matrix slightly increased the density of the material . As expeWith regards to nanofiller dispersion, for low loading of GNP (1 wt.%), we observed GNPs composed of a few stacks of graphene layers and good distribution of the GNP within the matrix c. For hiAt the interface region between the nano reinforcement and the matrix, the PEEK completely wets the surface of most of the graphene agglomerates. However, debonded regions can be found among sheets of graphene, which can result from poor interactions between the graphene agglomerates and the PEEK or among the layers of graphene within agglomerates , 22 Figd. We did3.2c) of the composites was calculated with the following equation [The thermal properties of the injected samples were analysed by DSC. The degree of crystallinity (χequation :(1)χc=ΔHS is the heat of melting calculated by integration from 130 °C  to 375 °C, ΔH0 is the heat of fusion of 100% crystalline PEEK, taken as 130 J/g [In which ΔH 130 J/g  and ϕ isFrom the first heating and later cooling ramps, both at 10 °C/min, thermal parameters such as glass transition, melting temperature, melting enthalpy, crystallinity, crystallisation temperature, and heat of crystallisation were calculated. 3.3The effect of GNPs on the mechanical properties of the composites has been explored by three-point bending and tensile tests, the results of which are shown in The addition of graphene increases the stiffness of the nanocomposite see . The nanConversely, the tensile and flexural strength slightly decreased with the addition of GNP. PEEK with 5 wt.% GNP exhibited an approximately 7% and 10% decrease in flexural and tensile strength, respectively. This reduction of strength can be attributed to a range of factors. Strong interfacial adhesion between the reinforcement and the matrix is essential to reach high strength in composites , 37. In 1. The standard ν is defined as:We also calculated Poisson's ratio (ν), which is a measure of the transverse contraction , the Poisson's ratio was similar to pure PEEK, as expected . But, thFractography analysis of the broken surfaces of pure PEEK showed two different regions; Region I and Region II  Figure a. Figure2 nanocomposites [For the nanocomposites, two distinct regions can be seen d and g. mposites . GNP migIn addition, with increasing GNP content, the size of the dimples decreases as a result of the formation of larger number of nanoplates per volume fraction of material e, h. How3.4The thermal conductivity of PEEK/GNP composites versus weight fraction of GNP is shown in This value  is the highest thermal conductivity measured and reported for PEEK/GNP composites so far; note that 37% and 90% improvements have been reported , 45 for However, the enhancement of 10 wt.% of GNP was moderate relative to the previous results with other polymeric matrices or similar nanofillers. As an example, Hwang et al.  reported4High performance PEEK/GNP nanocomposite samples even with high GNP loadings (5 and 10 wt.%) could be manufactured by an industrial two-step process (melt-compounding and injection moulding). This study shows how current industrial machines can be used to manufacture nanocomposite parts in the industry.The processing conditions were enough to obtain good dispersion of GNP agglomerates at low concentrations (1 wt.%). However, with increased amounts of GNP, larger agglomerates of GNP were found. The size of the agglomerates, even at 10 wt.% GNP, is smaller than in pristine GNP, which shows how efficient this processing technique is at GNP dispersion. These GNP/PEEK composites crystallise faster than pure PEEK, probably because GNP has a nucleating effect that results in higher crystallinities after crystallisation at high cooling rates.The addition of GNP within the PEEK matrix enhances the elastic modulus of PEEK, but decreases the strength, which indicates that there is poor transfer between nano-reinforcement and matrix. Furthermore, graphene decreases the Poisson's ratio of pure PEEK. Different failure behaviours were observed by fractography of the nanocomposite samples, which were probably a result of a decrease in strain at break through the addition of GNP. Higher contents of GNP showed larger areas associated with brittle behaviour.The introduction of graphene to the PEEK matrix improved the thermal conductivity. The sample with 10 wt.% GNP exhibited 0.73 W/mK, which is two-fold the thermal conductivity of pure PEEK. This significant increase has never observed for GNP nanocomposites manufactured by melt-compounding.To improve the dispersion and enhance the stress transfer across the interface between PEEK and graphene, a change in the semi-industrial parameters and the use of complementary techniques, such as functionalisation of graphene, or the use of compatibilizer agents need to be explored. To conclude, we have demonstrated how a semi-industrial technique can produce PEEK nanocomposites with enhanced thermal properties with retained or even slightly improved mechanical properties.A. Alvaredo-Atienza: Performed the experiments; Analyzed and interpreted the data; Wrote the paper.Juan P. Fernández-Blázquez: Analyzed and interpreted the data.P. Castell: Performed the experiments; Contributed reagents, materials, analysis tools or data.Guzmán de Villoria: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.10.13039/100012818Community of Madrid through the NMAT2D-CM (S2018/NMT-4511). R. Guzman de Villoria was supported by the Spanish 10.13039/501100003176Ministry of Education, Culture and Sports through the Beatriz Galindo fellowship (BEAGAL18/00091).This work was supported by the The authors declare no conflict of interest.No additional information is available for this paper.


In this paper, we numerically and theoretically study the tunable plasmonically induced transparency (PIT) effect based on the graphene metasurface structure consisting of a graphene cut wire (CW) resonator and double split-ring resonators (SRRs) in the middle infrared region (MIR). Both the theoretical calculations according to the coupled harmonic oscillator model and simulation results indicate that the realization of the PIT effect significantly depends on the coupling distance and the coupling strength between the CW resonator and SRRs. In addition, the geometrical parameters of the CW resonator and the number of the graphene layers can alter the optical response of the graphene structure. Particularly, compared with the metal-based metamaterial, the PIT effect realized in the proposed metasurface can be flexibly modulated without adding other actively controlled materials and reconstructing the structure by taking advantage of the tunable complex surface conductivity of the graphene. These results could find significant applications in ultrafast variable optical attenuators, sensors and slow light devices. Typical electromagnetically induced transparency (EIT) occurs in a coherently driven atomic system, resulting from the destructive interference of two dressed states, and a narrow transparency window is generated simultaneously in a broad absorption spectral region ,2,3. HowSo far, several optical active materials such as semiconductors ,11 and nIn this paper, we propose a tunable metasurface structure to theoretically and numerically study the PIT effect, which consists of a graphene cut wire (CW) resonator and split-ring resonators (SRRs). It is found that a narrow transparency window with 90% transmission appears at 6.08 µm in the transmission spectra due to the weak hybridization between the graphene CW resonator and SRRs. We theoretically calculate the transmission spectra with various coupling coefficients according to the coupled harmonic oscillator model, and the results are approximately consistent with the numerical simulation results from different coupling distances, achieved by utilizing the finite-difference time-domain (FDTD) solutions software. Moreover, the optical response of the proposed metasurface structure can be tuned by controlling structural geometries such as the coupling distance between the graphene SRRs and CW resonator, the number of the graphene layers and the geometries parameters of the graphene CW resonator. Importantly, compared with the metal-based metamaterials, the tunable transparency window can be realized by utilizing the electrical gating to alter the Fermi level of the graphene without adding other actively controlled materials and reconstructing the proposed structure. The results will open a new avenue in tunable sensors, ultrafast optical switches and variable optical attenuators.2O3 substrate as illustrated in 2O3 substrate whose complex refractive index can be obtained in [L = 85 nm and W = 7 nm, respectively. The distance of the SRRs is 1d = 5 nm, while the coupling distance between the CW resonator and SRRs is d = 6 nm. L1, L2 and G refer to the length in the y and x-direction and the gap of the split-ring resonator and their detail values are set as L1 = 29 nm, L2 = 25 nm, and G = 19 nm, respectively. The period of the proposed metasurface structure is xP = 125 nm in the x-direction and yP = 66 nm in the y-direction.The unit cell of the proposed metasurface structure used to investigate the PIT effect is composed of a graphene CW resonator and two identical and axisymmetric graphene SRRs on the Alained in  is set a2O3 with the thickness of 40 nm can be grown by atomic layer deposition [2O3 substrate by the dry