Respiratory rate and skin humidity are important physiological signals and have become an important basis for disease diagnosis, and they can be monitored by humidity sensors. However, it is difficult to employ high-quality humidity sensors on a broad scale due to their high cost and complex fabrication. Here, we propose a reliable, convenient, and efficient method to mass-produce humidity sensors. A capacitive humidity sensor is obtained by ablating a polyimide (PI) film with a picosecond laser to produce an interdigital electrode (IDE), followed by drop-casting graphene oxide (GO) as a moisture-sensitive material on the electrode. The sensor has long-time stability, a wide relative humidity (RH) detection range from 10% to 90%, and high sensitivity (3862 pF/%RH). In comparison to previous methods, the technology avoids the complex procedures and expensive costs of conventional interdigital electrode preparation. Furthermore, we discuss the effects of the electrode gap size and the amount of graphene oxide on humidity sensor performance, analyze the humidity sensing mechanism by impedance spectrum, and finally perform the monitoring of human respiratory rate and skin humidity change in a non-contact manner. Humidity sensors have a large number of applications in many fields, such as agricultural production, industrial manufacturing, food processing, and environmental monitoring ,2,3,4. H2-modified graphene oxide on Au electrodes as a sensitive film [3-hybridized carbon atoms experience photothermal conversion to sp2-hybridized carbon atoms, and the polymer is converted into a conductive graphene structure. The approach not only reduces the cost of electrodes, but is non-toxic, photomask-free, and has a controllable electrode shape, which has the potential for large-scale applications.There are many kinds of humidity sensors, mainly capacitance ,8, impedive film . The impive film ,38,39, aive film ,41, whicIn this paper, a picosecond laser is utilized to ablate polyimide (PI) film to form a conductive interdigital electrode (IDE), and then GO solution is coated onto the interdigital electrode to create a humidity-sensitive layer. This method not only reduces the cost of the sensor but also simplifies the process. The humidity is monitored by measuring the capacitance of the sensor. The sensor has a high capacitive response over an RH range of 10% to 90%. The sensors also feature small humidity hysteresis and good stability. To obtain the best performance, sensors with different gap sizes of the interdigital electrode and different GO amounts are prepared for analysis. The excellent performance of the sensor is demonstrated by breathing monitoring and non-contact sensing of skin humidity.Polyimide film was purchased from Xinhongsen Technology Co., Ltd. . The conductive silver glue was obtained from Kaixiang Electronic Products Co., Ltd. . Graphene oxide (2 mg/mL) was purchased from Guoheng Qihang Co., Ltd. .As shown in A Leica DVM6 depth-of-field (DOF) microscope was used to observe the morphology of GO and LIG. Raman spectra were observed with a Raman spectroscopy . Raman excitation source was a laser of 532 nm. The sensor capacitance was measured using an LCR meter . A high-precision Bluetooth humidity sensor  was used to monitor humidity with a humidity accuracy of 1.5% RH and humidity resolution of 0.1% RH. The gas flow rate was controlled by a gas flow controller .2: 78%, O2: 22%) bottle was dry gas with a flow rate of 1 L/min, and the gas bubbling through the deionized water was moist gas. The dry gas was mixed with the moist gas in different proportions by adjusting the flow meter to obtain a stable humidity environment. We placed the sensor in the test chamber and measured its capacitance with LCR. The ambient humidity in the test chamber could be measured in real time by a commercial hygrometer. Humidity sensitivity is denoted by the formula S = RHC denotes the capacitance at RH% humidity, and C0 denotes the capacitance at RH0% humidity. The response and recovery time of the sensor is the time it takes for the sensor capacitance to go from the initial value to 90% of the stable value.As shown in 2, 41.34 mm2, and 59.34 mm2, respectively, with an electrode width of 290 μm, as shown in M-N, where M represents the gap size, and N represents the volume of GO solution. For example, in LIG150-60, 150 represents the gap size of 150 μm, and 60 represents 60 μL of GO solution.Three sensors with different electrode gaps are designed, 50 μm, 150 μm, and 360 μm corresponding to electrode areas of 32.34 mmAs shown in −1, 1580 cm−1, and 2695 cm−1. These peaks are identified as a graphene structure, with the D peak representing the amorphous carbon structure and the G peak representing the carbon–carbon bond stretching, which can be considered as a graphite structure. The ratio of 2D to G can be a good indication of the presence of high-quality monolayer graphene. I2D/IG is 0.5 near the center of the electrode, and it can be inferred that there is a multi-layer graphene with around 4–5 layers [As shown in 5 layers . There i0S/d for parallel-plate capacitor, where d is the gap size, S is the cross-sectional area of the electrode finger, and ε is the relative permittivity of the interdigital electrodes, ε0 is the dielectric constant in vacuum, as shown in water is 78.4, which is much higher than 3.4 of εPI in the electrostatic field. When the humidity rises, the PI film absorbs more water molecules, increasing the dielectric constant of PI film and thus an increase in capacitance. The high dielectric constant of water is because water molecules are polarized in an electric field. This polarization can better respond to the low-frequency electric field. When the frequency of the electric field increases, the polarization speed of water molecules cannot keep up with the change in the direction of the electric field [ic field ,45. Ther150-60 is the highest, as seen in 150-60 changes from 18.8 pF to 3.09 × 105 pF. The sensitivity reaches 3862 pF/%RH, which is much greater than that of PI-based sensors. It is not sufficient to explain the change in capacitance by an increase in the dielectric constant between the interdigital electrodes. In addition, the capacitance response increased more significantly at high RH than at low RH. We can try to explain this phenomenon by impedance spectroscopy.For the three gap sizes  of GO-based sensors, the drop-coated GO solution is 30 μL, 60 μL, 90 μL, and 120 μL, among which the sensitivity of LIG150-60 impedance spectrum, at 10% to 30% humidity, it is an approximate semicircle; the diameter of the semicircle can be used as the internal resistance of the sensor [2O + H3O+ = H3O+ + H2O), [As seen in e sensor , about 1e sensor , water m + H2O), ,48, hydr150-60 at different humidity levels. From the phase diagram, it can be seen that the phase curves at all humidity levels basically intersect at the position of 100 Hz, which indicates that the phase angle is basically unchanged at 100 Hz regardless of the humidity level. Therefore, the change in impedance at 100 Hz can represent the change in capacitance. At other frequencies, the impedance and phase angle vary with humidity, and the uncertainty increases. The selection of 100 Hz frequency as the test frequency also considers this factor. As can be seen from the impedance diagram in 2. The details are shown in To investigate the effect of different gaps and different GO amounts on the performance of the humidity sensor, we measured the capacitive response of the sensor with different parameters, as shown in 150-60 experiencing five cycles between 80% and 40% RH, and it can be seen that the adsorption time is 58 s and the desorption time is 15 s. As mentioned above, water molecules will combine with hydrophilic groups and adsorb on the GO surface when the humidity rises. However, the adsorption process is not uniform, and there may be some areas where more water molecules have been adsorbed, while others may not have water molecules yet, which results in the ion diffusion process being hindered. The first layer of chemisorption must be completed on the GO surface, and the Grotthuss effect is produced so that the ions can diffuse sufficiently and the diffusion capacitance can increase rapidly. Additionally, the desorption process is a shift from high to low humidity; at high humidity, a complete layer of physically adsorbed water molecules has formed on the GO surface, but the thickness of the water molecule layer is not uniform. In areas where there are fewer water molecules, the water molecules quickly and completely release from the GO surface. As long as these areas are free of water molecules, the diffusion process is impeded, and the diffusion capacitance is rapidly reduced. Since the area where water molecules need to be released is smaller, the time is also shorter. In the adsorption process, on the other hand, a much larger adsorption area is required to form a complete chemisorption layer on the GO surface. The adsorption time is also much longer.150-60 sensor weekly for a period of 42 days to evaluate its long-term stability. The capacitance varies very little at each humidity level, which confirms that the sensor has long-term stability, as shown in As we all know, humidity hysteresis is a key parameter for humidity sensor performance. The black and red curves in Due to the sensor’s superior performance, it can be used for non-contact monitoring of human physiological signals, such as sweating, breathing, and non-contact fingertip techniques. Non-contact sensing is better than contact sense because it prevents sweat from contaminating the sensing surface and allows the sensor to be used repeatedly. 2. Due to the sensor’s high sensitivity, rapid response time, and minimal hysteresis, it can monitor human physiological signs, such as breathing and perspiration.In this work, we ablated the PI film by a picosecond laser to obtain an interdigital electrode and enhanced the humidity response by GO. This method has a simple fabrication process and lower cost. After the PI film is ablated by laser, graphene is generated on the electrode of the PI-based sensor, which contributes to the conductivity, and the smaller the electrode gap size, the greater the capacitive response. The effects of the electrode gap size and the amount of GO on the performance of GO-based sensors are discussed. The result shows that the sensitivity of the sensor is inversely related to the electrode gap size when the amount of GO is low, and when the amount of GO is large, the sensitivity is proportionate to the electrode gap. Meanwhile, there is an optimal value of the amount of GO, and the sensor is the most sensitive when the drop-coated GO is in the range of 1.45–1.86 μL/mm


