Miniaturized olfactory sensor chip-based AI-wearable biometric ring for human body metabolic odor analysis

Domain: biology
Published: 2026-03-27
Editorial outcome: major_revision

Abstract
The human body emanated metabolic odor encompass biometric information used for tracking metabolic conditions of individuals. The challenge of decoding these long-unappreciated physiological signals of metabolic states impedes the development of technologies for real-time monitoring and next-generation personal health digitization. Here we introduce a monolithic AI-wearable biometric ring designed to non-invasively and reliably monitor skin volatile-organic-compounds associated with diet-related metabolism. The ring utilizes the attention-based feature fusion algorithm on a miniaturized olfactory sensor chip, incorporating three-dimensional vertical hetero-interfaces within a small footprint of 0.0081 mm². It discriminates six categories of diet-induced emanated volatile-organic-compound patterns, three physical activities, and quantifies food intake with a 0.991 determination coefficient. Through gas chromatography-mass spectrometry validation and the personalized AI health agent, our biometric ring demonstrates broad efficacy in human body odor monitoring with evident diet-induced metabolic adherence.

Introduction
The odor of human body comprises an assort ment of volatile organic compounds (VOCs) as gaseous molecular biomarkers are emitted majorly from skin, breath, and even urine1. Many of these VOCs are the physiological metabolic byproducts and the human body odor can be further utilized as exclusive ‘odor-fingerprint’ of individuals for tracking physical fitness and early disease diagnosis2-4. Previous research has uncovered that the VOC biomarkers are indicators of diseases including lung cancer5-6, chronic disease7, and metabolic disorder8-9. Intriguingly, the human body odor profile may also show physiological patterns related to diverse dietary intakes10. Diet-induced metabolic alteration engages the oral cavity11, stomach12, liver13, and intestines14 with a series of enzymatic processes for VOCs biomarkers’ generation15 (Fig. 1a). Therefore, monitoring and decoding the human body odor VOCs patterns can be utilized to improve daily dietary choices with regular physical activities for optimal nutritional health and chronic disease prevention16-17.

Conventionally, the gold standard in clinical research for the human body odor VOCs’ analysis relies on gas chromatography-mass spectrometry (GC-MS) with trace-level sensitivity and multi-species gas identification capability18-19. However, GC-MS is not a viable solution for daily personal VOCs monitoring owing to its bulkiness, high cost, and local data access limitation. Notably, the skin is the largest organ of the human body, serving as the primary interface between the external environment and the glandular secretion of human body emanated VOCs20. In principle, mounting a wearable VOC sensor on the skin serves the purpose of long-term continuous monitoring21. However, the emission rate of VOC released by the skin is on the order of hundreds of µg h-1 per person22, along with surrounding interfering gases. This mandates that the wearable VOC sensor mounted on the skin not only accurately identifies various VOCs but also possesses high sensitivity and anti-interference capability.

To address these challenges and achieve daily human body emanated VOC non-invasive monitoring, here we report a monolithically integrated artificial intelligence empowered wearable (AI-wearable) biometric ring with the miniaturized olfactory sensor chip. The ring emulates AI embodiment23 comprising sensory perception, signal extraction, AI decision, and “odor-fingerprint” pattern recognition (Fig. 1b). The miniaturized olfactory sensor chip features three-dimensional vertical hetero-interfaces (3D-VHI) for high sensitivity of multicomponent gas species. The ultra-small form factor of the olfactory sensor chip (0.0081 mm² device area) enables mechanical conformability with the human finger in a ring configuration with full-angle bendability of the flexible circuitry having dimensions of 8.5 × 54.0 × 0.21 mm3 towards real-time human body emanated VOC analysis (Fig. 1c-e). As a demonstration, diet-induced skin VOC physiological signals are acquired from human participants with respect to six types of dietary intake: fruit, nuts, meat, carbohydrate (carbs), vegetable, and alcohol. The human body emanated VOC patterns are classified for dietary intakes with an accuracy of 98.20% and validated by GC-MS. Additionally, axial-attention stacked long short-term memory neural network (AA-sLSTM) is developed to quantify the intake with a 0.991 coefficient of determination. As such, a personalized AI health agent is developed to generate the health guidance based on the results (Supplementary video 1). Overall, our miniaturized wireless AI-wearable biometric ring heralds a revolution in the field of personalized healthcare by recognizing unique "human body odor" patterns in response to known diet alterations, as a supplement to the gold standard GC-MS. In the future, it has great potential to facilitate long-term human nutrition research and chronic disease prognosis by establishing objective and continuous physiological measurement of diet adherence.