or wet transfer method [The proposed graphene-based metasurface can be fabricated in the following sequence of steps. First of all, the substrate Alposition . Next, tposition ,23. Finar method . The proThe surface conductivity of monolayer graphene ation as :(1)σ(ω)=2O3, the external voltage on graphene and the thickness of Al2O3, respectively. We employ the Here, a τ=1/2Γ , where iεdVg/eH1 ,28. Heref=106m/s  and H1=4ulations : (4)ε=1+n vacuum . ParticuNσ (N≤6) ,32.In this paper, the FDTD solutions software is utilized to numerically study the optical response of the metasurface structure, and the periodic boundary condition is employed in the x and y directions, while the perfectly matched layer (PML) absorbing boundary condition is applied along the propagation direction of the incident light.As shown in z|2-component of the unit cell with individual graphene CW resonator at 2O3 substrate as illustrated in Then, we numerically calculated the electric field of the |EIn addition, the Ez field distribution of the graphene SRRs and CW resonator at two transmissions—dip I x¨lated as ,36:(7)x=The transmission Then, we explore the influence of the geometrical parameters of the graphene CW resonator on the PIT spectra. As mentioned above, the complex surface conductivity of the graphene can be flexibly controlled by  voltage ,38,39. T voltage ,41,42. P voltage .In the simulation, the ectively . α0=e2/, which consists of a graphene CW resonator and SRRs. After optimizing the geometry of the structure, a transmission peak of


Paradata are (timestamped) records tracking the process of (electronic) data collection. We analysed paradata from a large household survey of questions capturing pregnancy outcomes to assess performance (timing and correction processes). We examined how paradata can be used to inform and improve questionnaire design and survey implementation in nationally representative household surveys, the major source for maternal and newborn health data worldwide.The EN-INDEPTH cross-sectional population-based survey of women of reproductive age in five Health and Demographic Surveillance System sites  randomly compared two modules to capture pregnancy outcomes: full pregnancy history (FPH) and the standard DHS-7 full birth history (FBH+). We used paradata related to answers recorded on tablets using the Survey Solutions platform. We evaluated the difference in paradata entries between the two reproductive modules and assessed which question characteristics  affect answer correction rates, using regression analyses. We also proposed and tested a new classification of answer correction types.We analysed 3.6 million timestamped entries from 65,768 interviews. 83.7% of all interviews had at least one corrected answer to a question. Of 3.3 million analysed questions, 7.5% had at least one correction. Among corrected questions, the median number of corrections was one, regardless of question characteristics. We classified answer corrections into eight types , zigzag, flat zigzag, missing after correction, missing after flat (zigzag) correction, missing/incomplete). 84.6% of all corrections were judged not to be problematic with a flat (simple) mistake correction. Question characteristics were important predictors of probability to make answer corrections, even after adjusting for respondent’s characteristics and location, with interviewer clustering accounted as a fixed effect. Answer correction patterns and types were similar between FPH and FBH+, as well as the overall response duration. Avoiding corrections has the potential to reduce interview duration and reproductive module completion by 0.4 min.The use of questionnaire paradata has the potential to improve measurement and the resultant quality of electronic data. Identifying sections or specific questions with multiple corrections sheds light on typically hidden challenges in the survey’s content, process, and administration, allowing for earlier real-time intervention . Given the size and complexity of paradata, additional time, data management, and programming skills are required to realise its potential. High-quality routine health data on maternal, newborn, and child health (MNCH) can be used to monitor, identify gaps, and take action to improve quality of care, optimise health system performance, and enable informed decision-making. Routine health management information systems vary in their completeness and quality across low- and middle-income countries (LMICs), and in many cases, are not able to provide the high-quality coverage data required for assessing and guiding health programmes , 2. HousThe shift from paper-based to computer-assisted personal interviewing (CAPI)-based data collection  has enabled inclusions of inbuilt validation and consistency checks, as well as a real-time review of collected data . In addiEven though paradata are widely used in the field of survey methodology in high-income countries , the useIn this paper, we examine how paradata can be used to inform and improve questionnaire design and survey implementation in a large household survey collecting information on pregnancies and births using full pregnancy histories (FPH) and full birth histories with additional questions on pregnancy losses in the past 5 years (FBH+). This paper is one of a series of papers from the Every Newborn International Network for the Demographic Evaluation of Populations and Their Health (EN-INDEPTH) study in five Health and Demographic Surveillance System (HDSS) sites in sub-Saharan Africa and Asia.To assess the differences in paradata timestamped entries between two reproductive modules (FPH and FBH+);To determine whether question characteristics  affect the duration of section completion and answer correction rates;To propose and test classification of answer correction types and determine whether they differ by two reproductive modules.This paper has three objectives:The EN-INDEPTH study aimed to compare two approaches of collecting maternity history (FPH and FBH+) to examine whether the two methods yield different estimates of stillbirth rates and neonatal mortality rates and to determine whether there is a difference in completion time for these two approaches. The study protocol and main findings can be found elsewhere , 11. BriThe analyses in this paper focus on the reproduction section section 2) of the questionnaire only  and separate per observation (in case of parity > 1). For questions with corrections, the timestamp of the final answer was taken as the question’s timestamp. To exclude implausible values, but allow for multiple corrections during the module completion and/or switching between questionnaire sections during the interview , we restt test. Statistical significance level was defined at the 5% level.We categorised all questions by type, nature, and structure. The question types included single-select ; multi-select ; numerical computational ; date-related ; and free-text input . There were three categories based on questions’ nature: two groups of potentially sensitive questions  and regular (non-sensitive) questions. Lastly, considering question structure, there were questions with built-in error notifications , warnings appearing in capital red letters, or any other prompts for correction  and those without such notifications. Differences between FPH and FBH+ were evaluated using descriptive statistics and independent sample Differences in the duration of response time and proportion of corrections by question characteristics  were evaluated using descriptive statistics. A two-part model was used to analyse which question characteristics are associated with the likelihood of question correction  with a binomial distribution and logit link) and the number of corrections (GLM with gamma distribution and log link function). Explanatory variables included question characteristics. Models were adjusted for respondent’s characteristics and location, with interviewer clustering accounted as a fixed effect. Statistical significance level was defined at the 5% level.In paradata, the process of data collection where all answers are entered and corrected is recorded as an ordered list of answers (sequence). To understand this process of data entry and correction better, we ordered all interviews based on the total number of questions asked during the interview and the number of answers (length of a sequence). We distinguished between original answers and corrections and visually inspected the resulting sequence index plot .Whenever an answer to the same question has multiple corrections, these corrections can form a distinct pattern. For example, corrections can be single or multiple; the value of the original answer and the last correction may or may not match; correction entries may have identical or different values and may lead to missing data. As currently there is no classification of answer correction types, we developed and tested one using our survey data. We then used descriptive statistics to examine whether answer correction patterns vary by question characteristics and two reproductive modules.Results are reported in accordance with STROBE Statement checklists for cross-sectional studies  Additio.We analysed 3.6 million timestamped entries corresponding to 3.3 million answered questions and their correction for 65,768 interviews, of which 52.1% related to FPH module , which by design contained more questions