Quantum Hall (QH) edge channels propagating along the periphery of two-dimensional (2D) electron gases under perpendicular magnetic field are a major paradigm in physics. However, groundbreaking experiments that could use them in graphene are hampered by the conjecture that QH edge channels undergo a reconstruction with additional nontopological upstream modes. By performing scanning tunneling spectroscopy up to the edge of a graphene flake on hexagonal boron nitride, we show that QH edge channels are confined to a few magnetic lengths at the crystal edges. This implies that they are ideal 1D chiral channels defined by boundary conditions of vanishing electronic wave functions at the crystal edges, hence free of electrostatic reconstruction. We further evidence a uniform charge carrier density at the edges, incompatible with the existence of upstream modes. This work has profound implications for electron and heat transport experiments in graphene-based systems and other 2D crystalline materials. Scanning tunneling spectroscopy in graphene reveals ideal quantum Hall edge channels, strongly confined at the physical edge. In 1982, 2 years after the discovery of the quantum Hall (QH) effect . Ince is the electron charge, and B is the magnetic field) from the crystal edge, leaving no room for edge reconstruction.Nonreconstructed edge states can substantially clarify QH edge transport with virtually ideal 1D edge states (2 substrate that serves as a back-gate electrode (see Methods). The graphene flake is contacted by a Cr/Pt/Au trilayer that allows to apply a voltage bias Vb and collect a tunnel current It via the STM tip. All experiments presented here are performed at a temperature of 4.2 K and a perpendicular magnetic field of 14 T.Here, we unveil the real-space structure of the QH edge states of graphene lying on an insulating hexagonal boron nitride (hBN) flake and evidence the absence of edge reconstruction by performing scanning tunneling spectroscopy up to the graphene crystal edge under strong perpendicular magnetic field. We achieved this by overcoming the long-standing experimental challenge . Our samVg = −5.4 V. Tunneling spectroscopy of Landau levels (G(Vb) = dIt/dVb that is proportional to the local density of states. We show in G as a function of tip distance perpendicular to the graphene edge dedge and bias voltage Vb. Far from the edge, Landau levels are readily identified as bright conductance peaks that we label LLN, where N is the Landau level index. These conductance peaks are conspicuously stable upon approaching the edge on the left of the figure. Within 40 nm from the edge, we observe a suppression of the Landau level peak heights . The range of dedge/Bl coincides with the range of displacement in dedge the higher their Landau level index, and on the same spatial scale as observed experimentally in dedge (solid lines) including a single realization of on-site disorder (dashed lines). The latter breaks the particle-hole symmetry of the spectrum and thus may contribute to the asymmetries observed in the zeroth Landau level peaks.To substantiate our finding, we performed numerical simulations of the local density of states of a charge-neutral graphene ribbon with an armchair edge under perpendicular magnetic field see  (16–18).The question of charge carrier homogeneity is critical for graphene transport. A body of work has shown anomalous asymmetry in some transport properties supplemented by scanning probe investigations , when the Fermi level is pinned by localized states in the cyclotron gap separating LL0 from LL1. There, because of the little density of localized states as compared to the highly degenerate Landau levels, a small variation of charge density would result in a substantial shift of the Landau levels in the tunneling spectra. 0, LL−1, and LL−2) stay at the same energy over the scan and vanish at about 20 nm from the edge, clearly indicating the absence of charge accumulation. We further performed systematic gate-tuned tunneling spectroscopy maps at various locations, from 500 to 5 nm from the edge . n = (−1.5 ± 1.1) × 1011 cm−2.To enhance the sensitivity of the spectroscopy to possible charge inhomogeneities, we performed similar measurements at filling factor ν = 2  resist was used to pattern a guiding markerfield on the whole 5 mm–by–5 mm substrate to drive the STM tip toward the device and to locate the graphene edge. Cr/Pt/Au electrodes contacting the graphene flake were also patterned by electron beam lithography and metalized by e-gun evaporation. The sample was thermally annealed at 350°C in vacuum under a halogen lamp to remove resist residues and clean graphene before being mounted into the STM where it was heated in situ during the cooling to 4.2 K.The graphene/hBN heterostructure was assembled from exfoliated flakes with the van der Waals pickup technique using a polypropylene carbonate polymer (x axis is perpendicular to the edge direction (as imaged by STM) and the y axis is parallel to the edge with a size of a few tens of nanometers. A safety condition is added to the tip vertical z-position controller to prevent the crashing into the hBN flake beyond the graphene edge: if the z position reaches a threshold , the tip is withdrawn and the CITS ends. Imaging of the Kekulé bond order was carried out in STM constant-height mode after tuning the graphene to charge neutrality with the back gate, at a bias voltage corresponding to the energy of the LL0+ peak  with W/t = 0.3.To compute the local density of states shown in


The combination of polyethylene terephthalate (PET), one of the most used polymers in the textile industry, with graphene, one of the most outstanding conductive materials in recent years, represents a promising strategy for the preparation of conductive textiles. This study focuses on the preparation of mechanically stable and conductive polymer textiles and describes the preparation of PET/graphene fibers by the dry-jet wet-spinning method from nanocomposite solutions in trifluoroacetic acid. Nanoindentation results show that the addition of a small amount of graphene (2 wt.%) to the glassy PET fibers produces a significant modulus and hardness enhancement (≈10%) that can be partly attributed to the intrinsic mechanical properties of graphene but also to the promotion of crystallinity. Higher graphene loadings up to 5 wt.% are found to produce additional mechanical improvements up to ≈20% that can be merely attributed to the superior properties of the filler. Moreover, the nanocomposite fibers display an electrical conductivity percolation threshold over 2 wt.% approaching ≈0.2 S/cm for the largest graphene loading. Finally, bending tests on the nanocomposite fibers show that the good electrical conductivity can be preserved under cyclic mechanical loading. Electronic textiles (e-textiles) and, in particular, smart textiles, which are e-textiles capable of sensing stimuli from the environment and react accordingly, have gained great interest in recent years ,2,3,4,5.The preparation of such textiles requires the efficient combination of the polymer fibers with the electronic component, which has evolved from the bulky electronic devices of the late 1980s, focused on meeting specific requirements mainly for military purposes, to the smaller electronic components incorporated into fabrics and principally dedicated to sensing environmental or body parameters for healthcare. However, the real integration of electronic components into textiles requires the fabrication of devices directly on the fibers using high-performance materials that allow seamless incorporation into fabrics. Consequently, fiber-based devices are rapidly developing as an alternative and versatile platform that can offer functionality in a variety of configurations due to their peculiar geometry, aspect ratio, feature sizes, and mechanical properties [The fast-growing interest in smart textiles has oriented investigations towards the search for new electronic materials beyond metals, since the incorporation of the latter into textiles involves complex, expensive, difficult-to-scale-up and environmentally unfriendly processes. In addition, the presence of metals in the final material adds much weight and toxicity and imparts limited mechanical compliance to stress. Conducting polymers (CP) have been proposed as conductive elements in textiles due to their high solution processability, light weight and mechanical properties, which are closer to textile polymers than metals . Very reGraphene or its derivatives have been incorporated into textiles principally using coating techniques from appropriate inks ,18,19,20−1 have been prepared by melt mixing, where the dispersion of GNP in the matrix is assisted by an ethylene methyl acrylate copolymer (EMA) [Beyond the initial interest, the research ,26,27,28er (EMA) . Neverther (EMA) ,33,37.According to preceding studies, the fiber morphology and mechanical and electrical properties are directly related to the choice of fiber formation process (dry or wet-jet or combined), including the type of precipitation bath ,39,40. H−1) was kindly supplied by AITIIP (Zaragoza). Trifluoroacetic acid, TFA (CAS No.: 76-05-1) and methanol (CAS No.: 67-56-1) were supplied by Sigma-Aldrich.PET (NovaPET CR pellets with intrinsic viscosity 0.8 dL·g2·g−1 and oxygen content (XPS) of ~2.5% was obtained from Avanzare Nanotechnology . The G was characterized by SEM and Raman spectroscopy  with a thickness of <3 layers, an average particle size of ~40 µm, a BET surface of 480 mtroscopy  and the ne oxide . The ele−1 was prepared using a Hielscher USP400s sonication probe with a sonotrode tip diameter of 7 mm, at a working frequency of 24 kHz and amplitude of 40%, for 15 min. A polymer solution with a PET concentration of ~18 wt.