AI‐Driven Wearable Mask‐Inspired Self‐Healing Sensor Array for Detection and Identification of Volatile Organic Compounds

Volatile organic compounds (VOCs) sensor arrays have garnered considerable attention due to their potential to provide real‐time information for monitoring pollution levels and personal health associated concerning VOCs in the ambient environment. Here, an AI‐driven wearable mask‐inspired self‐healing sensor array (MISSA), created using a simplified single‐step stacking technique for detecting and identifying VOCs is presented. This wearable MISSA comprises three vertically placed breathable self‐healing gas sensors (BSGS) with linear response behavior, consistent repeatability, and reliable self‐healing abilities. For wearable and portable monitoring, the MISSA is combined with a flexible printed circuit board (FPCB) to produce a mobile‐compatible wireless system. Due to the distinct layers of MISSA, it creates exclusive code bars for four distinct VOCs over three concentration levels. This grants precise gas identification and concentration prognoses with excellent accuracy of 99.77% and 98.3%, respectively. The combination of MISSA with artificial intelligence (AI) suggests its potential as a successful wearable device for long‐term daily VOC monitoring and assessment for personal health monitoring scenarios.


Introduction
[3][4] Their high concentrations pose severe environmental repercussions, with prolonged exposure having detrimental effects on various human organs. [5,6]Conversely, low concentrations of VOCs from human DOI: 10.1002/adfm.202309732[9] This has made VOC monitoring increasingly popular as a means of gauging environmental pollution levels, as well as providing vital information about individuals' physiological and pathophysiological states.In order to address the growing demand for effective means of detecting VOCs in an individual's surroundings, the production of wearable VOC sensors has skyrocketed, [10][11][12] largely driven by the advantages of some conventional materials, such as electrochemical materials and metal-oxide semiconductors with high selectivity and low cost. [13,14]An example of this is the isoprene gas sensor developed by Zhang et al., [15] with a limit of detection (LOD) below five ppb.Despite this, for wearable applications that necessitate continuous monitoring-such as smart wound dressings and electronic skin-these VOC sensors may be subjected to external mechanical forces that can ultimately degrade or disable their performance. [16,17]As a result, there is an immediate need for sensors that can guarantee stability during long-term, uninterrupted monitoring.
Fortunately, the advancements made in the realm of selfhealing materials have offered a promising solution for this problem.[20][21][22][23][24] Nevertheless, a key challenge of these sensors is that they often show strong adsorptions to multiple types of VOCs, thereby limiting their selectivity.To combat this, researchers have focused on both the hardware and software domains.[27] For example, the self-healing VOC sensor array demonstrated by Huynh et al., [28] which displayed high levels of discrimination, sensitivity, and selectivity despite being subjected to mechanical damage.However, current fabrication methods for producing these arrays typically involve the use of different materials or chemically modified host material, leading to an expensive and complex design.Thus, there is a great demand for a simpler and easier fabrication process that can produce wearable VOC sensing arrays with self-healing capabilities.
31][32][33][34] Despite this, precise identification and concentration prediction of target gases in mixed environments still remain an elusive goal, requiring more precise feature selection and comprehensive visualization.Amidst the current epidemic of disease, the masks have become indispensable because of their ability to filter out gases.Drawing inspiration from this, we propose a novel self-healing sensor array (MISSA) in a layered form, with an uncomplicated and resourceful single-step technique for recognizing VOCs and foreseeing concentration (Figure 1).The selfhealing element used for VOCs detection is called HDIM, comprised of a mix of prepolymer and MXene, which can be disentangled in chloroform to form a conductive ink-like solution.Exploiting this characteristic, a breathable self-healing gas sensor (BSGS) based on HDIM was designed through screen-printing on polyurethane (PU) electrospun film, displaying a low LOD, straight behavior, consistent accuracy, and beneficial self-healing competencies.Just like the mask design, three BSGSs were set up vertically to create the MISSA, which made it possible for the formation of singular barcodes for individual VOCs through the selective adhesion of distinct layers.To offer portable and wearable detection, a smartphone-based joined wireless system was produced, with a flexible printed circuit board (FPCB) to sense array impedance and transfer information to a mobile phone via Bluetooth.With the aid of personalized assessment software, the present-time reactions of the MISSA to VOCs could be conve-niently viewed on the phone screen.Besides, the MISSA highlighted superior gas recognition and concentration prognosis capabilities for four varied VOCs via the application of prudent feature choice and principal component analysis (PCA)-facilitated machine learning (ML), conveniently illustrating the outcomes in a complete way.The effective enforcement of sophisticated MISSA underscores its feasibility as a movable tool to accurately identify VOCs for enduring regular health monitoring.