than the FBH+ reproductive module  , followed by numerical computational (32.1%), date-related (1.2%), and less than 0.03% being free-text and multi-select types of questions  Table . The medThe median response time per question type was longest for free-text and multi-select questions (0.6 min), followed by date-related (0.2 min), numerical computational (0.1 min), and single-select questions (0.05 min) Fig. . The medOf all asked and answered questions, 7.5% had at least one or more corrections  and numerical computational (n = 23) types of questions. In terms of questions’ nature, the maximum number of corrections was among regular questions (n = 28), followed by death-related questions (n = 23) and TOP-related questions (n = 19). Regarding the question structure, the maximum number of corrections was highest for questions with no built-in notifications (n = 28).Among corrected questions, the median number of corrections was one, regardless of question characteristics. The maximum number of corrections was the highest for single-select  and correction pattern , we developed a classification of answer correction types. We distinguished between eight possible answer correction types after accounting for possibly missing answers after corrections Table . BuildinWe tested our proposed classification using our survey data. Out of eight proposed answer correction types Table , we obseThe flat correction type was the most frequently observed correction type 84.1%), irrespective of question type, nature, or structure , the maximum numbers of corrected questions and corrections per question were high . Not only does this add to the duration of section completion (as shown in our results), and ultimately to interview duration, it could also potentially shed light on questions that are poorly understood or misunderstood by either respondent or interviewer. For example, in our survey, the highest proportion of corrections was among the numerical computational (9.7%) and TOP-related (8.0%) questions. At the same time, even higher proportions of corrections were observed for multi-select questions (44.8%) and free-text (16.2%) questions. They also had the strongest association with the probability of making answer corrections. However, these results should be treated with caution as these questions constitute less than 0.1% among all questions asked.Moreover, given the design and related data entry process , distinguishing between answers, answer combinations, and corrections might not always be straightforward. Nonetheless, we suggest that these types of questions get additional attention during training sessions, with more time being allocated to explaining and practising asking these questions, with additional guidance and supervision provided during the fieldwork. One could also consider limiting even further these types of questions in household surveys.We also developed and tested a new classification of answer correction types. We found this classification useful and suggest it for future studies. For example, we found that almost 90% of all corrections were simple mistake corrections (which is less worrisome) or impulsive and repetitive answers (most likely due to non-responsive screen); hence, they should not be considered problematic. This suggested that the reasons for the remaining multiple zigzag corrections around 10%) lie elsewhere. We speculate that the remaining corrections were made following additional clarifications or confirmations of previous or later answers , lacking missing or incomplete answers after correction. This is primarily due to a lack of ‘missing’ (or empty in content) timestamped entries in our paradata dataset (which by default is not possible) and our decision not to treat ‘Do not know’ answers as ‘missing’ entries. However, we also much acknowledge effective training and comprehensive training manuals, diligent work of our data collectors, and their dedication to prompt interviewees and complete all relevant fields, which we believe aspired minimisation of any missing data. About a third of all questions in our questionnaire had inbuilt error notification, prompts, and warnings. Our results suggest that such notifications are effective since the proportion of corrected questions was 3.3% higher for questions with notifications compared to those without them, and having notifications was significantly associated with the probability of making corrections but negatively associated with the number of such corrections. Additionally, Survey Solutions application had a built-in colour coding indicating survey section completion (red for incomplete and unanswered questions and green for complete), which prompted data collectors to answer all questions. For example, during some training sessions at several data collection sites, interviewers were insisting on learning how to achieve completeness ‘having all sections colour-coded as green’ in all survey sections and were ‘somewhat unhappy’ to finish the exercise with one or more sections remaining incomplete (or red).Given our focus on corrections during interviewing, in our analysis, we excluded implausible and impractical values (over 180 min for section completion duration) but allowed for multiple corrections during the module completion and switching between questions and questionnaire sections. We assumed that such restrictions could provide meaningful and practical insights into face-to-face data collection process, even if it would exclude and not account for long breaks in the interviews  or other errors in timestamps . However, we recognise that this decision could be considered as one of the limitations of the study, as it potentially did not capture corrections based on office data quality and error checks (following which questionnaires were returned and/or reassigned back to the interviewer for correction in-field). Moreover, in our analyses, we specifically focused only on a subset of paradata that related to answers and corrections. We did not utilise the data with timestamped events that related to process-related activities , which could be considered another limitation of the study. As paradata were not readily available for export at the beginning of our data collection (due to software limitations), we did not evaluate individual and team productivity , or estimate the response likelihood and perform measurement error evaluation . HoweverGiven a lack of other studies that have used paradata in MNCH field, we cannot compare our findings directly to other studies, and we would like to stimulate the wider use of survey paradata to advance survey design and implementation for collecting information on pregnancies and births and for other purposes.Paradata provide a wealth of information and could augment surveys, particularly overseeing the data collection process. Not surprisingly, it has already found use in other health and medical areas. For example, similar to our study, paradata were previously used in telemedicine research to estimate time spent to complete a questionnaire  and to eFuture analyses could investigate the relationship between correction rates and correction type and being supervised by a supervisor or other colleagues, which potentially could prompt additional corrections under peer pressure. Other potential uses of paradata in MNCH research could include effort indicators, tracking individual and team productivity, estimating contact attempts without success, and response propensity. Outcome indicators and case status indicators can also include non-interviews by type and refusal patterns by respondent characteristics. Paradata in MNCH research can also be used to generate a dashboard/monitoring system or a validation system for collected data against external sources of information, hence, automatically flagging incorrect entries in the interviews.Accurate estimation of coverage indicators from household surveys is vital but contingent on data quality; hence, a better understanding of how to improve the questionnaire design and survey implementation is crucial. Paradata have the potential to enhance survey design and implementation for collecting information on pregnancies and births, leading to improved metrics of measurement in maternal and newborn health research. They can help to identify questions and sections with multiple corrections and shed light on typically hidden challenges in the survey’s content, process, and administration. Overall, our experience suggests that given the size of paradata and their complex structure, analysis is not always straightforward, and consideration should be given to the additional data management and programming skills required. Nonetheless, paradata provide a wealth of data, can improve the process of data collection using live survey monitoring, and can add value in improving survey data quality as well as efficiency.Additional file 1. Detailed overview of questions in Section 2 in FPH and FBH+.Additional file 2. Example of survey paradata structure.Additional file 3. STROBE guidelines checklist.Additional file 4. Types of questions in Section 2 in FPH and FBH+.Additional file 5. Detailed overview of questions correction by question type, content and structure and reproductive module.Additional file 6. Correction patterns, 23 groups by the number of corrections per question.Additional file 7. Ethical approval of local Institutional Review Boards.