% was prepared by dissolving it in TFA under magnetic stirring. Subsequently, appropriate amounts of each component were mixed under magnetic stirring and part of the solvent was allowed to evaporate at 60–68 °C in order to increase the viscosity of the mixture. Nanocomposite inks with 2, 3, 4 and 5 wt.% of graphene were studied.For the preparation of the fiber’s precursor PET/graphene inks, the filler and the polymer were separately dispersed and dissolved in TFA. A graphene dispersion with a concentration of 3 mg·mLTM precision fluid dispenser from NORDSOM EFD, coupled to a polypropylene syringe with a stainless-steel needle tip. After testing needles of different diameters, a 0.84 mm diameter was selected. The air gap between the needle and the methanol coagulation bath was ~3 cm and a pressure of 25.0 psi was applied. The fibers were dried under a vacuum at 60–65 °C for 96 h.Fiber spinning was conducted via the dry-jet wet-spinning approach using an ULTIMUSThe dispersion of graphene in the fibers was examined by scanning electron microscopy (SEM) using a Hitachi SU8000 field emission microscope . The nanocomposite fibers were cryofractured and images were collected at 0.8 kV, using a secondary electron and backscattered electron detector combination.−1, under an inert atmosphere . Samples were analyzed using the TA Instruments Universal Analysis 2000 software .Thermogravimetric analysis (TGA) was carried out using a TA Instruments Q50 thermobalance  between 50 and 800 °C at a heating rate of 10 °C·min−1 under a nitrogen atmosphere (flow of 50 mL·min−1).Differential scanning calorimetry was performed in a DSC25 with a RSC90 refrigerated cooling system (TA Instruments). Samples of approximately 5 mg were placed in hermetically sealed aluminum pans. Scans were carried out from 0 to 300 °C at 10 °C·mincX, was calculated from the ratio of the area under the crystalline peak to that of the total diffraction curve.Two-dimensional wide-angle X-ray diffraction (WAXS) images were obtained with the fiber axis perpendicular to the incident beam, using a Bruker AXN diffractometer operating at 50 kV and 1 mA, with wavelength λ = 0.1542 nm. The beam size was around 100 μm × 100 μm. A photon detector was used with a resolution of 1024 × 768 pixels and 135 μm/pixel. The sample-to-detector distance was 40 mm. Background-subtracted diffraction patterns were analyzed using the FIT2D software . AzimuthIn order to determine the effect of the graphene loading on the electrical conductivity, the fibers were pressed into thin films and DC conductivity measurements were carried out using a four-probe setup comprising a dc low-current source (LCS-02) and a digital microvoltmeter (DMV-001) from Scientific Equipment & Services Pvt, Ltd. . For the study of electrical conductivity under cyclic bending, the electrical resistance of each type of fiber was measured in a two-contact configuration with silver paint tracks and using a low-current digital voltammeter. The fibers were subjected to a 90-degree bending regime for 3 s, and then the perturbation was released to allow the fibers to recover their original extended shape, and the electrical resistance was recorded.P was incremented exponentially with time to achieve a constant strain rate (P’/P = 0.05 s−1), and, at the same time, a small oscillation force was applied. A continuous measurement of the stiffness as a function of indent depth h was determined from the phase lag between the oscillation force and the harmonic displacement produced [E’ and hardness H could be calculated following the procedure of Oliver and Pharr and assuming elastic–viscoelastic correspondence [Nanoindentation tests were carried out by the application of a small load (≈15 mN) on the fibers’ cross-sections. Fibers were mounted vertically using a plastic clip and subsequently embedded in epoxy resin. The cross-sections of the fibers were exposed using a microtome and the surfaces were polished using progressively finer sandpaper and finally finished with a microcloth lubricated with alumina paste. The resulting cylindrical blocks were placed on the platform of a G200 nanoindenter . A low-load head (DCM) including a Berkovich indenter was used. Experiments were carried out under dynamic testing. The load produced . Finallypondence ,45.E* at 1 Hz were obtained from the slope of the plot of the imposed applied tensile dynamic stress versus the produced tensile dynamic strain.Dynamic Mechanical Analysis (DMA) was performed in a Perkin Elmer DMA7 using fiber extension fixtures at room temperature (T = 20 °C) in the controlled tensile stress mode. Measurements were made in the linear viscoelastic region (LVR) by dynamic force sweeps between 100 and 3000 mN at a fixed frequency of 1 Hz. The generated dynamic strain ε as a consequence of the imposed dynamic stress σ* varied up to a maximum value of 0.35 % (ε = 0.0035), well within the LVR. The values of the complex tensile modulus As the real integration of electronic components into textiles requires the incorporation of nanoscale conductive fillers directly in the fibers, the optimization of the spinning conditions  is fundamental to obtain fibers with balanced mechanical and electrical properties. We initially approached wet spinning from TFA solutions and coagulation in methanol and water baths, but either no fiber formation or fibers with poor mechanical stability were produced. TFA has been suggested as a solvent that meets the solubility–spinnability requirements for the production of PET fibers ,47, and The morphology of all PET/graphene fibers was evaluated by SEM . Images Fibers were studied in depth by analyzing the cross-sections on cryofractured samples. From The thermal stability of PET/graphene nanocomposites under nitrogen and air atmospheres was investigated and results are shown in cX = 23–29%. Most interesting is the influence of graphene on the PET fiber nanostructure. The WAXS patterns of pristine PET fibers exhibit a clear broad halo characteristic of an amorphous material see . In contThe fibers were also analyzed by DSC and the curves of the first heating scan are shown in E´ and H as a function of indenter displacement into the surface. Error bars arise from the statistical analysis of indentations produced at different locations to cover the whole fiber cross-sectional area. The high noise at shallow depths h < 500 nm can be associated with the tip–sample interaction and the surface roughness of each particular fiber [E´ and H values eventually remain constant with an indent depth beyond h ≈ 1 μm. Moreover, also beyond this point, the standard deviation in the data is quite similar in all samples. Hence, it seems that the addition of graphene does not introduce mechanical heterogeneities at the micrometer scale, and E´ and H for h > 1 μm represent average values for each material. It is worth recalling that the deformation volume typically extends up to ≈10 times the indent depth for the plastic field and ≈20 times for elastic deformation.The structural differences described above upon the addition of graphene can be correlated to variations in the mechanical behavior. Nanoindentation testing was carried out on the cross-sections of all PET/graphene and pristine PET fibers, and E´ and H values taken at h = 2 μm as a function of graphene content for all materials investigated. In the first place, the E´ and H values for the pristine PET fiber are in agreement with earlier values in other PET systems [E´ and H improvements of 28% and 42%, respectively, are found. The initial E´ and H improvement with the incorporation of the smallest quantity of graphene (2 wt.%) can be associated with both the development of matrix crystallinity , which is probably due to the development of crystallinity and the superior properties of the graphene filler. The longitudinal strength of the 4 wt.% graphene fiber (E* = 5.0 ± 0.3 GPa) only increased by around ≈25% compared to non-reinforced PET and is slightly lower than that for the sample with a loading of 2 wt.%. This may be attributed to heterogeneities along the sample and a decrease in interfacial filler/polymer interactions due to the formation of graphene aggregates. In this respect, it is also worth commenting that the modulus values could not be determined for PET/graphene fibers with the highest graphene content (5 wt.%), as a consequence of fiber breaking events during sample fixation. Such observations suggest that high graphene loading promotes fiber fragility, and this could also be correlated with the enhanced surface roughness observed by electron microscopy with increasing graphene content  such as sensors, wearable heating devices and healthcare monitoring devices, among others. Regarding e-textile applications, it is very important that the conducting fibers preserve the conductivity under mechanical deformation, recovering their initial values after the perturbation is released. Thus, the variation in the resistance of the nanocomposite fibers under repetitive bending/release cycles was investigated. The optimized dry-jet wet-spinning preparation of PET/graphene nanocomposite fibers with balanced mechanical and electrical properties using precision fluid dispensing equipment is presented. TFA and methanol were selected as the solvent and precipitation bath, respectively.Fibers with good thermal stability and a homogeneous dispersion of graphene were developed. Graphene was found to induce crystallinity in the fibers, as observed by X-ray diffraction and DSC. Such structural changes, together with the intrinsic superior properties of graphene, produce a modulus and hardness improvement that reaches 28% and 42% for the largest quantity of graphene (5 wt.%). Finally, the nanocomposite fibers exhibit good electrical conductivity stability under cyclic bending. Hence, the PET/graphene fibers appear to be promising candidates for applications in e-textiles.