Results
Miniaturized olfactory sensor chip fabrication and characterization
Figure 2a is the schematic illustration of the miniaturized olfactory sensor chip. The chip comprises five major components: Pt sensing electrodes, three-dimensional vertical hetero-interfaces (3D-VHI), Pd decorated SnO nanotubes within the nano honeycomb membrane, an electrical insulation layer, and an integrated micro-heater. The fabrication process flow is shown in supplementary fig. 1. Figure 2b shows the photographs of the olfactory sensor chip. The left-side photo is the magnified image of the olfactory sensor chip. The right-side photo shows the size comparison on a fingertip. The overall packaging dimension of the sensor chip is 3 × 3 mm2 with the chip carrier and the compact device area is 0.0081 mm2 (Supplementary table 1). The pore diameter of the nano-honeycomb membrane is about 300 nm (Supplementary fig. 2a) and such a structure utilizes the large surface-to-volume ratio enhancing the gas molecules’ interaction with the sensing material. The scanning electron microscopy (SEM) and energy dispersive X-ray spectrometry (EDS) show lateral 3D-VPHI element distribution on the top (ZnO, TiO2, and WO3) and bottom (In2O3, NiO, and Cr2O3) surfaces (Fig. 2c to 2d). The various oxide-oxide semiconductor interfaces could construct different interface energy barriers based on their electronic property of work function, and electron affinity to build unique sensing patterns for testing multiple target gases. The energy barrier height modulation would happen in response to the VOC analyte adsorption/desorption state, which significantly enhances the sensing performance24. A 1.5 µm thickness SiO2 insulating layer is deposited between the bottom surface of the nano honeycomb membrane and the integrated Pt heater to provide electrical isolation (Supplementary fig. 2b).

The backbone sensing material is vertical Pd@SnO2 nanotubes formed conformally along the sidewall of the nano-honeycomb membrane by atomic layer deposition (ALD). The thickness is ~10 nm (Supplementary fig. 2c). The structure and element distribution of Pd, Sn, Al, and O is confirmed by cross section SEM and transmission electron microscopy (TEM) with EDS mapping (Supplementary fig. 3a to 3b). The Pd nanoclusters have lattice distance of 0.29 nm and lattice plane (111) (Supplementary fig. 3c). The crystal structure and composition of the Pd@SnO2 are determined by X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). In the XRD analysis, the tetragonal rutile structures of SnO2 (JCPDS no. 41-1445) and Pd (JCPDS no. 46-1043) are confirmed but Pd nanoclusters have small intensity peaks due to the small amount cycle deposition. XPS analysis further confirms the formation of Pd@SnO2 schottky junction (Supplementary fig. 3d to 3f). The cross-section EDS of the top and bottom hetero-interface representatives (ZnO-SnO2, and Cr2O3-SnO2) clearly shows that Pd majorly exists and distributes along the side wall of the nano honeycomb membrane while the ZnO-SnO2 and Cr2O3-SnO2 are deposited on the surface (Supplementary fig. 4a-b).

The intersected sputtering process creates a top-bottom 3D-VHI rendering the olfactory sensor chip with diversified pixels. Six different types of metal oxide are successively sputtered on each top row and bottom column respectively. The detailed planar element distribution on top and bottom surfaces of the 3D VHI is characterized by EDS (Supplementary fig. 5a to 5b). The crystal structures of 3D VHI are characterized by XRD (Supplementary fig. 6a to 6f). The composition of the 3D-VHI exhibits distinctive hetero-interfaces formation in terms of ZnO-SnO2, TiO2-SnO2, WO3-SnO2, In2O3-SnO2, NiO-SnO2, and Cr2O3-SnO2 (Supplementary fig. 7a to 7f). 500 Å thickness of 3D-VHI prevents the obstruction from the nano honeycomb membrane and ensures the gas sensing performance. (Supplementary fig. 8a to 8d).

Miniaturized olfactory sensor chip sensing measurement and performance evaluation
The olfactory sensor chip is suspended and securely bonded onto a dual in-line ceramic chip carrier for gas sensing performance measurement. Due to the integration of the in-situ micro-heater, the operational temperature can be modulated locally without external heater attachment. The sensor chip working temperature is set to be 180 C, and the corresponding heat power is ~17.21 mW, which is verified by both simulations and measurements (Supplementary fig. 9a to 9f). Seven gas species (acetone, formaldehyde, ethanol, isopropanol, toluene, isobutylene, and ammonia) with concentrations ranging from 20 ppb to 1000 ppb are tested three times to examine repeatability. Taking acetone as an example, the real-time gas sensing response curves of the olfactory sensor chip are measured (Fig. 2e) with excellent repeatability. In this work, we design a voltage divider-based crossbar suppression topology with pseudo potential implementation. Row/column buffers and the scaling buffers are added to suppress sneaky current and fit in the appropriate range for the next stage analog-to-digital converter sampling to achieve accurate current flow (Fig. 2f). The real-time sensor chip resistance curves and extracted gas sensing response distribution curves of each gas species are measured both under dry air and increasing relative humidity (RH) from 30% to 80% (Supplementary figs. 10-23). The sensing response curves validate the overall response diversity of the entire sensor chip in sync with the 3D-VHI device structure, which is applicable in the multicomponent gas classification applications. To further reduce the measurement bias caused by the pre-defined sequential gas concentration order, the olfactory sensor chip is tested under 7 gas species in a randomized order (400-1000-800-80-200-40-100-20 ppb) under 4 different humidity atmospheres. The gas sensing response under both sequential and randomized concentration order is compared and visualized in group box plots, indicating the similar sensor responses across these two testing protocols (Supplementary fig. 24a-d). To further assess the similarity between two testing protocol measurement results, we adopted the minimum square error (MSE) to serve as a robust metric between 2 sets of the sensing response across 4 humidity atmospheres. The results show that all MSE values are below 2.5, which indicates that the sensor responses are nearly identical regardless of the measurement methodology (Supplementary fig. 24e-h).