Strain sensors in the form of buckypaper (BP) infiltrated with various polymers are considered a viable option for strain sensor applications such as structural health monitoring and human motion detection. Graphene has outstanding properties in terms of strength, heat and current conduction, optics, and many more. However, graphene in the form of BP has not been considered earlier for strain sensing applications. In this work, graphene-based BP infiltrated with polyvinyl alcohol (PVA) was synthesized by vacuum filtration technique and polymer intercalation. First, Graphene oxide (GO) was prepared via treatment with sulphuric acid and nitric acid. Whereas, to obtain high-quality BP, GO was sonicated in ethanol for 20 min with sonication intensity of 60%. FTIR studies confirmed the oxygenated groups on the surface of GO while the dispersion characteristics were validated using zeta potential analysis. The nanocomposite was synthesized by varying BP and PVA concentrations. Mechanical and electrical properties were measured using a computerized tensile testing machine, two probe method, and hall effect, respectively. The electrical conducting properties of the nanocomposites decreased with increasing PVA content; likewise, electron mobility also decreased while electrical resistance increased. The optimization study reports the highest mechanical properties such as tensile strength, Young’s Modulus, and elongation at break of 200.55 MPa, 6.59 GPa, and 6.79%, respectively. Finally, electrochemical testing in a strain range of ε ~ 4% also testifies superior strain sensing properties of 60 wt% graphene BP/PVA with a demonstration of repeatability, accuracy, and preciseness for five loading and unloading cycles with a gauge factor of 1.33. Thus, results prove the usefulness of the nanocomposite for commercial and industrial applications. Their ability to transform mechanical deformation stimuli into electrical shift signals is largely appreciated by researchers as well as industrial guys5. At large, flexible sensors are principally divided into four categories, namely resistance type, capacitance type, triboelectric, and piezoresistive type6. Henceforth, the piezoresistive type is usually synthesized by a facile method, with low power consumption, convenient signal acquisition, and a simple readout mechanism7. Thus, various materials have been tried to obtain a highly efficient, flexible, sensitive, and stretchable strain sensor. However, nanomaterials are highly regarded for their abilities to satisfy all these criteria and provide further room for research and development.Flexible strain sensors, in contrast to metal or semiconductor-based conventional strain sensors, exhibit a superior application potential in a wide array of fields that includes monitoring of sports performance8. Most significantly, the tremendous attention it gathered among technological and scientific communities due to superior mechanical and electrical properties and high frequency which go up to THz9. Graphene is a material of immense strength with young’s modulus of 1100 Gpa, the fracture strength of 125 GPa, specific surface area of 2630 m2/g, the thermal conductivity of 5000 W/mK, the thickness of 0.345 nm, excellent transport phenomena, zero bandgaps, and mobility charge carrier of 250,000 cm2/V2s10. Among other extraordinary features of graphene, strong π-π stacking, and forces of attraction such as van der Waals among planar basal planes of graphene layers that allow them to aggregate, which results in an ability to develop a controllable self-assembled structure11. These extraordinary properties of graphene endow graphene with novel technological opportunities, in the form of promising nanofiller material for the sole purpose of reinforcement of composites, in contrast to other carbon nanomaterials. Thus graphene stands out among other materials and ideal for developing 3-dimensional structures12. Furthermore, the defects of carbon nanotubes in orientation and geometry can be compensated by graphene sheets. This unique characteristic of graphene has profound implications and thus gives the lead to the era of ultra-sensitive strain sensors. Thus graphene is described as a “miracle material” has demonstrated its sheer potential for the synthesis of graphene-based strain sensors10.Graphene is such material that proves to be a promising candidate as the flexible sensor, on account of its outstanding flexibility, high electrical conductivity, chemical stability, and lightweight13. However, advancements have been made with the use of graphene as a modifier in polymer matrices, whereas no considerable success has been achieved due to poor compatibility between polymer matrix and pristine graphene. Though, as compared to pristine graphene, abundantly present oxygen functional group in graphene oxide not only allows better compatibility and dispersion in aqueous solution but also provides a strong connection between GO and polymer matrix through non-covalent and covalent bonds14. Thus, that makes GO a better choice to be used to prepare graphene oxide/polymer nanocomposite in the form of buckypaper (BP) and study its strain sensing properties.In the field of polymer nanocomposites, graphene proves itself to be a promising modifier due to its astonishing properties. However, the whole effort on polymer nanocomposite is in line to modify neat polymer to attain new and better properties without adding excessive weight or sacrificing processability15. However, polymeric based buckypaper has achieved much success and has been able to seek superior attention to vast sensing applications. The graphene sheets have interlayer interaction due to van der Wall forces of attraction that are relatively weak and have slippage as a result of sliding between layers of graphene. Thus, these issues have the potential to disturb the strain sensing properties of strain sensors16. The buckypaper infiltrated with polymer matrix is viewed as one with better strain transfer phenomena across the composite by providing better binding interfacial bonding. The polymer infiltration into the porous graphene network improves interfacial adhesion among graphene and polymer, which results in a strong bond between graphene and polymer and forms a helical polymer structure. The current method of synthesis has provided a solution to the intrinsic tendency of graphene sheets to agglomerate due to van der Waals forces of attraction, the high viscosity of graphene/polymer suspension, and low solubility in solvents. Whereas, the graphene oxide produced initially had essential functional groups to make strong bonds with polyvinyl alcohol and produced a buckypaper stain sensor with strong interfacial bonding among graphene sheets and polymer matrix17. Han, et al.18 synthesized Carbon nanotube-based buckypaper infiltrated with a polymer matrix of epoxy resin that helped to achieve significant mechanical properties such as tensile strength, Young’s modulus, and elongation-at-break of 146.1 Mpa, 13.8 GPa, and 1.3% respectively. Whereas, Yee et al.17 synthesized multiwalled carbon nanotube-based buckypaper infiltrated polyvinyl alcohol with superior mechanical properties such as tensile strength, Young’s modulus, and elongation-at-break of 156.28 MPa, 4.02 GPa, and 5.85%, respectively. The strain sensor synthesized in this work demonstrated better mechanical properties such as tensile strength, Young’s modulus, and elongation-at-break of 200.55 MPa, 6.59 GPa, and 6.79%, respectively. Another important aspect of the strain sensor is its gauge factor which the selected sample has of 1.33, which is higher than reported gauge factors of buckypaper strain sensors such as 0.4919, 0.3420, and 0.9 to 1.321. Hence, this strain sensor proves to be a significant addition to the existing range of strain sensors and may provide a wide range of applications in various fields of life.Buckypaper is a thin porous freestanding network of carbon fillers bounded by van der Waals forces of interaction and in appearance, it looks like paper-like material as its name indicates. It is fabricated through various techniques, namely the spin coating technique, evaporation induced, vacuum filtration method, self-assembly technique, and resin infiltration technique23. Regardless, polyvinyl alcohol has high applicability for strain sensing applications, is biodegradable, synthesized artificially, and readily available commercially24. Samples were evaluated through mechanical, electrical, electromechanical, and morphological techniques to determine the sample properties. The research findings were consistent with the identification of optimal conditions and concentration ratio of PVA and graphene to synthesize PVA infiltrated BP for strain sensing.In this work, a graphene-based strain sensor infiltrated with polyvinyl alcohol (PVA) is reported. The buckypaper is usually synthesized from carbon nanotubes, but here we report a graphene-based buckypaper, which is the main novelty of the work. The other process of synthesis follows a facile technique, which is reproducible and requires limited equipment for the synthesis. Whereas, the nanocomposite material has superior mechanical properties in terms of tensile strength, young’s modulus, and elongation at break as compared to