The reliability of analysis is becoming increasinglyimportantas point-of-care diagnostics are transitioning from single-analytedetection toward multiplexed multianalyte detection. Multianalytedetection benefits greatly from complementary metal-oxide semiconductor(CMOS) integrated sensing solutions, offering miniaturized multiplexedsensing arrays with integrated readout electronics and extremely largesensor counts. The development of CMOS back end of line integrationcompatible graphene field-effect transistor (GFET)-based biosensinghas been rapid during the past few years, in terms of both the fabricationscale-up and functionalization toward biorecognition from real samplematrices. The next steps in industrialization relate to improvingreliability and require increased statistics. Regarding functionalizationtoward truly quantitative sensors, on-chip bioassays with improvedstatistics require sensor arrays with reduced variability in functionalization.Such multiplexed bioassays, whether based on graphene or on othersensitive nanomaterials, are among the most promising technologiesfor label-free electrical biosensing. As an important step towardthat, we report wafer-scale fabrication of CMOS-integrated GFET arrayswith high yield and uniformity, designed especially for biosensingapplications. We demonstrate the operation of the sensing platformarray with 512 GFETs in simultaneous detection for the sodium chlorideconcentration series. This platform offers a truly statistical approachon GFET-based biosensing and further to quantitative and multianalytesensing. The reported techniques can also be applied to other fieldsrelying on functionalized GFETs, such as gas or chemical sensing orinfrared imaging. Traditionally multiplexed testing is done on enzyme-linkedimmunosorbent assays (ELISA),2 where everyanalyte and receptor pair is measured individually. More advancedmultiplexed multianalyte diagnostic solutions can be achieved on asingle chip by using sensor arrays with integrated complementary metal-oxidesemiconductor (CMOS) readout. The benefits of CMOS integration includelow cost, dense array formation, lower power consumption, label-freedetection mechanism, readout integration, and smaller device dimensions4 which all are very beneficial especially for PoC applications.Point-of-care (PoC) diagnosticsis transitioning from single-analytedetection toward multiplexed multianalyte detection, which increasesthe importance of reliable statistical analysis.5 attomolar-level DNA hybridization,6 zika-virus detection,7 and recently also for COVID-19 causative virus monitoring.9 With CMOS-integrated GFET arrays, it is possible to overcome thecommon problems in field-effect biosensing related to the limiteddetection ranges in real-life sample matrices by utilizing PEG-polymer-aidedfunctionalization schemes.10 This is all very promisingconsidering the prospects of GFETs in label-free electrical sensingfor early diagnostics and PoC applications requiring sensitive andspecific biorecognition from biological sample matrices.One of the most promising label-free biosensing technologies, compatiblewith CMOS integration, is sensitive and selective graphene field-effecttransistor (GFET) sensors based on chemical vapor deposited (CVD)graphene. The use of GFETs has been demonstrated for peptides andantibodies,11 organic semiconductor,12 and semiconductor silicon nanowire (SiNW)FETsensors.13 All of these technologies stillhave some limitations that need to be addressed to get the full potentialout. Organic FETs have lower charge sensitivity and dynamic rangewhen compared to GFETs and SiNWFETs due to larger channel dimensions.14 SiNWFETs suffer from high device-to-device variationsand low carrier mobility.15 CNTFETs havelow current output, small active areas, and heterogeneous semiconductorand metal mixtures.16 These aspects canlead to a large variation between devices and low manufacturing yieldsfor CNTFETs.16 GFETs offer at least similarsensitivity and easier fabrication when compared with the competingtechnologies. This makes GFETs a very interesting and cost-effectiveapproach for highly sensitive label-free biosensors.Otherpromising, compatible with CMOS back end of line (BEOL) integration,FET-based technologies for biosensing are based on carbon nanotube(CNT),16 ion-selective FETs (ISFETs),17 extendedgate field-effect transistors (EGFETs),18 and film bulk acoustic resonators (FBARs).19 ISFETs and EGFETs are easy to manufacture on top of the CMOS withstandard processes.18 ISFETs also suffer typicallyfrom device stability and drift issues.21 EGFETs wereoriginally proposed as a solution to solve the drift and stabilityissues of ISFETs, and the sensitivity of the devices can be improveddue to the possibility for larger sensing areas.23 CNT-based sensors and FBARs are harder to fabricate on CMOS readoutand easily suffer from low yields in fabrication.19 With CNT-based sensors, the as-grown angle of CNTs is difficultto control without deliberate alignment, and deposited films showhigh variability in contact resistance.24 FBARs suffer from low yields due to residual stresses in the suspendedmembrane and piezoelectric layer and lengthy etching processes.25 The possibility to integrate CVD graphene withCMOS readout on chip scale has been demonstrated earlier for broadbandimage detectors26 and gas sensing.27 The possibilities of graphene integration intoCMOS BEOL have been discussed by Neumaier et al.28 However, full BEOL integration of the graphene-based biosensorarrays has not been realized due to the lack of wafer-scale CMOS readoutintegration.Previously,scalable CMOS-integrated biosensor solutions have beenreported for example with carbon nanotubes,In this work, we demonstrate for the first-timewafer-scale CMOSintegration of graphene FETs for biosensing. Functionality of theintegrated GFETs for biosensing is demonstrated using simultaneousmeasurement of 512 GFETs for a series of sodium chloride concentrationmeasurement. In our approach, we integrate GFETs on a CMOS multiplexerplatform that enables the simultaneous measurement of hundreds ofGFETs. The work done here further enables a statistical approach formultianalyte biosensing since the reliable detection of a certainanalyte requires several devices and statistics for the analysis.2The CMOS wafers used for GFETintegration were manufactured using a standard commercial technologyprovided by XFAB. The 200 mm CMOS wafers utilize a 0.35 μm analogueCMOS process node with up to four metallization layers. The CMOS technologyincludes a wide range of active devices and high-performance analogdevices.The CMOS read-out platform was designed to supportseveral types of postprocessed sensors. The architecture of the CMOSsystem and the local readout for a resistive GFET are illustratedin The global digital control is used to select theGFETs by enablingpixel-level local CMOS switches. For each GFET there is an optionto select either two-wire or four-wire connection to enable a Kelvin-typeresistance measurement. The global control of the resistive GFETsoperates as follows. The digital control SPI interface is utilizedto fetch a code string to address the GFET(s) to be analyzed. Theaddress decoder selects the corresponding GFET(s) and enables analogueswitches and the output multiplexer. The multiplexer output is inthe case of the GFET read-out fed directly to ASIC pads. In this ASICdesign, the GFET resistance is measured using external laboratoryequipment. CMOS readout also enables the integration of the resistancemeter functionality on the chip level, which will be evaluated infuture designs to increase the functionality of the system.29 The selectedoptions result in 512 individual GFETs being fabricated for each chip.A microscope image of the fabricated GFET sensors is shown in The postprocessed sensors were designed to utilize half of theavailable readouts to allow space for local liquid gate electrodesand in addition to an on-chip global liquid gate electrode. Theseliquid gate electrodes can gate the graphene channels in biosensingmeasurements. The global platinum liquid gate electrode is formedof 64 stripes that are 12 μm wide and 6400 μm long. Theusage of similar pseudoreference electrodes has been demonstratedto work reliably for GFETs.2O3 grown by atomic layer deposition (ALD). ALDgrowth on graphene was seeded using an evaporated 1-nm-thick Al filmoxidized in the evaporation chamber.A schematic illustration of the postprocessed GFETon the CMOSreadout is presented in 2O3 inH3PO4 followed by etchingthe graphene in an O2 plasma. After the graphene patterning,the devices were passivated by 50-nm-thick ALD Al2O3 and 120-nm-thick plasma-enhanced chemical vapor deposition(PECVD) Si3N4. Finally, the passivation of thedevices was opened from the top of the graphene channels by dry etchingof Si3N4 and wet etching the Al2O3. The success of the channel opening step was evaluated byan etch step and surface characterization of the graphene channelsby atomic force microscopy (AFM) . The graphene quality at the end of the processing was also characterizedby Raman spectroscopy . Thewafers were then diced into single CMOS microsystem chips.Graphene patterning wasdone by wet etching the protective AlVG) is controlledby using the global on-chip Pt liquid gate. The current (I) in the graphene channel is measured with a 0.1 V source–drainbias (VSD) over the channel.For the characterization,five sensor chips were selected across the diced wafer. The chipswere wire bonded onto chip carriers for electrical measurements. Adetector chip wire bonded on a chip carrier is shown in The graphene detector surface was cleaned withisopropanol anddeionized water (DIW), before characterization of the devices in DIWand in a series of sodium chloride (NaCl) with different concentrations.The measurement in DIW was repeated once before and after the actualmeasurement series to ensure that the devices are stable. For DIWand each concentration of NaCl, the sensor surface was further washedfive times with a 100 μL droplet of the corresponding solutionto ensure that the concentration on top of the chip was correct.e. It can be calculated for the monovalent electrolytes at room temperatureaccording to the following equation:I is the ionic strengthof the solution.To evaluate the response of the sensor chip to the physiologicalsolutions, the ionic strength of the sample solution was varied. Thetests included measurements in DIW and 1, 10, and 100 mM NaCl solutions.The channel of the graphene FETs is charged, and an electrical doublelayer (EDL) is formed on the graphene–liquid interface to neutralizethe charged surface. The thickness of the EDL can be estimated byusing the Debye length, which corresponds to the distance at whichthe electric potential has decreased in magnitude by 1/31 at 25 °C, and approximately the same value applies for thelow NaCl concentration solutions.32Based on the formula, the Debye lengths forthe 1, 10, and 100mM NaCl concentrations are 9.6 3.0, and 0.96 nm, respectively. TheDebye length corresponds approximately to the gate oxide thicknessof the GFETs. The dielectric constant of the DIW is 78.43In this work, wefocused on studying 512 GFETs with a resistive readout option fromthe five selected chips numbered from #1 to #5. The resistance valuesmeasured in ambient conditions without gating from chip #4 are shownin VG was applied using an on-chip Pt electrode to all theGFETs. The results are shown in SI2. Thedata have been normalized to the average Dirac peak voltage valueof 1.45 V (SD = 0.01 V) measured in DIW. A histogram of the normalizedDirac peak voltage values is shown in The performanceand stability of the GFETs was first evaluated by electrical measurementsin DIW. The GFETs were characterized twice in DIW to obtain resistanceof the devices as a function of the gate voltage. The gm) of the GFETsis defined as the derivative of the channel current, with respectto VG. The transconductances as a functionof the gate voltage are shown in The transconductance . The measured resistance and transconductancevalues indicate that the main response is the shift of the Dirac peakposition. The behavior can be explained with a doping change in thegraphene channel caused by the change in the gate capacitance.34 After the NaCl concentration series, an additional measurement wasdone in DIW to ensure that the devices return to the baseline establishedin the DIW characterization .Similarlyto the characterization in DIW, the GFETs were characterized to obtainresistance and transconductance values of the devices as a functionof the gate voltage for each NaCl concentration , the effect onthetotal capacitance is negligible, and only the quantum capacitanceand the double-layer capacitance at the graphene electrode need tobe considered. The experimental value of quantum capacitance is reportedto be between 2 and 11 μF cm–2, which dependson the gate potential and charged impurities.35 With a moderate charge impurity level (0.5 × 1012 cm–2) and graphene potential (±0.2 V) correspondingto the maximum and minimum transconductance point values, the quantumcapacitance is estimated to be around 4 μF cm–2. The double-layer capacitance at the graphene electrode can be calculatedby using the following equation:0 is the permittivityof free space and εr is the dielectric constant.By substituting 2/(V s) (SD = 170 cm2/(V s)) and900 cm2/(V s) (SD = 190 cm2/(V s)) for holesand electrons, respectively.The extracted transconductance values can be used to estimatethe mobility of the devices by using the following equation:36 Thesensitivity of each individual device has been estimated by usingthe measured Dirac voltage values and linear fitting, as shown in The resistance values at the Diracpeak and the corresponding Diracpeak voltage values for DIW and each NaCl concentration are shownin 48 to take the next steps toward multianalyte viral sensing.This technology enables the use of different sizes of GFET channels,with the possible number of GFETs ranging from thousands to millionsof devices depending on the ASIC circuit design and technology. Thisflexibility in the number of and size of the devices allows for awider set of applications where large amounts of biological informationneed to be evaluated, such as gene sequencing.38We demonstrate, for thefirst-time, wafer-scale GFET CMOS integrationfor biosensing with a device yield higher than 99% with a low device-to-devicevariability. We believe this approach will be crucial when commercializingGFET-based biosensors, enabling multianalyte sensing and the statisticalanalysis required for biologically quantitative on-chip bioassays.In the future, on-chip multianalyte sensing could enable efficientscreening of multiple viruses with sufficient statistics for reliabledetection from a single small analyte sample. The platform could becombined for example with the functionalization approach shown bySilvestri et al. on similar non-CMOS integration GFETsIn addition to its application in quantitative bioassays,CMOSmultiplexing can also be a valuable tool in the assay developmentphase. The most common issues in the development of FET-based biosensingare related to the reliability and repeatability of individual devicesand analysis. In many cases, the sensor chips are only used in a singlemeasurement; hence, individual faulty devices, voids in the functionalization,and small air bubbles can lead to misinterpretation of results. Thelarge-scale statistics provided by CMOS multiplexed sensor arrayson a single chip will improve the general reliability of the analysisand enable easy removal of defective devices from the data.39 Furthermore,multiplexing sensor arrays offer advantages in sensor defect exclusionand wider dynamic ranges by allowing for varying sensor design fordifferent sensitivities.37 The demonstratedtechnology also opens possibilities for the wafer-scale fabricationof CMOS-integrated GFET-based gas sensor arrays27 and infrared cameras26 thathave been demonstrated in the past. In this sense, the developmentof GFETs as a generic template that can be made specific by the choiceof functionalization allows for the adoption of this technology ina wide range of different applications.40 With CMOS integration, the readout can be tailored to fit specificneeds, which leads to a versatile sensor platform.The other benefits that the monolithic integration of GFETs offersare reduction in environmental noise and simplified connections, whencompared to hybrid off-chip sensors.