Preparation of the HDIM-Based BSGS
Figure 2a presents the synthetic structure of the HDIM, where MXene is uniformly dispersed in the self-healing prepolymer as a conductive material.The synthesis procedure of the prepolymer is depicted in Figure S1 (Supporting Information), with hydroxylterminated polybutadiene (HTPB), 1,10-decanediol (DE), and isophorone diisocyanate (IPDI) as the primary constituents, and dibutyltin dilaurate (DBTDL) as the catalyst.Notably, within the HDIM, HTPB acts as the soft component for controlling flexibility, while DE and IPDI collectively serve as the hard segments.They contribute to hydrogen bonding by forming urea/urethane linkages, impacting the mechanical characteristics and selfhealing properties of the material. [35]The absence of significant peaks of the N═C═O stretching bond at 2264 cm −1 in the FTIR spectrum shown in Figure 2b indicates the complete conversion of diisocyanate monomers into urethane bonds, an essential requirement for the formation of hydrogen bonds. [15]After preparing the transparent prepolymer (Figure S2, Supporting Information), a solution of single-layered Ti 3 C 2 T x MXene with a concentration of 5 mg mL −1 is added proportionally to the prepolymer solution to create a thick mixture, ultimately leading to the production of HDIM (Figure S3, Supporting Information).The X-ray photoelectron spectroscopy (XPS) patterns and elemental mapping in Figure 2c,d revealed the incorporation of Ti and F elements into the HDIM through MXene. [36,37]All of these results demonstrated that MXene was dispersed uniformly within the HDIM.Furthermore, the water contact angle (CA) of ≈104°evidenced the HDIM's good hydrophilicity, visible in Figure 2e.The thermogravimetric analysis (TGA) of HDIM indicated its outstanding thermal stability, even at elevated temperatures of up to 240°C (Figure S4, Supporting Information).
It is remarkable that HDIM can be re-dissolved in chloroform to produce an ink-like solution upon request (Figure 2f; Figure S5, Supporting Information).Moreover, this ink-like solution is suitable for the development of printed sensors and displays a good level of electrical conductivity when applied to commonly used substrates such as PU, PTFE, and paper (Figure 2g; Figure S6, Supporting Information). [38]To produce a BSGS with HDIM, the ink-like property was utilized in a screen-printing process (Figure 2h).[41] The PU electrospun film exhibited an ideal hydrophobic quality (Figure S9, Supporting Information), meaning sweat or wound fluids would not affect the performance of the HDIM.Printing procedures used the HDIM ink to create an array with a width of ≈5 mm (Figure 2i; Figure S10, Supporting Information) which enabled cost savings and improved efficiency.Additionally, the BSGS had great air permeability which ensured skin comfort upon direct contact with the sensors (Figure S11, Supporting Information).