other buckypaper strain sensors. Graphene as a material has promising characteristics, in terms of superior mechanical and electrical properties, flexibility, and high thermal conductivity. Thus, graphene exhibited a bright prospect for strain sensor application. A nanocomposite material consisting of graphene oxide and polyvinyl alcohol was prepared by varying concentrations of 45, 60, and 75 wt% through simple vacuum filtration and polymer intercalation. The motivation behind the use of PVA for infiltration is supported by past studies demonstrated by several researchers−1. In FTIR analysis, a strong peak at 3435 cm−1 is assigned to strong hydroxyl group O–H (O–H stretching vibrations)25. In pristine graphene same peak was observed but with lower intensity. In contrast, after surface modification, the higher intensity peak shows the degree of oxidation of pristine graphene by oxidation in a highly acidic condition26. The peaks at 2935 and 2880 cm−1 are attributed to symmetric and asymmetric stretches of C–H and the H–C=C–H group is attributed to the presence of methylene stretches. The two new peaks at 1775 and 1701 cm−1 are not observed in pristine graphene, while on graphene oxide indicates carbonyl (C=O) group, which could be related to extending vibrations due to carbonyl group (–COOH)27. Whereas, the vibrations at 1775 and 1701 cm−1 could be associated with the attachment of C–O groups giving evidence of the presence of oxygen-containing groups at the graphene surface. The peak observed at 1620 cm−1 on both pristine and oxidized graphene could be attributed to alkene (C=C), termed as the backbone of the graphene structure. The strong peak at 1110 cm−1 also indicates (C–O–C) the presence of strong oxygen-containing functional groups28. The surface modification of graphene by a strong acidic mixture of sulphuric and nitric acid has significantly modified the structure of graphene and shows a high degree of covalent functionalization29.In order to confirm the functionalization of GO, FTIR analysis of pristine graphene and GO was conducted. FTIR analysis is shown in Fig. 30.A high degree of dispersion and stability of graphene oxide in aqueous suspension is highly desired to obtain an outstanding GO film in the form of buckypaper with good mechanical and electrical properties. In this research, the optimum condition to obtain a well-dispersed solution of graphene was recorded at 60% sonication power, with sonication applied for 15 s after every 30 s for 20 min. The sonication process exploited and disintegrated graphene oxide from aggregates and bundles into freestanding graphene oxide sheets. The low power sonication after one hour clearly showed that graphene oxide has very low dispersibility in methanol and acetone and settles at the bottom of the beaker. Whereas in ethanol, graphene achieved a high degree of uniformity and dispersibility. The dispersibility of oxidized graphene is due to the highly polar nature of ethanol contributed by the presence of carboxylic groups on the graphene oxide surface that creates electrostatic repulsion and polar-polar affinity31. These results further signify that the electrical double layer consisting of a rigid layer bonded to particles and the electrostatic repulsion produced by the diffused layer, which overcomes van der Waals forces resulting in a homogenous solution of GO. As a concluding remark, relatively charged GO overcomes van der Waals attraction and plays a crucial role in solution stabilization. The zeta potential results lower than − 15 mV and higher than 15 mV are considered acceptable for good quality dispersion. However, a value higher than 40 mV is considered as high-quality dispersion32.Figure 33. The tunnelling resistance and contact resistance are less in 75 BP/PVA as compared to 60 BP/PVA as shown in Fig. 34. Thus, the filling of empty spaces forces graphene sheets apart and increases distance among them; therefore, resulting in increased tunnelling and contact resistance.The SEM image of the surfaces of BP is presented in Fig. 35. Good infiltration of the PVA matrix into graphene sheets ensure good adhesion between BP sheets and PVA matrix and contributes to better mechanical properties.Figure 3 character) at around 1338.9 cm−1 is due to the defects, disorder, impurities etc., present in the samples whiles G-band (sp2) at 1563.4 cm−1 corresponds to the characteristic peak of most of the carbon related materials due to the in-plane vibration of carbon atoms. G band shifted to a higher energy of 1611.9 cm−1 in acid treated GN. This could be due to changes and disturbance in GO sheet. The intensity ratios of D- and G-bands (ID/IG) calculated for pure GN and acid treated GO are 0.05 and 1.01 respectively. The dramatic increase of D-band Raman intensity is interpreted as the presence of defects and disorder formed in the sample after vigorous mechanical and chemical process of acid treatment37.Figure 38. The low-intensity peak shows the presence of sulphur atoms due to acid treatment for the functionalization of graphene and also sulphur is used as a growth promotor during the synthesis of graphene by a chemical vapor deposition method. EDX composition of 60 BP/PVA polymer composite C-peak 40. The last peak on PVA infiltrated BP at Energy-dispersive X-ray analysis (EDX) of GO and PVA infiltrated BP (60 BP/PVA polymer composite) was performed to know the elemental composition. Table 41. The strain-to-failure ratio represents the maximum elongation ability in contrast to the original length before its complete failure.The tensile testing was performed to determine the performance of various compositions of PVA infiltrated BP, such as 45 wt%, 60 wt% and 75% of BP, respectively. Figure 42. PVA infiltrated BPs show tensile strengths in a range of 121–200.53 MPa, Young’s modulus within 3.69–6.59 GPa, while all BPs have strain to failure ratio in a range of 4.66–6.79%. The pure 100% BP was not synthesized successfully due to the high degree of slippage between graphene layers, which does not allow layers of graphene to stay together and cannot be peeled off successfully from the substrate. Slippage can be predicted with high certainty as it is an inherent property of carbon nanotubes and graphene materials, even slippage in carbon nanotubes BP is attributed to slippage of graphene layers as graphene is the mother material of carbon nanotubes43. The slippage further can be explained as weak van der Waals interaction between layers of graphene44. The other possibility of failure to achieve freestanding graphene-based BP without any PVA infiltration is attributed to the agglomeration of graphene layers at a high loading of 100%. The forces of attraction among graphene sheets in 100 wt% BP were so weak that it broke when tried to peel off from the PTFE membrane45. Table The stress–strain curve in Fig. 15. The phenomena of Plastic deformation is permanent in nature, first necking starts to appear in structure and further material shows no resistance to the applied force, and finally breaks apart. These tests help to define the working range of a material in which it can be utilized and exceeding this limit may fail the respective component permanently29. All PVA infiltrated BPs demonstrated steeper linear slope with high tensile strength, Young’s modulus, and strain to failure ratio in a range of (ε ~ 4 to 6%). The 75-BP/PVA polymer composite showed a lowest tensile strength of 121.38 MP, Young’s modulus of 3.69 GPa, and strain to failure ratio of 4.66% as shown in Fig. 46.However, PVA infiltrated BPs composites demonstrated linear elastic deformation in the elastic region, and plastic deformation in the non-elastic region, first necking, and finally complete failure17. Additionally, the strong hydrogen bonding between functional groups containing oxygen such as C=O, –OH may also add strength by the contribution of interaction between graphene oxide carbonyl groups and PVA hydroxyl groups47. PVA contains a hydroxyl group capable of hydrogen bonding interacts with the functional group present on functionalized graphene such as carbonyl (C=O), hydroxyl (–OH), and carboxylic group. Thus, strong interaction and interfacial adhesion between various functional groups of GO and PVA matrix contribute to good dispersion throughout the BP, resulting in superior mechanical properties.The superlative mechanical properties can be a result of improved interfacial interaction between Graphene sheets and the PVA matrix. During the infiltration process, the PVA solution diffuses into BP and interacts with GO by binding GO layers together48 while less than prepared by soaking49.However, the Graphene-based BP/PVA shows variability in mechanical properties as shown in Fig. 50. Moreover, the solidification of PVA at various locations on the BP surface and the presence of insoluble hydrogels in the PVA solution further reduced or completely blocked the flow of PVA solution into BP. All these factors collectively contributed to incomplete diffusion of the PVA matrix into BP, hence retarding its mechanical properties51.Furthermore, Young’s modulus is also higher than the reported in the literature (0.25–1.37) while 60-BP/PVA polymer composite demonstrated Young’s modulus of 6.59 GPa, significantly higher than reported for BP strain sensors. The