As rare diseases (RDs) receive increasing attention, obtaining accurate RD incidence estimates has become an essential concern in public health. Since RDs are difficult to diagnose, include diverse types, and have scarce cases, traditional epidemiological methods are costly in RD registries. With the development of the internet, users have become accustomed to searching for disease-related information through search engines before seeking medical treatment. Therefore, online search data provide a new source for estimating RD incidences.The aim of this study was to estimate the incidences of multiple RDs in distinct regions of China with online search data.Our research scale included 15 RDs in China from 2016 to 2019. The online search data were obtained from Sogou, one of the top 3 commercial search engines in China. By matching to multilevel keywords related to 15 RDs during the 4 years, we retrieved keyword-matched RD-related queries. The queries used before and after the keyword-matched queries formed the basis of the RD-related search sessions. A two-step method was developed to estimate RD incidences with users’ intents conveyed by the sessions. In the first step, a combination of long short-term memory and multilayer perceptron algorithms was used to predict whether the intents of search sessions were RD-concerned, news-concerned, or others. The second step utilized a linear regression (LR) model to estimate the incidences of multiple RDs in distinct regions based on the RD- and news-concerned session numbers. For evaluation, the estimated incidences were compared with RD incidences collected from China’s national multicenter clinical database of RDs. The root mean square error (RMSE) and relative error rate (RER) were used as the evaluation metrics.P=.01). Analysis of different RDs and regions showed that session input was more suitable for estimating the incidences of most diseases (14 of 15 RDs). Moreover, examples focusing on two RDs showed that news-concerned session intents reflected news of an outbreak and helped correct the overestimation of incidences. Experiments on RD types further indicated that type had no significant influence on the RD estimation task.The RD-related online data included 2,749,257 queries and 1,769,986 sessions from 1,380,186 users from 2016 to 2019. The best LR model with sessions as the input estimated the RD incidences with an RMSE of 0.017 (95% CI 0.016-0.017) and an RER of 0.365 (95% CI 0.341-0.388). The best LR model with queries as input had an RMSE of 0.023 (95% CI 0.017-0.029) and an RER of 0.511 (95% CI 0.377-0.645). Compared with queries, using session intents achieved an error decrease of 28.57% in terms of the RER (This work sheds light on a novel method for rapid estimation of RD incidences in the internet era, and demonstrates that search session intents were especially helpful for the estimation. The proposed two-step estimation method could be a valuable supplement to the traditional registry for understanding RDs, planning policies, and allocating medical resources. The utilization of search sessions in disease detection and estimation could be transferred to infoveillance of large-scale epidemics or chronic diseases. Rare diseases (RDs) refer to a group of diseases with very low prevalence . There Disease surveillance  is a common but crucial method for understanding RDs . TraditiTherefore, researchers have been seeking to detect or estimate the incidences of RDs with indirect information. For instance, various international and national platforms were constructed for collecting RD knowledge and incidences ,8,9.With the development of the internet, a tremendous amount of data was created online. Infoveillance  has beeNevertheless, to our knowledge, no study has yet explored the possibility of using infoveillance data in RD incidences estimation, and the existing research has not paid attention to the context information of disease-related data in the online environment, such as searching sessions in the search engines. However, comparing online search data to RD incidences and further estimating RD incidences is beneficial. Search engine data will locate the patients and families from the source, which is more convenient than a multiround clinical diagnosis and registry. In addition, search engines provide unlimited information, which can be used to break the barriers between RDs in different clinical departments. Hence, search engine data can make it possible to estimate multiple RDs in multiple locations simultaneously.Because few studies have focused on estimating RD incidences with online information, we reviewed prior research about employing online data in detecting or estimating epidemic and chronic diseases, and evaluated their differences with respect to RD incidences estimation.Since the spread of epidemic diseases will cause an increase of related online searches, several studies have focused on the detection and prediction of epidemic diseases using infoveillance methods . The newIn addition to epidemics, infoveillance has also been utilized in chronic diseases and other disorders. Ram et al  tried toThese previous works on epidemics and chronic diseases showed great successes of infoveillance, which inspired us to apply search data for RDs incidence estimation. Nevertheless, existing methods cannot be used directly for RDs because RDs remarkably differ from epidemics or common chronic diseases. In all previous studies based on search engine data, disease-related queries were extracted and the number (volume) of queries was used as the model input. However, RD-related search behaviors may be caused by cyberchondria , as search engines can potentially escalate medical concerns . Our expThe aim of this study was to estimate the incidences of multiple RDs in distinct regions using search engine data.queries were selected by matching the search logs with RD-specific keywords. The search sessions were constructed with the queries submitted in the period before and after the RD-related queries.As RD-related search behaviors are sparse and complex, it is not suitable to utilize RD-related query numbers directly for RD incidence estimation. Therefore, we designed a two-step machine learning method to estimate RD incidences with the volume of search sessions that concern RDs. The RD-related The two-step method is as follows. In the first step, the intents of search sessions are predicted. Users’ search intents indicate their purpose when querying RD-related questions on the search engine. The intents vary when the users mention RD-related queries in the session, such as seeking medical resources for patients, learning about news, searching for answers to medical assignments, and out of curiosity. By identifying sessions specifically concerned with RDs, we could filter out the noise from the RD-related search data effectively. In the second step, the incidences of multiple RDs are estimated in multiple regions with the volume of different session intents. RD incidences could be estimated more accurately with the filtered session numbers. Following previous works on disease detection with search engine data ,23,24,34The novel aspects of this study are two-fold. First, to our best knowledge, this is the first study to utilize search engine data in the estimation of multiple RD incidences, paving a new direction for improved understanding of RDs. This study therefore provides a helpful supplement to traditional RD registry systems. Second, the proposed approach introduces search sessions, especially session intents, into search engine–based infoveillance. The experimental results showed significant improvement when session intents were considered. The search session information could also be applied for the infoveillance of other diseases.In this study, a two-step method was designed to estimate the incidences of RDs from search engine data. The first step was to distill RD-related search sessions and predict their intents into three categories: RD-concerned, news-concerned, and others. The second step was to estimate multiple RD incidences based on the volume of RD-concerned sessions and news-concerned sessions. The method was applied to search data of 15 RDs in 4 regions in China during 16 seasons from 2016 to 2019. To evaluate the results, we compared the estimated incidences with RD incidences collected from China’s national multicenter clinical database of RDs ) and Disease 5 . MS represents a class of diseases that has received relatively less attention but gradually attracted public attention, which had related queries on around May 30 every year . ALS is a relatively well-known RD with thousands of RD-concerned and news-concerned sessions, which attracted massive attention when Stephen William Hawking, a world-famous physicist who had ALS, died on March 14, 2018.In RDs incidence estimation with session input, news-concerned intents were used as input for the LR models. We aimed to analyze the usefulness of the weights considering news about different diseases  and relative error rate (RER) were utilized for performance evaluation to obtain both the absolute error and relative error of the models:predy is the predicted output of LR models.where All experiments were conducted in the Python 3.6.13 environment and all methods were implemented with the Pytorch 1.7.1 library. Models were trained with the Adam optimizer until convergence on the validation set with a maximum of 1000 epochs.In general, the RDs incidence data set included more than 80,000 incidences from 2016 to 2019 in China . The RD-related search data set included 2,749,257 RD-related queries and 1,769,986 sessions from 1,380,186 users. It is worth noting that repeated search was not a serious problem in our data set. On average, each user had 1.282 sessions, most users  had only one session, and 97.75%  of users contributed less than four sessions. This is mainly due to two reasons. First, the sessions grouped RD-related search queries that were submitted by a user over a short period of time; therefore, repeated sessions were less common for RD patients in our data set. Second, we distilled RD-related sessions by specific keywords for RDs , and the provided results might be sufficiently clear that there was no need to repeat the search. Therefore, we adopted the intent prediction and incidence estimation tasks at the session level rather than the user level.Furthermore, we considered four regions in our data set, which divided 31 provinces in China’s mainland into four parts: East, West, Central, and Northeast. The populations of the four regions were 535.6 million, 378.1 million, 369.9 million, and 108.5 million, with gross domestic products of 7109 billion dollar, 2752 billion dollar, 2899 billion dollar, and 797 billion dollar, respectively (average of 4 years). In the RDs incidence data set, the sum of the incidences of 15 RDs was the highest in the West, followed by the East, Central, and Northeast regions. The incidence of different RDs varied among the four regions. For instance, MS and hemophilia had the largest incidences in the West, whereas ALS was the most frequently registered disease in the East. In the RD-related search data set, the