VOCs Sensing Performance of BSGS
The BSGS's electrical property was measured through a currentvoltage (I-V) test at room temperature, showing a positive ohmic behavior (Figure S12, Supporting Information).VOCs exert a significant influence on disease diagnosis, playing a crucial role in monitoring various metabolic processes.For instance, the monitoring of alcohol metabolism, aldehyde production, and other gas emissions originating from bacterial activities at wound sites holds the potential to serve as a supplementary diagnostic criterion for assessing wound infection and healing progress.In this context, we consider the following specific VOCs as illustrative examples, namely ethanol, isobutanol, and formaldehyde.Additionally, we examine chloroform, given its serious harmful effects and its solubility for the sensing materials employed.To evaluate the sensing performance of the BSGS, the response of the sensor was evaluated using the following formula: where R 0 represents the initial resistance observed in a dry air environment, and R t corresponds to the measured resistance in the target gases.The dynamic sensing outcomes of the BSGS at room temperature, with varying concentrations of ethanol ranging from 0.2 to 50 ppm, are illustrated in Figure 3a.As the concentration of ethanol gas increased, the sensor response exhibited a corresponding increase, demonstrating the BSGS's ability to differentiate between different ethanol concentrations.Importantly, as depicted in the inset of Figure 3a, the response of the BSGS exhibited a linear relationship with the concentration of ethanol, simplifying the practical application.The fitting curve revealed a response slope of 0.01934 ppm −1 with a fitting quality r 2 = 0.99677.At room temperature, the calculated noise value of 0.009315 leads to a theoretical detection limit of 0.04 ppm (for calculation details, see the Supporting Information).The assessment of repeatability is an essential measurement for sensors in actual sensing scenarios.In order to analyze this index, the BSGS was continuously assessed through three cycles.Figure 3b demonstrates that the BSGS responded reliably to the same concentration.The response time, which is measured as the time it takes to reach 90% of the maximum response, was recorded as 160 s for 10 ppm of ethanol.The recovery time, which is the time needed to go back to 10% of the stabilized response in dry air, was measured to be 265 s (Figure S13, Supporting Information).Additionally, the BSGS maintained a stable response to 10 ppm of ethanol over 180 days, without any significant degradation in terms of conductivity and sensitivity (Figure S14, Supporting Information).This emphasizes its strength and reliability.
The primary components of HDIM's sensing capability consist of two crucial aspects: I) the redox reaction occurring between HDIM and the analyte, and II) the swelling effect (Figure 3c).I) The p-type semiconductor sensing ability of Ti 3 C 2 T x MXene toward adsorbed VOCs is evidenced at room temperature.At this temperature, the oxygen molecules on the surface of HDIM will sequester electrons from HDIM, creating oxygen anions (O 2 − (ads)).[44] The process can be summarized as the two formulas as follows: II) The introduction of VOC molecules into the HDIM induces a swelling process, which in turn leads to a greater separation among MXene sheets, hindering the electron hopping processes and resulting in an increase in the resistance of the BSGS. [45]herefore, the combined effect of these factors contributes to the observed increase in resistance.
In addition to ethanol, the BSGS was also assessed for its realtime reaction toward three other distinct VOCs (Figure 3d), with excellent reproducibility being noted (Figure S15, Supporting Information).Notably, the response speed and amplitude of BSGS were highest for chloroform among these four VOCs (Figure 3e).This could be attributed to the more potent interaction between the HDIM material and the larger polarity of chloroform. [46,47]In addition, chloroform's capability of dissolving HDIM can result in structural and property changes in the material.These changes may appear in the form of discrepancies in surface topography, conductivity, and adsorption characteristics of the HDIM, thereby amplifying the effect of the VOCs detection procedure and the related response.

Self-Healing and VOCs Sensing Properties of BSGS after Treatment
[50] Consequently, it is essential to include self-healing capabilities in order to improve the longevity and dependability of these gadgets.To evaluate the self-healing properties, scratches were deliberately inflicted on HDIM surfaces and then placed in various temperature conditions.As can be seen in Figure 4a, it took roughly 72 h for the scratches to recover completely at room temperature.In contrast, at 40°C and 60°C, the healing time drastically decreased to 60 min and only 10 min, respectively, which shows that HDIM possesses a remarkable self-healing ability that can be further boosted by heating.Dynamic mechanical analysis (DMA) on HDIM disclosed more knowledge about its mechanical and self-healing behavior (Figure 4b).At room temperature, the storage modulus (E') exceeded the loss modulus (E″), which implies that the material behaves like an elastic solid.With higher temperatures, E′ and E″ dropped, demonstrating a transition to a more viscous state in HDIM (Figure S16, Supporting Information).Therefore, the transfer of hydrogen bonds in the urea/PU bonds was sped up, thereby improving the efficacy of self-healing.
The damaged HDIM reestablished its electronic conductivity when the two pieces were brought together (Figure 4c).Moreover, HDIM's conductivity stability during the stretching-relaxing process was noteworthy (Figure S17, Supporting Information), attesting to its potential in daily use.To investigate the BSGS's dependability in practical applications, we compared its VOC sensing performance before and after varied treatments.At 10 ppm of ethanol gas, there was little difference in the BSGS's response that was repaired from scratch compared to the original response (Figure 4d).Additionally, Figure 4e presents the results of the fatigue test, which included stretching and relaxing the BSGS multiple times.Despite the heightened noise level, the response value at a similar response time was little altered.These results affirm the BSGS's capability to remain resilient in testing circumstances.