further addition of PVA into BP in 45 BP/PVA reduced the tensile strength to 155.27 GPa and Young’s modulus to 4.33 GPa. This effect can be attributed to the irregular distribution of the PVA matrix into BP; besides higher concentration of PVA solution blocks the pores of BP at the surface and stops further penetration into BPOverall, PVA infiltration into BP phenomenally improved its properties and provided necessary strength to the structure of BP so that it can be peeled off from the PTFE membrane. All the studies in the mechanical section indicate that 60-graphene based BP/PVA polymer composite is a better choice due to its superior properties, with 40% higher tensile strength from 75-graphene-based BP/PVA polymer composite while 23% higher than 45-graphene based BP/PVA polymer composite. In terms of Young’s modulus, it is 31% higher than 75-graphene-based BP/PVA polymer composite while 14% higher than 45-graphene based BP/PVA polymer composite. The better mechanical characteristics of 60-graphene based BP/PVA polymer composite is evident that good dispersion PVA matrix and infiltration into the graphene network and the existence of strong intermolecular interaction between PVA and graphene sheets. The strong interaction between the PVA matrix and graphene makes possible an effective strain signal transfer throughout the structure and makes it feasible for strain sensing application.52. A dense system of conductive graphene in BP offers a smooth flow of current via BP. In order to evaluate BP’s strain sensor electrical efficiency, BP specimen DC electric measurement was conducted through a digital multimeter based on a two-point probe method concept, ASTM D-4496-13. Figure BP possesses electrical resistance that is based on three primary elements i.e., contact resistance among graphene sheets, tunnelling resistance among the bordering graphene, and pristine resistance of graphene as an individual53.The increase in PVA loading, not only increases PVA in BP but also built a thick layer of BP surface, hence they collectively block the current path and increases BP resistance. Furthermore, this is followed by greater bulk resistivity value and lower conductivity. In contrast, uppermost bulk resistivity and lowermost conductivity noticed for 45-graphene based BP/PVA composite reflects a thicker coating of PVA surface deposit54.There was a slight increase in BP/PVA thickness from 0.43 to 0.49 mm, due to more PVA could hold on the nanocomposite surface. Furthermore, a decrease in conductivity in BP/PVA may be attributed to high electrical insulating characteristics of PVA polymer, which is attached at the composite interface hence the bulk and surface resistivity that results in poor electrical conductivity. The electrically insulating PVA penetration into the porous graphene system fills the gaps among adjacent graphene. As a result, electrically conductive paths are decreased; hence, contact and tunnelling resistance increase among adjacent graphene sheetsThe electrical properties of graphene-based BP specimens were analysed further using a four-point probe method based on Van der Paw Hall effect measurement. The results of electrical studies are illustrated in Table 18. As displayed in Table –1 cm2/Vs, and density, 7 × 1021 cm−3 Moreover, greater content of BP forms a denser system of conductivity paths, consequently lower bulk resistivity and higher conductivity. In contrast, PVA infiltration has affected electronic conductivity as depicted in Fig. 2 S/m order, which is appropriate for strain sensing application.According to Fig. 20–1021 cm−3 range55, which are considered comparatively higher than prior studies. Among all graphene-based BP/PVA samples, 60 BP/PVA composite achieved the highest mobility of 2.97 × 10–1 cm2/Vs but with the lowest carrier density of 5.5 × 1021 cm−3. This indicates that low-density electron can move more conveniently via the composite with a faster rate, leading to superior strain sensing performance. While BP samples conductivity was obtained to be proportional to electron density product, Ne, and mobility, µe, in which q refers to electric charge, as written in Eq. , as 75-graphene-based BP/PVA starts to deform its shape after (ε ~ 4%) and linear relationship between strain and relative resistance change no longer holds59. In addition to tunnelling resistance, the higher value of the gauge factor can be attributed to increased contact resistance among layers of graphene, additionally, the PVA matrix fills vacant spaces in BP. In stretch position, graphene layers become completely isolated with no conductive paths and even worse happens when graphene becomes physically apart.Table 60. The loading and unloading cycles were demonstrated in the strain range of (ε ~ 4%) which is within the elastic region. The relationship between tensile strain and relative resistance change vs time of 60-graphene-based BP/PVA polymer composite is shown in Fig. Furthermore, to fully reveal electrochemical characteristics of BP/PVA polymer composite for strain sensor application. Cyclic uniaxial tensile testing was performed to demonstrate and investigate the piezoresistive behaviour of graphene-based BP/PVA polymer matrix during multiple unloading and loading cyclesFigure 61, suggesting two possible phenomena that might be responsible, the first is the hysteresis that the sample does not return to its original value after each cycle and the second reason can be attributed to internal structure destruction of PVA infiltrated BP that results in permanent deformation of the sample. The cyclic loading and unloading of the sample increased its original length that increased the gap between layers of graphene, which consequently increased contact and tunnelling resistance, thus higher relative resistance change is observed in the second, third, fourth, and fifth cycle62. In the end, it can be concluded that 60-graphene-based BP/PVA demonstrated better characteristics of a strain sensor, showed reproducibility and hysteresis free operation.However, on the other hand, 75 BP/PVA did not give satisfactory results. Initially, after the first cycle strain did not return to zero and stopped at 0.1%, indicating the deformation in the shape of the sample. A slight incremental change is also observed in relative resistance change after each cycle63. Overall, all PVA infiltrated graphene-based BPs demonstrated a good piezoresistive response in the strain range of (ε ~ 4%), but 60 graphene-based BP/PVA demonstrated the highest test results under five loading and unloading cycles and showed recovery in characteristics after each loading and unloading cycle64. All these parameters validate the applicability of graphene-based BP infiltrated with PVA for strain sensing applications.In general, the sensing ability of PVA infiltrated BP, mainly depends on three aspects, increase or decrease in contact resistance between graphene sheets, piezo resistivity of individual graphene sheets under strained conditions, and change in tunnelling resistance between graphene sheets due to increase in the gapIn summary, a high-quality flexible, and stretchable strain sensor was fabricated by vacuum filtration technique and PVA intercalation. 60 percent concentration of BP with infiltration of PVA showed the best strain sensing characteristics. The sensor demonstrated mechanical strength, Young’s and elongation at break of 200.55 MPa, 6.59 GPa, and 6.79%, respectively, in comparison to buckypaper based strain sensors found in the literature. The sensor showed electric conductance characteristics due to the conducting properties of graphene, while conductivity decreased with increasing PVA content. The 60 BP/PVA infiltration demonstrated a gauge factor of 1.33 and the piezoresistive response was repeatable with no hysteresis for 5 cycles. These characteristics of 60 BP/PVA polymer composite proves to be the best choice among others with improved strain transfer, linearity, repeatability, highly reproducible results, and recoverable structure within strain range. It has commercial applicability to measure critical human health parameters and predict symptoms of chronic diseases, structural health monitoring, wearable technology, and industrial pressure sensors.A solution of sulphuric acid (98%) and Nitric acid (70%) in a proportion of 3:1 (v/v) was prepared and graphene was immersed in it. Immersion resulted in an exothermic reaction and introduced oxygenated groups on the graphene structure through covalent functionalization. The solution with graphene was bath sonicated for 3 h at room temperature. The combination of acid treatment, along with bath sonication helps to create bond cleavage in carbon lattice via electrophilic attack. It also provides an opportunity to carbonyl group for attachment and at the same time, removes residual metallic catalysts. The solution was poured into large beakers with deionized water to dilute the solution and bring down pH near to neutral. The solution was left overnight to let the graphene oxide settle down. The supernatant was discarded, and the precipitate was collected and washed with copious amount of the deionized water. The process of washing with deionized water was repeated until the pH above 5 was achieved. Finally, the solution was poured through the