average session and query numbers of the 4 years were 225,906.5 and 1,023,152.0 for the East; 91,357.5 and 413,361.3 for the West; 94,151.8 and 429,708.5 for the Central region; and 31,080.8 and 141,278.0 for the Northeast, respectively.Generally, the East had the largest population, the most developed economy, and, accordingly, the highest number of queries and sessions. Overall, the session volume was proportional to the population. However, regional reported RD incidences and population did not always match, since the incidence of an RD in a given region might relate to whether it is a family genetic disease in the region, the diagnosis technique of the disease in that region, and other factors. Therefore, we considered the effect of region variables on the RD incidence estimation specifically.The first-step session intent prediction was evaluated with the human-annotated test set of 240 sessions. In the three-category classification task, the model had a macro-F1 value of 0.452 and an accuracy of 0.682 on the test set. The F1 scores for RD-concerned sessions, news-concerned sessions, and other sessions were 0.397, 0.353, and 0.606, respectively. Some representative sessions with different intents are shown in S, resulting in 426,031 RD-concerned sessions, 115,016 news-concerned sessions, and 1,228,939 other sessions. The RD-concerned and news-concerned sessions were grouped by their RDs, regions, and periods to form the session inputs sdx and snx for comparing and estimating RDs incidence.Finally, the model was applied to predict the intents of all 1,769,986 sessions in Session Set t-test was performed on the results with different input types on the same model, and the P values are also reported in The incidence estimation results of different input types and LR models are shown in LR Spec. D. L. exhibited the best performance, with RER=0.365 on session input and RER=0.511 on query input. However, the 95% CI was large. The instability was mainly due to the relatively large number of parameters in LR Spec. D. L. Further detailed comparison between session input and query input are shown in Session input had significantly better performance than query input on all models and metrics, which indicated the usefulness of considering search session intents in the RDs incidence estimation task. Comparing different models, id and regions jl in the LR Spec. D. L. model snθ(jl)) are shown in The weights considering news about different diseases To explore how news-concerned sessions affect RDs incidence estimation dynamically, we display two cases of RDs for Disease 1 (MS) and Disease 5 (ALS) in The RD incidence estimation experiment on 15 RDs in 4 regions of China showed that RDs could be estimated with search engine logs, especially search session data. The RER of RDs incidence estimation was 0.365 for the session input and 0.511 for the query input. Considering the sparsity of RD cases, the RDs incidence estimation performance is encouraging.The first step predicted session intents with a deep neural model. The prediction results indicated the necessity to distinguish the user intents in searching sessions. Among 1,769,986 RD-related sessions, only 426,031 (24.07%) were RD-concerned and 1,228,939 (69.43%) belonged to other intents. By identifying sessions concerned with RDs, irrelevant queries were effectively filtered from the data.P=.01). This illustrates the significant contribution of considering search sessions with more context for RD incidence detection. Moreover, as shown in The second step, multiple RDs incidence estimation with LR, demonstrated that considering the volume of sessions rather than RD-related queries was significantly more helpful for disease estimation in most RDs and regions, as shown in To our knowledge, this study is the first to apply infoveillance in RDs incidence estimation, which provides a novel method to understand RDs. Compared with prior research on utilizing search engine data to estimate other diseases, a novel aspect of this study is that we considered the session context about disease-related queries and then utilized session intents to replace query volume for disease incidence estimation. Session inputs showed significant improvement on the RDs incidence estimation task. Although the sparsity of RD-related queries inspired the use of session information, the two-step method can be effectively transferred to other search engine–based disease detection and estimation tasks, as data noise pervasively exists online.This study has several limitations. First, the current data from the national multicenter clinical database of RDs were collected by retrospective reports. Due to the difficulty of RD diagnosis and the limited support of International Classification of Diseases 10th Revision codes for RDs, there might be delayed or unreported cases in the database. Therefore, the overestimations of incidence might reflect unreported cases, which was neglected in our analysis and discussions. In the future, it would be helpful to revisit patients in overestimated RDs and regions with privacy protection.Second, 15 RDs with stable long-term data in the registry database were utilized for our experiments. These experiments could be applied to other RDs, whereas some RDs might not be estimated with our proposed methods, such as those with unclear symptoms, too low incidence, and low public awareness. Extending this method to more RDs and finding the boundary is promising future work.Third, the level 1 keywords used for matching RD-related queries were provided by medical experts, which was time-consuming and might reflect knowledge bias. In the future, we will test automatic keyword discovery methods for RD-related keyword discovery.Finally, a simple combination of LSTM and MLP was adopted for intent prediction in this study as the first attempt to integrate session intents in RDs incidence estimation. Since the numbers of RD-concerned and news-concerned sessions were much smaller than the numbers of sessions about other intents, the F1 scores of intent prediction about RD-concerned and news-concerned sessions were limited . Although challenging, accurate intent prediction is essential for capturing RD-concerned sessions precisely. Therefore, we aim to design neural predictors with more sophisticated network structures and more features about the sessions and queries to improve the session intent prediction accuracy, especially for RD-concerned and news-concerned sessions.In this study, an experiment on multiple RDs in multiple regions showed that it is possible to estimate RDs incidence with online search engine data. The two-step estimation method illustrates promising performance improvement when session intents are considered in the RDs incidence estimation task. The use of session information can be transferred to infoveillance on other diseases.This study did not aim to replace the clinical RD registry systems with search engine–based estimation. The two-step RDs incidence estimation model was designed as a supplement and prewarning method. For instance, if the model overestimates an RD in a region, this can remind experts of possible missing records from clinical registries or lack of medical support in the region. This method could help provide information for allocating medical resources and RD-related policy-making in the future. Moreover, with privacy protection, the method could offer advice to RD-concerned users of appropriate medical aids such as hospitals or institutes specialized in certain RDs. In conclusion, this study provides a promising method for understanding and locating RDs.


Elevated humidity levels in medical, food, and pharmaceuticalproductsmay reduce the products’ shelf life, trigger bacterial growth,and even lead to complete spoilage. In this study, we report a humidityindicator that mechanically bends and rolls itself irreversibly uponexposure to high humidity conditions. The indicator is made of twofood-grade polymer films with distinct ratios of a milk protein, casein,and a plasticizer, glycerol, that are physically attached to eachother. Based on the thermogravimetric analysis and microstructuralcharacterization, we hypothesize that the bending mechanism is a resultof hygroscopic swelling and consequent counter diffusion of waterand glycerol. Guided by this mechanism, we demonstrate that the rollingbehavior, including response time and final curvature, can be tunedby the geometric dimensions of the indicator. As the proposed indicatoris made of food-grade ingredients, it can be placed directly in contactwith perishable products to report exposure to undesirable humidityinside the package, without the risk of contaminating the productor causing oral toxicity in case of accidental digestion, featuresthat commercial inedible electronic and chemo-chromatic sensors cannotprovide presently. Therefore, intelligent tags thatcan dynamically report on the storage environment are considered efficientto address these issues in food safety.The best-by date is mandatory and printedon packages of perishableproducts. Yet, it does not provide dynamic information on productsafety prior to consumption. For instance, several products past thesedates are usually safe to eat depending on consumer handling practices.3 Macroscopic size intelligent tags such as temperature tags9 and gas tags14 have been widely studied and are constantly reported in recent years.In addition to macroscopic size intelligent tags, microparticles15 that can report changes in pH16 or carry encoded information17 and respond to external stimuli18 havealso been reported.Intelligent tags aredesigned to monitor the storage environmentof perishable products in a cost-efficient manner.19 Medicines formulated as tablets and powderscan also disintegrate or cake by absorbing excess moisture.20 Furthermore, medicines can deteriorate or exhibitundesirable chemical reactions, leading to reduced drug potency inthe presence of excess moisture.Detection of humidity is often overlookedin the research fieldof intelligent tags despite its relevance in the storage of perishableproducts. Condensation of moisture on perishables can lead to hasteneddeterioration, bacterial growth, and reduction in their estimatedshelf life. Some common undesirable effects of excess humidity infood include caking, stickiness, lumping, crumbling, and molding.22 while other examples by virtue of geometric deformation have alsobeen proposed.24Research on humidity tags remainssparse to date despite the importanceof humidity control as discussed above. The majority of reported humiditytags are chemo-chromatic; hence, they respond to changes in humidityby changing their color21 proposed a chemo-chromatictag using cobalt chloride to estimate the water activity of driedproducts. The easy hydration and dehydration reaction between CoCl2 and CoCl2·6H2O induces a colorchange, thus indicating a humidity shift. However, direct contactwith consumable products was not advised due to the fact that chronicingestion of cobalt compounds has been widely reported to cause serioushealth problems.25 Therefore, users arerecommended to place this tag on the outside of the packaging, whichintroduces the risk of reading the