VOCs Identification by MISSA
Drawing on the benefits of the gas filtration effect and stackable design exhibited in masks, we have crafted a unique selfhealing sensor array, called MISSA, to obtain more information and simplify the detection of different VOCs. Figure 5a illustrates that MISSA was simply produced by aligning three equal BSGS units (with the same shape and size) in a vertical line and tightly enclosing them with tape, thus providing a layered effect.In addition, PU electrospun film, much like the filtration system found in masks, is capable of changing the diffusion rate of gas molecules arriving at layer two and layer three.Figure 5b presents the comprehensive set of various response values and their accompanying time patterns shown by each layer of MISSA in terms of ethanol gas sensing.This can be attributed to the absorption properties and obstruction effect caused by the PU film, leading to a sequential decrease in the response values and rates across layer one, layer two, and layer three.The outcomes suggest that MISSA can be proficiently used as a sensor array to create diverse dynamic reactions to the same VOC.Figure 5c portrays the response traits of MISSA to four VOCs at a concentration of 10 ppm.This unique set of code bars enables an easy examination of certain VOC types, thus permitting efficient classification within the sensor array.
To enable mobile gas surveillance and compact display, we merged the MISSA with a FPCB equipped with Bluetooth technology to develop a portable electronic system, as shown in Figures S18 and S19 (Supporting Information).Moreover, a tailor-made application program was designed to obtain and process the real-time VOC sensing data, which was subsequently exhibited on a smartphone.As illustrated in Figure 5d, when the MISSA-based electronic system was placed in the ethanol atmosphere, the smartphone screen showed three real-time response graphs matching the MISSA three-layer construction.Thus, the smartphone-compatible VOC sensing system, blending MISSA with portable circuits, features superior sensing performance and holds potential for application as a wearable covering in VOC gas monitoring.

VOCs Analysis and Identification with MISSA
Acquiring dynamic gas response curves for four gas species with concentrations of 0.2, 5, and 10 ppm through MISSA, 12 distinct features that accurately reflected the respective response curves were extracted by utilizing fitting parameters labeled according to the gas type and concentration.This process is essential to protect human health in an environment with VOCs frequently encoun-tered in a mixture.These parameters include the maximum response value (S i max ), response time (t i res ), recovery time (t i rec ), and the offset (y i off ), where i represents the layer number of MISSA (Figure 6a).Each line connects the values of the parameters along their respective axes, representing a single point within the 12D parameter space.This visualization offers valuable insights into the clustering patterns of gas responses at specific parameters, highlighting the convergence of measurements.
To visualize data more clearly, we employed PCA dimensionality reduction to extract the primary features and transformed the 12D parameters into a 2D space represented by PC1 and PC2 (Figure 6b).From the left panel of the figure, it can be seen that PC1 (accounting for 66.6% of the variance) and PC2 (accounting for 26.1% of the variance) have the highest variances and the combination of the two amounts to a variance of ≈92%.This indicates that PCA retains almost all of the original data information and achieves effective dimension reduction.Moreover, the right panel of Figure 6b displays the loading scores of the extracted parameters onto the main principal component.The combination of PC1 and PC2 forms vectors that demonstrate their respective influences on the principal components, as suggested by the gray arrows in Figure 6c.This dimensionality reduction approach enabled the separation of four gas species, each with three different concentrations, into 12 clusters, thus making it applicable for subsequent gas identification and concentration prediction (Figure 6c).K-Nearest Neighbors (kNN) is both a straightforward and resilient method for multi-category problems with small sample sizes.To create kNN model, the PCA results were utilized as the training data due to its noteworthy efficiency and precision.As seen in Figure 6d, the PCA-assisted kNN produces a clear, discernible boundary decision map for the four VOC gas groups.We used the five-fold cross-validation and illustrated the confusion matrix in Figure 6e which shows the results of 446 test samples on the identification of the four VOC gases.This displays an impressive identification accuracy of 99.77%, further testifying to the usefulness of PCA-assisted kNN in precisely recognizing the four VOCs.
To predict the concentration of gas, a linear regression model was created using data from 0.2 and 10 ppm of ethanol.The 5 ppm data was only used to validate the model's prediction accuracy.The red dashed line in Figure 6f displays the fitted line.The model yielded a high prediction accuracy of 98.3% for ethanol (calculation details in Supporting Information).The regression surface was then projected into a 2D PC space to display the prediction regions (Figure 6g).The x-axis and y-axis represent PC1, PC2, and the z-axis corresponds to the ethanol concentration.Notably, the red block within the pink decision region (representing 5 ppm ethanol) matched the data that was not used to train the regression model.This proves the effectiveness of the PCA-assisted ML technique for identifying VOCs and accurately predicting their concentrations.