PTFE filter membrane and the graphene oxide was put into a Freezer at − 80 ºC for 24 h, followed by freeze-drying for 24 h to remove the remaining water content.Three samples of oxidized graphene, each weighing 100 mg were immersed in three 50 ml solvents, namely acetone, methanol, and ethanol. All three samples were sonicated in low power ultrasonic bath cleaner for an hour. The ethanol sample was found to be one with better dispersion of graphene oxide visually. All three samples were further sonicated by high power probe sonication  for 15 s with a 30-s break for a total duration of 20 min at an ultrasonic amplitude of 60% . After zeta potential analysis, ethanol was found to one with the best solubility and dispersion. Later the solution of graphene oxide with ethanol was filtered through Polytetrafluoroethylene (PTFE) membrane filtration using a vacuum pump (ULVAC-41B).2H4O)n. PVA is used widely in textile, coating, and papermaking. In BP synthesis, it serves the purpose by developing strong interfacial interaction between graphene and polymer matrix due to functional groups. A 2 wt% solution of PVA in water was prepared such that the solution is not too viscous and there is no residue left. The solution, according to the required weight for infiltration was poured on to the BP formed on the PTFE membrane and was allowed to infiltrate into BP. Samples with different weight fractions of PVA were dried in a vacuum oven at 40 °C for 24 h. The weight fraction of graphene in infiltrated samples was found by subtracting the amount of graphene used initially to cast a BP.The choice of polymer is critical as it plays a major role in determining the BP properties. PVA is a biodegradable, low-cost synthetic polymer with a chemical formula of . Analytical grade concentrated sulphuric acid −1. Further, the zeta potential of dispersed graphene oxide in various solvents was collected using Zetasizer Nano ZS  to identify the solvent with the highest solubility and dispersibility. Whereas the morphology of the BP was observed using a scanning electron microscope (FEI Quanta 400 SEM). EDX analysis was done using EDX spectroscopy (Oxford-Instruments INCA 400 equipped with X-Max Detector). The graphene and graphene oxide samples was determined by using Raman spectroscopy model Renishaw inVia, USA at a wavelength of 5142 nm with green laser excitation. While for mechanical characterization, the strips of rectangular-shaped BP (20 mm × 5 mm) were prepared following standard ASTM D-882 The samples were analysed for tensile loading using a computerized tensile testing machine (Lloyd LR10K Plus). A constant tensile load at a rate of 0.1 mm/min was applied until the sample fractured. Further, for electrical characterization, two aluminum foil strips were attached to opposite ends of samples using silver adhesive to reduce contact resistance and reinforce the connection binding. Direct current electrical resistance was measured using a digital multimeter (Sanwa Japan CD800a), following two probe method according to the ASTM D4496-13 standard. The bulk resistivity  to find out functional groups attached to graphene and graphene oxide. The IR spectra were obtained between wavenumber 650 and 4000 cmThe thickness of BP was measured by using a digital micrometer (Mitutoyo Japan). Whereas, electrical conductivity was measured using Eq. .4\documeFurther, electrical properties  were measured by the Four probe technique using the van der Pauw Hall measurement system (Ecopia HMS 3000 Korea), at a temperature of 300 K under magnetic field. The source of direct current between 1 to 15 mA was passed through outer probes and the resultant voltage drop was measured, however, Ohm’s law  under applied strain. The gauge factor was measured in the linear elastic range of (


We report on the synthesis and enhanced thermal conductivity of stable Ag-decorated 2-D graphene nanocomposite in ethylene glycol based nanofluid by laser liquid solid interaction. A surfactant free nanofluid of Ag nanoparticles anchored onto the 2-D graphene sheets were synthesized using a two-step laser liquid solid interaction approach. In order to understand a pulsed Nd:YAG laser at the fundamental frequency  to ablate Ag and graphite composite target submerged in ethylene glycol (EG) to form AgNPs decorated 2-D GNs-EG based nanofluid. From a heat transfer point of view, it was observed that the thermal conductivity of this stable Ag-graphene/EG is significantly enhanced by a factor of about 32.3%; this is highest reported value for a graphene based nanofluid. Graphene is a flat monolayer of carbon atoms or a thin layer of pure carbon tightly packed into two-dimensional (2D) honeycomb lattice. Early investigations of graphene demonstrated that this material exhibits extraordinary physical and chemical properties such as significantly higher values of thermal conductivity, electrical conductivity, high room temperature carrier mobility and lateral quantum confinement amongst others. These properties confer graphene and graphene based nanocomposites numerous potential technological applications such as super capacitors, organic solar cells, photovoltaics, fuel cells, and inter alia7. In terms of heat transfer, the thermal conductivity of graphene is demonstrated to be within the range of 3,000–5,000 Wm−1 K−1 at room temperature which is an exceptional figure when compared to the thermal conductivity of pyrolytic graphite (2000 W m−1 K−1 at room temperature), and surpasses even the highest value of natural diamond  as well as that of standard Cu (384.1 Wm−1 K−1). Hence, graphene would be an ideal candidate for use in nanofluids applications12, especially when decorated with additional metallic nanoparticles as will be demonstrated in this contribution.Following the discovery of graphene by Geim and Novoselov2O, oil or ethylene glycol (EG) amongst others. Figure −1 K−1 at room temperature whilst that of metals and their corresponding oxides are two to three orders of magnitudes higher. Hence, the mixture of such metallic or their oxides nanoparticles in standard coolant host fluid in a form of a nano-suspension would induce a significant enhancement in the thermal conductivity of the nanofluid. Such an enhancement has been predicted theoretically by Batchelor and O’Brien in 1977 and Hamilton, Grosser et al., as early as 196210. The thermal conductivity of a nanofluid was predicted to be10.eff/kf is the enhancement of the thermal conductivity of the nanofluid, kf, kp are the thermal conductivity of the host fluid and the nanoparticles respectively, η being the volume concentration and φ is the sphericity reflecting the shape anisotropy of the nanoparticles. Following the pioneering experimental work of Choi et al.,11, such an enhancement in thermal conductivity in various nanofluids was confirmed although prepared by different methodologies28 including direct evaporation, submerged arc nanoparticle synthesis, laser ablation, microwave irradiation, polyol process and phase-transfer. Various nanofluids consisting of nano-scaled Ag, CuO, Al2O3, TiO2 and SiO2 in various host fluids28 have further validated this enhanced thermal conductivity reaching values varying from 10 to 70%. However, the highest enhancements, generally, were observed in shape anisotropy nanosystems such as Ag nanorods28.As shown in Fig. 31. The Ag nanoparticles (Ag NPs) decorating the 2D GNs carpets would act as a facilitating heat medium transfer between the host liquid (EG) and the graphene nanosheets (GNs). In addition, the agglomeration of the (Ag NPs) is likely to be minimized if they are anchored onto the GNs. Hence, in view of validating such a concept, a ternary nanofluid was engineered by pulsed laser ablation in two steps phases: (i) producing graphene sheets first, and then (ii) producing silver nanoparticles. Both are fabricated in the same ethylene glycol host fluid. The measured thermal conductivity of AgNPs-GNs-EG has been found to be to be superior than that exhibited by pure Ag NPs-EG or pure GNs-EG nanofluids37.The originality of this contribution lies in investigating for the first time the thermal transfer enhancement of a nanocomposite of silver nanoparticles (Ag NPs) decorated 2-D graphene nanosheets (GNs) dispersed in ethylene glycol (EG). In the rest of the manuscript, a ternary-composite nanofluid is labeled as AgNPs-GNs-EG. The rationale behind such an approach is that the 2-D nature of graphene sheets would assist in ensuring the percolation threshold at lower volume concentrations in addition to their elevated intrinsic thermal transfer property combined with their homogeneous dispersion in polar host liquids40. This nanoparticles’ synthesis technique is based on laser—solid interaction with the target immersed in a liquid or covered by a protective liquid layer . As shown in Fig. 2H6O2) could take place.As mentioned above, the synthesis of the AgNPs/GNs/EG nanofluid was performed in 2 steps utilizing the so-called Laser Liquid Solid interaction (LLSI) also known as pulsed laser ablation in liquids (PLAL). Indeed, in parallel with the standard well-established vacuum pulsed laser deposition (PLD) technology dedicated to the synthesis of nano-structured and nanophased materials in the form of thin film coatings, LLSI/PLAL is a hybrid chemical–physical