ambient humidity instead of thetrue condition experienced by the target products. To overcome this,Bridgeman et al.26 proposed a hygroscopiccomposite tag of less toxic chemicals, i.e., sodium borohydride andDenimBlu30 dye. In this tag, DenimBlu30 dye functions as a redox indicatorand evolves from yellow to blue after absorbing water, hence reportingthe humidity increase. However, direct contact is still not recommendeddue to toxicity concerns.Thompson et al.24 presented a tailored shape memorypolymer of which theglass transition temperature can be driven lower to the operationaltemperature by vapor pressure. The absorbed water drives this transitionby increasing the free volume and the mobility of polymer chains.Therefore, the initial rigid polymer develops into a flexible elastomericstate over time under vapor. However, the reversibility of this polymeronly endows an instant humidity value and not the moisture exposurehistory.Abandoning chemical reactions, Snyderet al.31 A noteworthy study by Esteves et al.28 focused on gelatin/IL-based formulations, functioning aselectrical and optical sensors; the formulations can be tailored torespond to humidity and detect volatile organic compounds under dryand humid conditions.Ionic liquids (ILs) have gained attention in recentyears as theycan be immobilized in polymer or metal–organic frameworks,enabling the creation of flexible composites that exhibit high sensitivity,fast response, and wearability as humidity sensors.28 unfortunately, exhibit reversible changes. Sincea perishable product exposed to high humidity conditions still showsdeterioration or bacterial growth even if the humidity is broughtback to the recommended level, it is impractical to interpret spoilageaccurately using reversible indicators. Therefore, a food-grade, edible,nontoxic, and irreversible humidity indicator with a tunable responsetime that matches the spoilage course is desirable.Despite the recent progress in intelligenttags, a biocompatiblehumidity indicator that can be placed in direct contact with foodand medicine without contamination risk has not yet been reportedin the literature to the best of our knowledge. The biocompatibilityof the ionic liquids is not discussed in the above studies; it isquite promising to design fully biocompatible IL humidity sensors.Nevertheless, reading IL-based sensors requires the use of electricaldevices due to the chemisorption of water molecules on the ILs whichenhances its ionic conductivity and capacitance. However, this increasesthe cost of producing humidity tags and has environmental implicationsas the materials used in fabricating these electrical devices aretypically plastics and metals. In addition, the indicators discussedabove,32 Based onthe thermogravimetric analysis, we hypothesize that the bending mechanismis a result of hygroscopic swelling and consequent counter diffusionof glycerol and water. Leveraging our understanding of the bendingmechanism, we demonstrated that EHI’s lag time, response time,and bending curvature can be tuned to expand its application scope.Overall, the developed indicator shows the potential to irreversiblydetect undesired humidity exposure. The humidity indicator’sversatility is attributed to its ability to tune the lag time andresponse time by changing the layer thickness, making it suitablefor applications where humidity changes occur gradually or infrequentlyover an extended period. The demonstrated response time is slow forapplications requiring real-time or near-real time response. The indicator’sdeformation in response to humidity makes it also suitable for useas a humidity-activated actuator, particularly in situations wherelow humidity levels need to be maintained. Moreover, thanks to itsfood-grade hence edible ingredients, the EHI has two distinct advantagescompared to commercial nonedible electronic and chemo-chromatic sensors.First, the EHI can be placed in direct contact with perishable products;hence, it can report on the humidity level inside the packaging withoutany risk of contaminating the product. Second, in case the EHI isaccidentally ingested, for instance, by infants or children, it doesnot pose any risk of oral toxicity.In thisstudy, we report an edible humidity indicator (EHI) madeof food-grade ingredients that responds to undesired humidity exposureby irreversibly deforming and rolling on itself. The indicator ismade of two food-grade polymer films with distinct protein, caseinate,to plasticizer, glycerol, ratios. Caseinate, originating from bovinemilk, has been repeatably reported as a promising biodegradable packagingalternative in recent years.Figures S1 and S2). Thefilms are conditioned by storing them in desired humidity over 24h. The conditioned films are then attached to each other leveragingthe intrinsic sticky nature of films to fabricate bilayer EHIs.The fabrication processconsists of several simple steps 1a and is33 not only due to its attributes such as biocompatibility,biodegradability, and wide availability but also because of its randomcoil structure with the ability to form electrostatic, hydrophobic,and hydrogen bonds. Meanwhile, the casein film on its own is brittleand hence not easy to handle. To improve this, varying amounts offood-grade plasticizer glycerol are added to each layer. To ensurethat EHIs are made of entirely nontoxic ingredients, no glue is adoptedin the stick-together step. Perhaps due to caseinate’sself-healing ability, which rises from its random coil nature andability to form weak intermolecular interactions within polymericnetworks, i.e., electrostatic, hydrophobic, and hydrogen bonds asmentioned above,34 the single caseinatefilms can attach to each other and form a stable bilayer film. A scanningelectron microscope image  showsthat the gap between the two layers is negligible. In general, thiscomposite bilayer contains only edible ingredients, which avoid anysafety concern about direct contact with edible products.To avoid potential oral toxicity issues, EHIs aremade of onlyfood-grade ingredients, namely, caseinate, glycerol, and water. Caseinateis proposed as biodegradable packaging materialR) as illustrated. Prior to humidity exposure, the EHI keepsits flat shape for a relatively long time . We observed two distinctregimes, a Lag stage where the EHI absorbs waterfrom the environment with no significant bending followed by a Response stage where the EHI deforms and gradually rollson itself. After that, the EHI does not revert back to its initialshape when the humidity level is brought down. We refer to this experimentalobservation as the irreversible bending of the EHI, a feature thatis essential for detecting and reporting humidity exposure.The EHI response to accidental humidityexposure is illustrated as bending curvature development 1b. MoreoLag stage as thestage from t0 to troll, where t0 is the moment theEHI is exposed to a high humidity level while troll is the moment the EHI starts to roll  film remains insidethe rolled structure. We refer to this bending direction as rollingonto the less hygroscopic film.The reported bendingand consequent rolling behavior are observedin other systems as a result of a mismatch in layer properties.Figures S1 and S2). If the bending mechanism of EHIs was identicalto the aforementioned hydrogel bilayer structures, bending towardLGF would be expected. As we observe a different bending directionfrom the hydrogels in the literature, the bending mechanism of theEHI can not be solely explained by the distinct water adsorption capacityof two layers attached together, namely, HGF and LGF. A differentrolling mechanism is needed to explain this observation.Surprisingly, such a bendingdirection is not observed in our study.EHI sways back and forth slowly at the early stages of the exposure;it eventually bends toward and rolls onto HGF  regardless of RH value, EHI composition, or geometry. In otherwords, HGF (blue) remains inside the rolled structure and LGF (pink)remains outside 1b. This 42 Improper handlingand storage of these test strips, especially due to humidity exposure,have been reported to give rise to incorrect diagnosis.43 We test whether the EHI can be used to detecturine test strips with compromised functionality due to humidity exposure.For this test, we exposed urine strips with EHIsattached to labhumidity fluctuating between 48–64% throughout the experimentand monitored the bending of the EHI strips 2a, top. ned flat 2c. To suAs different products degrade in distinct time scales,the broaderapplicability of EHI hinges on the tunability of the lag time andresponse time. The bending/rolling mechanism needs to be understoodnot just out of scientific interests but also from an engineeringinterest to benefit EHI design.Figure S3). Hence, we rationalize that a different physicalmechanism should be in play.As HGF (blue) has higher water absorptionability compared to LGF (pink), the EHI should have bent toward LGFif the bending mechanism was based solely on compressive stressesinduced by the layer swelling more. Nevertheless, the observed bendingdirection contradicts this initial expectation. More interestingly,scanning electron microscopy images revealed that the LGF of the EHIbilayer elongated more than the HGF after 2 weeks of storage outsidethe desiccator at ambient humidity conditions (see Table S1).To shed light on the mechanismbehind bending and consequent rolling of the EHI on itself, we conductedthermogravimetric analysis (TGA) on various films: a bilayer EHI(1),a single HGF(2), and a single LGF(3) 3a. PriorTGA profiles show three distinct regimes. In stageI (100 to 200°C), water evaporation induced a similar mass loss trend in allthe samples. Stage III, from around 300 °C, is the decompositionof sodium caseinate, leaving 30% residue at 700 °C. In stageII, where the part of our interest lies, we found that films 1a and1b show an identical trend after the humidity exposure, indicatingthat the compositions of these two films are identical. Therefore,it is rationalized to hypothesize that glycerol diffused from HGFto LGF after humidity exposure, considering that glycerol is the onlychemical that decomposes within stage II.Basedon the TGA observations, we propose a rolling mechanism that involvesthe adsorption of condensed water from the environment and subsequentlythe counter diffusion of glycerol and water across the bilayer. Thehypothesized mechanism 4 is illuStorage stage (at Low RH), LGF and HGF absorb a limited amount of water. Glycerolis constrainedin each layer at this stage since the caseinates are densely packedand the glycerol diffusion is considered extremely slow. Therefore,the EHI is capable of maintaining its flat shape for a relativelylong time under 10% RH. In fact, as already mentioned, the EHI canmaintain its shape for at least 15 days when subjected to low relativehumidity (10% RH) . Thisimplies that the EHI is stable and reliable within our observationperiod and its shape will not be affected until it is exposed to arelatively high level of humidity. Shelf stability should be furtherinvestigated and adjusted based on the shelf life of the product,and EHIs will be implemented.During the t0 when humidity is introduced,both films start to absorb moisture and