Summary and Conclusion
This study designed and created a novel type of MISSA to identify VOCs using an efficient single-stacking technique.MISSA consisted of three levels of BSGS, each developed by applying self-healable HDIM to a PU electrospun film through screenprinting technology.The results showed the superiority of BSGS with a small detection limit of 0.04 ppm, a range from 0.2 to 50 ppm, consistent performance, and reliable self-healing abilities.Also, similar to the mask design, MISSA was able to create a code bar that detected four VOCs in three different concentrations due to selective adsorption in each layer.Additionally, pairing MISSA with FPCB in a mobile phone-ready portable device allowed convenient use for wearable monitoring of VOCs.Hybrid PCA-assisted ML demonstrated the distinction of decision boundaries, providing 99.77% accuracy in VOC identification.Additionally, 3D regression surface prediction accurately predicted the concentration of ethanol with an accuracy of 98.3%.Considering its remarkable characteristics, MISSA is believed to be a promising tool for VOCs detection, having the potential to act as a dependable and precise monitor for prolonged everyday use in order to protect human health.
Characterization: Fourier-transform infrared spectra were recorded using the Nicolet 6700 (Thermo Scientific) Fourier Transform InfraRed.The XPS patterns were performed on the AXIS Ultra DLD (Shimadzu).The elemental mapping was carried out using RISE-MAGNA (TESCAN).The recovery of scratches was observed using the MV3000 microscope from Jiangnan.The DMA measurements were performed on the DMA-Q800 (TA Instruments).The electrospinning machine used was the HZ-12, purchased from Huizhi Electrospinning.The TGA was carried out using the Labsys Evo.The CA measurements were performed using the DSA100 instrument from KRUSS.
Synthesis of HDIM: HTPB (2.1 g, 1 mmol) underwent a vacuum treatment at 80°C for 2 h to eliminate any residual moisture.Following this, IPDI (467 mg, 2.1 mmol) and DBTDL (5 mg, ≈1600 ppm) were dissolved in THF (10 mL) and added dropwise to the HTPB reaction vessel.The resulting mixture was stirred for 1.5 h under a nitrogen atmosphere to achieve a homogeneous and viscous liquid state.Subsequently, DE (174 mg, 1 mmol) was introduced as a chain extender to the reactor.The mixture was further subjected to an additional 36-h treatment at 80°C to facilitate the desired reaction.It was then poured into a rectangle Teflon mold and left to slowly evaporate at room temperature overnight.The resulting mixture was dissolved in chloroform to achieve a concentration of 0.1 g mL −1 , and 10 mL of the solution was taken and heated at 80°C while stirring.MXene (5 mL, 5 mg mL −1 ) was added dropwise to the heated mixture and stirred for 1 h, resulting in a homogeneous viscous liquid.The final mixed solution was poured into a rectangle Teflon mold and left to slowly evaporate at room temperature overnight.Subsequently, the resulting film was dried in a vacuum oven at 80°C for 24 h to remove residual solvent, yielding a dark HDIM film.
Fabrication of BSGS and MISSA: PU was dissolved in a mixture of DMF and THF (40/60, v/v) at 60°C to a concentration of 13 wt.%.The prepared PU solution was loaded into a 5-mL syringe and fed through the electrospinning apparatus at a controlled rate of 1.5 mL h −1 using a syringe pump.An applied voltage of 14 kV was utilized, and the distance between the needle tip and the target receiver was set at 10 cm.The resulting PU film was cut into rectangular shapes measuring 1 cm × 1.5 cm.Subsequently, the prepared HDIM was dissolved in chloroform at a concentration of 0.1 g mL −1 and printed onto the cut PU film as ink using a screen-printing technique.After the solvent evaporation, the sensor was secured in place using conductive adhesive tape.To create a MISSA, three breathable self-healing sensors were arranged in a symmetrical manner, with identical appearances positioned at the top and bottom.The edges and bottom of the sensors were sealed using impermeable PVC tape to ensure a complete enclosure.
Gas Sensing System and Resistance Measurements: In order to measure the dynamic resistance variation of the breathable self-healing sensor, a gas-sensing system was established.The prepared sensor was placed within a cylindrical gas-sensing chamber.In order to mitigate the potential impact of humidity, a protective measure was implemented during the testing phase wherein a PU electrospun film was employed to cover the surface of the sensor to be measured.Target gases were appropriately diluted using dry air, and precise control of gas concentrations was achieved by employing accurate digital mass flow controllers (Sevenstar, CS200).The resistance changes of the sensor during gas detection were recorded using a data acquisition module (Keithley, 3706A).Data processing (PCA, kNN, and linear regression) was conducted using Python programs in this study.