laser based approach, promoted for the synthesis of metallic and oxide nanoparticles in a form of colloidal nanosuspensionsyer Fig. . The adv40. As a result, a vapor phase in the form of bubbles is formed. As in the case of H2O, acoustic excitations are then induced by the dynamics of the vapor plume as well as by the locally overheated metallic target40. The effect of the two acoustic excitations provokes the implosion of gas bubbles inducing ablation at the surface of the melted metallic target at the “focus point of the incident laser beam” in the case of Ag target and causing a removal of graphene sheets in the case of graphite target. As the ablated particles are in the nano-size range, this suggests that the bubbles are succumb to a positive external pressure before the implosion. Excluding the laser beam parameters, one should expect that the size of the synthesized nanoparticles by this laser liquid interaction, would depend on the bubbles nucleation rate and their implosion cycles40. Taking into account the different mechanisms mentioned above, one is led to conclude that LLSI/PLAL is likely to be governed by a photo-acoustic energy transfer phenomenon. The corresponding maximum energy fluence of the Nd-YAG was limited to ~ 85 mJ/pulse. The ablation time varied from 5 to 30 min in order to control the volume concentration of the nanoparticles within the ethylene glycol based nanofluid. The temperature of the EG coating fluid layer/ target interface is certainly above the ethylene glycol boiling point of 197.3 °C (470.4 K). The estimated induced temperature at the point where the laser pulse impinges on the target submerged in the EG is estimated to be around Tint = (hc/kBλ) ~ 14.2 × 104 K, significantly above the melting  and vaporization  temperatures of the Ag and graphite targets. Accordingly, and in view of the above considerations, one could expect that while the Ag NPs follow the standard ablation mechanisms to form silver particle clusters as a result of agglomeration, the graphene sheets would, likely, form via acoustic effects mechanisms. More precisely, single or multiple sheets of graphene should be pulled out of the graphite target.The real mechanisms of ablation are not discussed in detail as these, major physical phases are well established. As depicted in Fig. From experimental point of view, graphite and silver targets (both 99.9% purity as well as the ethylene glycol (EG anhydrous 99.8%) used in this study were of chemical grade supplied by Sigma-Aldrich. In phase One, a graphite target with , pulse duration of 12 ns, repetition rate of 10 Hz and an output energy of ~ 85 mJ/pulse was focused through a convex lens with a focal length of f ~ 300 mm to produce graphene nanosheets nanofluid in EG with the processing time of 5–30 min. In phase two, the graphite target was replaced by the silver target(< Ø >  ~ 5 mm diameter and 1 mm thick). The ablation time was fixed at 20 min. After ablating Ag, the Ag target was removed and the AgNPs/GNs/EG nanofluid was collected for further analysis. For comparison purposes and as reference sample, pure graphene-EG and Ag-EG nanofluids were prepared in similar conditions. Hence pure Ag, pure graphene, and Ag-graphene nanofluids will be referenced as AgNPs-EG, GNs-EG, and Ag-GNs-EG, respectively.1 radiation (λ = 1.5406 Å) with a scanning angular step of 0.2° s−1 over angular range 2θ of 20°–90°. The morphology of AgNPs/GNs/EG nanofluid was studied by using a Leo-Stereo high-resolution scanning electron microscope (HRSEM). Identification and characterization of the functional groups on the nanoparticles of the nanofluid was carried out using a Perkin Elmer ATR-FTIR spectrometer within the spectral range of 500–4,000 cm−1. The Raman studies were carried out using a WITEC Alpha 300 Confocal Raman system operating with the second harmonic of a Nd:YAG laser (λExc = 532 nm) as the excitation source. The size and morphology of the nanoparticles in suspension within the nanofluid were determined using a JEOL JEM 2010F microscope. The thermal conductivity of the fabricated AgNPS/GNs/EG nanofluid was investigated by a simplified transient hot-wire technique.The crystalline structure of AgNPs/GNs/EG nanofluid coated on a glass substrate was investigated by X-ray diffraction (XRD) using a SmartLab (Rigaku) diffractometer with a CuKαAg = kBT /6πη〈Ø/2〉 at room temperature is about 16.710–16 m/s2 and 3.2310–16 m/s2, respectively, (ηEG = 14 (mPa.s)). Such low diffusion coefficients, relative to the above estimated border to border separation distances of 〈ξ〉 ≈ 3 nm and 〈ξ〉 ≈ 12 nm seem to indicate that the AgNPs are unlikely to agglomerate at room temperature. From crystallographic point of view, the selected area electron diffraction (SAED pattern) of Fig. In Fig. .41, Vi and Lue et al.42, as well as Zhang et al.43. It is observed that such an anchoring process of the Ag NPs onto the GNs carpets is stable with time, confirming the homogeneous dispersion and the stability with time of such an anchoring. We also investigated the nature of such Ag NPs liaison onto GNs in order to understand its physical/chemical nature. The high-resolution transmission electron microscopy observations Fig. Figure Figure −1. One can distinguish clearly the D  and G  bands as well as the 2D and the 2G bands representative of a few layered graphene. As established, the D peak originates from TO phonon mode near K points in the Brillouin zone. This peak is only activated by structural defects as well as second order Raman scattering process through the intervalley double resonance and demonstrates the crystallinity of the sample44 in congruence the previous observations. The Raman spectra also show the first order Raman peak located at 1,580 cm−1 denoted by G band originating from the doubly degenerate zone center phonon E2g mode which corresponds to the C–C stretching modes43. The peak appearing at 2,680 cm−1 is the second order Raman peak, denoted by 2D and overtones D mode44. The intensity of the D peak  intensity is higher than that of the G peak with an intensity ratio (ID/IG) of about ~ 1% pointing to a high crystallinity within the graphene nanosheet45.Figure 2H6O2 if any at least at the 10% limit of detection of XRD technique limitation. This does not provide evidence of a potential physisorption or chemisorption at the surface of the 2-D Graphene sheets of Oxygen from the surrounding host fluid C2H6O2 molecules as corroborated by the previous EDS observations.Figure 35. This wide plasmon peak implies the non-agglomeration of the Ag nanoparticles as well as their small average size. Based on Mie Scattering Theory (MST), where the width at half maximum of the plasmon peak is related to the average size via the relation: 1/2〉 = γ0 + AvF/〈Ø〉  (γ0 + AvF/〈Ø〉)), where 〈Ø〉 is the size of nanoparticle, A = 3/4 for silver, γ0 is the velocity of bulk scattering (51012 deg s−1), vF is the Fermi velocity (for silver vF = 1.39106 deg m/s), the corresponding average size is about 〈Ø〉 ≈ 21.7 nm in the size range of those observed in the previous HRTEM observations. In the spectral range of 200–300 nm, there is a strong absorbance peak centered at 230 nm with a broad shoulder in both GNs-EG and AgNPs-GNs-EG samples as shown in the Fig. 46. In addition to the wide plasmon peak of the AgNPs, and the proper GNs absorbances, there is an additional set of quasi sharp absorbances in the AgNP-GNs-EG sample. These various substructure absorption peaks within the UV spectral range of 200—350 nm, are simulated and are summarized in Table 47. Likewise, both AgNPs-GNs-EG and GNs-EG absorbances exhibit a band at 261 nm which is attributed to pure graphene, while the 273 nm absorbance indicates the restoration of extensive conjugated sp2 carbon network51. The peaks at 221 nm and 430 nm are attributed to graphene oxide as reported previously52. This graphene oxide seems to indicate a significant chemical binding between the graphene sheets and the host fluid molecules of C2H6O2 as stipulated in the previous EDS observations.Figure mentclass2pt{minim57. However, for AgNPs-GNs-EG nanofluid there was a slight increase with temperature with a slope of about 7.0 × 10−4 W/mT2. At 45 °C, the thermal conductivity reaches 0.414 W/mK. When compared with the results, reported in literature on dispersed graphene in ethylene glycol summarized as shown in Table 55 the current enhancement value of 32.3% seems to be the highest so far.Figure In this contribution, we caused a multicomponent nanofluid based on silver NPs densely decorated with 2D graphene sheets was dispersed homogeneously with ethylene glycol as host base fluid sample. Such a stable multicomponent nanofluid was synthesized by laser liquid solid interaction in a two steps approach. Such a technology approach allows a significant anchorage of the AgNPs onto the graphene 2D sheets. The thermal conductivity of such nanofluids was measured to be 0.414 W/mK corresponding to a remarkable enhancement of the order of 32.3% compared with ethyl glycol. This is the highest reported value for a graphene based nanofluid. Further studies are required in order to understand the reason for the high anchoring of the Ag NPs onto the GNs sheets as well as the possibility of engineering Ag nanorods decorated GNs-EG based nanofluids.