exhibit hygroscopic swelling.In this stage, which we term as Lag stage, we speculatethat the films absorb more water and exhibit a volumetric change comparedto the initial volume. As previously mentioned, the high concentrationof glycerol in the HGF tends to bond more water, and there is a largervolume change in the HGF (ΔVHGF)compared to that of the LGF (ΔVLGF). On the macroscale, the bilayer first bends toward the LGF withthe HGF as the outer layer and the LGF as the inner layer (Movie S1). However, since the network of thecaseinate film is expanded by the absorbed moisture, the glycerolmolecules from this moment can move from the HGF to LGF due to theconcentration gradient. We hypothesize that moisture uptake and glyceroldiffusion along with water diffusion lead to a competition in volumechange between the LGF and the HGF (ΔVLGF ≈ ΔVHGF). On themacroscale, we hypothesize the lag time observed is due to the timerequired for the condensed water to penetrate the glassy network.At Response stage, the bulk volume of eachlayer further expands; hence, the caseinate proteins are loosely packed,and the size of the pores within the caseinate network further grows.As a result, glycerol in this stage diffuses significantly from theHGF to the LGF. Furthermore, water absorbed by the HGF migrates tothe LGF accompanied by glycerol diffusion. These two factors henceforthhelp the LGF eventually win the competition in the volumetric increase(ΔVLGF ≫ ΔVHGF) and lead the bilayer to roll up with the LGF as theouter layer along with HGF as the inner44 layer on the macroscale.In the Moreover, interface effects suchas the formation of a thick skinlayer due to enhanced evaporation at corners and at the interfacemay alter local microstructure in EHI. The altered crust skin mayslow down the diffusion of water consequently time scale of bending.Response stage, the counter diffusion ofglycerol and water reaches equilibrium, and the two layers becomeidentical in composition as supported by TGA results. The rollingof the EHI on itself stops since there is no concentration gradientthat can drive the dilatation. Similarly, since the bending and rollingare proposed to be driven by glycerol diffusion, the process cannotexhibit reversibility because there is no glycerol concentration differencein the equilibrium bilayer to provide the necessary driving force.After It is crucial to engineering the EHI response,namely, the lag time, response time, and final curvature of rolling,to match the characteristic time scale of decomposition/degradationof the labeled products. In this section, we will demonstrate thatthe EHI response can be tuned by simply adjusting the layer thicknessand aspect ratio.Normalized Area, i.e., Projected area/Initialarea, instead of the curvature, is given to represent the rollingbehavior. The projected area is highlighted by the gray dashed line. Both C1 and C2 showa normalized area increase during the lag stage. This increase inthe area could be attributed to the films swelling when they absorbmoisture, as we already discussed  is 25 min. It exhibits a linear rolling behavior in line withearlier presented results that is completed within 30 min. The rolledstructure is maintained after 150 min. For the thicker combination, the lag time is 60 min;meanwhile, it takes around 90 min to complete rolling. By increasingaround 30% of the HGF thickness, both the lag time and response timeare doubled. We can attribute this longer operation time to two reasons.First, a thicker film spends more time reaching the swollen equilibriumstate. Second, the thicker combination, due to its reduced flexibility,iscussed 4.However, the device is consideredversatile due to the abilityto easily tune its lag time and response time through the layer thickness,making it suitable for various applications where humidity changesslowly or infrequently over a longer period of time, such as monitoringhumidity trends in a climate-controlled environment such as storageroom. The response time varies from 90 to 150 min in the showcasedcombinations. While this showcased response time may not be idealfor applications requiring a real-time response, it is suitable forwarning users of slowly evolving humidity-triggered processes suchas bacterial or mold growth which typically requires multiple hoursto a few days based on environmental conditions and biological species.47 However, contradicting results exist in the literatureregarding the quantitative relationship between the aspect ratio andfinal bending curvature. Stoychev et al.,35 Wang et al.,48 and Abdolahi et al.49 found the bending curvature of the free-standinghydrogel bilayer is independent of the aspect ratio, which agreeswith Timoshenko’s theory.50 However,Kim et al.51 observed a strong dependencebetween aspect ratio and bilayer bending. Moreover, multiple-rollinghas been observed exclusively under the condition of a large aspectratio by Alben et al.45 We already foundthat at 50% RH all our EHIs roll to a radius of around 2.5 mm regardlessof aspect ratio , wherethe rolling degree can be considered uniform within experimental limits.In this study, the relationship between the aspect ratio and the attainedcurvature of the rolled EHI is further investigated for EHIs conditionedat 50% RH and exposed to 80% RH. The results are summarized in The aspect ratio hasalready been consistently reported to influence the bending conformation for other bilayer systems.atio see S6, where51 In this study, we only focus on the final curvature the rolled EHIattains because bending conformation is less critical for humidityinterpretation according to our observations. To eliminate certainconformations, the short edge of the EHI is attached to a solid object.When the short edge is fixed by a clamp, the EHI looks like a cantileverbeam, and the bending occurs along the long edge.As the aspect ratio influences the bending conformation,to bemore specific, bilayers will bend into a shape that minimizes resistanceto bending in an energetically preferred way.R) as illustrated in the inset. We further normalizethe radius with the thickness of the HGF of the EHI to ensure thatwe have a dimensionless graph (R/HGF thickness and theaspect ratio l/w. The coefficientof determination (R2 ≈ 0.97 forC1 and R2 ≈ 0.99 for C2) indicatesa reasonably strong, positive linear correlation between nondimensionalized R and aspect ratio. Since the length and the HGF layer thicknessare fixed, while the width is varied in this set of tests, we canalso put it in a more simple way; i.e., the final rolling radius islarger for smaller widths. It can be seen that C2  overall shows a smaller R, which is within expectation considering that the larger total amountof glycerol transported from the HGF to the LGF can induce more pronouncedswelling in the LGF in C2 compared to that in C1. Moreover, when wecompare the two slopes of C1 and C2, it is evident from the observationthat the aspect ratio exerts a more substantial effect on the finalradius of the rolled bilayer EHI in thinner C1 compared to C2.EHIs of twodistinct thicknesses (both C1 and C2) were preparedin various aspect ratios, conditioned under 50% RH, and exposed to80% RH. The final shape of the rolled EHI is characterized by radius and low glycerol film (LGF), respectively. Anillustration of the film production procedure is in To prepare the film-forming mixtures, ultrapure water  and glycerol  were mixed and magnetically stirred at 600 rpm and60 °C. To this solution, sodium caseinate was gradually added over time intervals of 2–3 min to reachthe desired caseinate/glycerol/water ratio, which is 12:14:100 forthe HGF (dyed blue) and 12:3:100 for the LGF (dyed pink), respectively.The stirring rate was subsequently increased to 850 rpm for 60 min.Afterward, food dyes (Dr. Oetker) were added to distinguish the HGFand the LGF. The final mixtures were centrifuged at 7400 rpm for 12min to remove any bubbles formed during the mixing process.Film-forming mixtures were cast into plastic Petri dishes  and baked in the oven at 40 °C for48 h. After gelation, the films were peeled off and stored in a desiccator(RH 10%). Films are conditioned for 24 h in an environmental chamber under specific RH before the test.Moreover,the thickness of the film was controlled by the amountof the mixture used and measured by a film thickness gauge (Heidenhain).For instance, a 12 mL mixture can obtain an HGF of 0.310 mm thickand an LGF of 0.155 mm thick.53The utility of EHIs is demonstratedwith a urinalysis reagent strip . An EHIstrip (0.155 mm-HGF and 0.155 mm-LGF) of 5 mm by 20 mm was cut andattached to the blank end of the urinalysis strip along the shortside with Norland Optical adhesive that cures using ultraviolet light.All EHIs were conditioned under 10% before usage and then exposedto atmospheric humidity that simulated a consumer leaving the actualstorage bottle open by accident. The test was conducted at a lab temperaturethat fluctuated around 23 °C. After a 48 h atmospheric humidityexposure, we kept the test urine strip back in the desiccator for4 h to examine any reversibility. Following this, a urinalysis testwas conducted on the model strip and compared with an original stripunexposed to atmospheric humidity. The preparation of artificial urineis the same as described in the literature.R) of the rolled EHIswas analyzed using ImageJ. The rolling curvature was calculated as1/R.For visualization, the EHI response was recorded throughoutthe process in the form of a time-lapse with an action camera  to visualize the process. To measure thecurvature evolution, the recorded time-lapse film was extracted percertain frames. The radius  conducted onan SDTQ600 instrument. In each analysis, approximately 8.0 ±0.5 mg of sample was placed in an aluminum oxide crucible and heatedover temperatures ranging from 30 to 750 °C at a rate of 20 °C/min.A continuous nitrogen flow at a rate of 30 mL/min was supplied tothe TGA to maintain an inert atmosphere.Normalized Area to represent the rolling in this test, which was calculated as Projectedarea/Initial area.For the thickness test,two combinations were tested. Combination 1 (C1) was made of a 0.155mm-thick HGF and a 0.155 mm-thick LGF while combination 2 (C2) wasa 0.206 mm-thick HGF and a 0.155 mm-thick LGF. After conditioningseparately under 50% RH for 24 h, the single-layer films were stucktogether and immediately cut into rectangular strips. One end of thesamples was fixed to a glass slide with water-proof tape. The ready-to-testEHIs are 15 mm in length. After that, the samples were transferredinto the environmental chamber at 80% RH and a constant temperatureof 25 °C. The EHI response was recorded in time-lapse from thetop view to visualize the rolling process. Finally, pictures wereextracted, and the projected area of the rolled circle was analyzedusing ImageJ image analysis software. We use R) of the rolls was analyzed using ImageJ as well.For the aspect ratio test, the conditioningand test procedure were the same as the thickness test as describedabove besides that samples were cut into rectangular strips of variouslengths and widths. We only focus on the final geometries. Therefore,images were only captured after the rolling was completed. The radius(