Figure 1 .
Figure 1.Conceptual illustration of MISSA preparation and its use for VOCs analysis and identification.

Figure 2 .
Figure 2. a) Schematic illustrating the two constituents of HDIM.b) FTIR spectra, c) XPS survey, and d) XRD patterns of HDIM.e) Contact angle of water on HDIM exhibiting favorable hydrophilicity.f) The dissolved HDIM in chloroform to form a conductive ink-like solution.g) The electrical conductivity of HDIM when incorporated into the electrospun PU film.h) Image of the BSGS.i) Standardized array created by screen-printing.

Figure 3 .
Figure 3. a) Real-time response of the BSGS to ethanol gas; The inset figure shows a linear correlation of the response values as a function of ethanol concentration.b) Repeatability tests of BSGS toward different concentrations of ethanol.c) The gas sensing mechanism underlying the HDIM composites, highlighting the pivotal roles of redox reactions and the swelling effect in the sensing process.d) Responses to different VOC gases at concentrations of 0.2, 5, and 10 ppm, including ethanol, isobutanol, formaldehyde, and chloroform.e) Single response waveforms when exposed to 10 ppm concentrations of various VOC gases.

Figure 4 .
Figure 4. a) Optical images showing the progressive self-healing process of the scratched HDIM film at room temperature, 40°C, and 60°C.Scale bar: 500 μm.b) Dynamic mechanical analysis highlighting the dominance of the E′ over the E″ across most frequencies.c) Photographs demonstrating the ability of the severed and subsequently self-healed HDIM film to illuminate an LED indicator when connected in series.d) Dynamic response curves to 10 ppm ethanol gas, both in its original state and after scratch healing.e) Dynamic response curves to 10 ppm ethanol gas, both in its original state and after 1000 times stretching.

Figure 5 .
Figure 5. a) (i) Three individual BSGS units for MISSA construction, each possessing identical shape and size; (ii) The front view of the MISSA; (iii) The back view of the MISSA.b) The response values and corresponding time profiles of the MISSA in the context of ethanol gas sensing.c) Bar chart representation showing the response characteristics of the MISSA when exposed to VOCs at a concentration of 10 ppm.d) Schematic diagram of the smartphone-enabled VOC sensing system.

Figure 6 .
Figure 6.a) Parallel coordinate plots of 12 parameters extracted from all VOC gas response measurements for ethanol, isobutanol, formaldehyde, and chloroform, corresponding to the blue, red, yellow, and green group lines, respectively.b) Principle component loading score for the 12 extracted parameters.c) PCA scatter plots of four VOC gases at three concentration levels d) Decision map for the four VOC groups identified by the PCA-assisted kNN method.e) Confusion matrix for four VOC gases based on PCA-assisted kNN classification results, EtOH represents ethanol, I-BUT represents isobutanol, HCHO represents formaldehyde, and CHCl 3 represents chloroform.f) Relationship between predicted and true gas concentrations of ethanol.g) Regression surface for the predicted concentration of ethanol.