To visualize the performance of the entire olfactory sensor chip, the response values are normalized to a grayscale between 0 and 255 in the violin plot by merging the responses from 20 ppb to 1000 ppb of seven gas species. The results indicate the diversified response patterns of different gas species with high consistency over a range of concentrations under dry air condition (Fig. 2g). The radar maps of gas sensing responses toward seven gas species enhance spatial awareness of the diversified sensor chip to develop AI algorithms to classify different gas species accurately and efficiently. Multi-group analysis between 3D-VHI olfactory sensor chip and single Pd@SnO2 sensor chip is conducted and visualized by box normal plots (Supplementary fig. 25). The results indicate a robust enhancement and diversification of response patterns facilitated by the 3D-VHI.

Multicomponent gas classification and regression analysis with the miniaturized olfactory sensor chip
The ability of the olfactory sensor chip to identify a single gas when placed in a particular atmosphere is a prerequisite for predicting gas concentration as a real-time olfactory system. Here, we employ least discriminant analysis (LDA) and k-nearest neighbors (KNN) to differentiate seven gas species from the extracted sensing responses of the sensor chip. The newly input response pattern detected by the sensor chip is classified into the specific gas species based on the distance metric. Figure 3a to 3d show multiple gas species discrimination in different relative humidity (RH) environments (dry air, 30%, 60%, and 80%) that could be treated as one kind of distinct gas with its own cluster. The actual and predicted gas labels are shown in the confusion matrix from figure 3e to 3h, which demonstrates that such a miniaturized olfactory sensor chip can provide highly condensed sensing information to achieve high classification accuracy of our model.

Apart from discrimination of multicomponent gases, we implement the axial-attention stacked long short-term memory (AA-sLSTM) recurrent neural network to establish regression analysis of multicomponent gas concentration (Fig. 3i). Here we adopt axial attention as the feature engineering method for preprocessing to efficiently capture global feature associations from rows and columns of the sensor chip as well as reduce computational complexity25. Furthermore, a stacked long short-term memory recurrent neural network can be optimized by modifying the number of layers and the number of cells within each layer. Consequently, we harness these advantages to optimize the feature information with high prediction accuracy.

Taking acetone concentration regression analysis as a representative, the training epoch takes over 48, 000 with validation accuracy reaching 98.80%. The training loss and validation loss decrease dramatically with convergence to the magnitude of 10-5 as epoch increases over 48, 000 (Fig. 3j to 3k). The scatter plots of the predicted values of acetone concentrations demonstrates remarkably high model robustness under different humidity conditions with coefficient of determination all above 0.990 (Fig. 3l). Figure 3m presents the relative mean error between the predicted and true values of eight concentrations of acetone at dry air, 30% RH, 60% RH, and 80% RH respectively. The small relative mean error of the individual gas prediction values confirms a high degree of fit in the regression analysis. Other six types of gases’ regression analysis exhibits the excellent capability and high accuracy of multicomponent gas concentration prediction corresponding to various humidity ranges (Supplementary figs. 26-31, supplementary tables 2-3).

To further evaluate the performance of the olfactory sensor chip under the practical scenarios of the VOCs complex mixtures in terms of various concentration and gas species rather than a single gas component, we performed a quantitative analysis of the VOC mixtures to demonstrate the olfactory sensor chip capability of identifying the gas mixture composition. We construct the dataset of every two of five gases (acetone, ethanol, formaldehyde, isopropanol, and toluene) to form 10 categorical VOC mixtures. Each categorical VOC mixture includes 9 concentration gradient groups (800, 400, and 100 ppb per gas component), producing 90 kinds of mixture dataset. Radar plots visualize sensor response patterns of each categorical VOC mixture (Supplementary fig. 32 a-b). To assess the mixture classification feasibility, we performed LDA with extracted responses as input features and compared mixture and single-gas response projection. It is noticed that acetone-, ethanol-, formaldehyde-, and isopropanol-based mixture samples are directionally located between corresponding single gas component samples within the mixture (with red arrow labelled, Supplementary fig. 32c-e). The confusion matrix reveals high accuracy and minimal misclassifications for mixtures and single gases (Supplementary fig. 32f). Four classifiers (KNN, Supporting Vector Machine (SVM), Logistic Regression (LR), and Decision Tree (DT)) undergo comparison via receiver operating characteristic (ROC) curves, where KNN achieves the highest area under curve value of 0.985 (Supplementary fig. 32g). We further performed quantitative regression analysis of individual gas component concentrations within mixtures. Predicted concentrations match actual values with relatively small MSE (Supplementary fig. 33a-t). This outcome confirms the capability of the olfactory sensor chip to classify individual gas species and predict individual gas concentrations within complex mixtures.

Continuous monitoring of various dietary intakes and physical activity statuses
The evaluation of the olfactory sensor chip for six categories of dietary intakes and three physical activity statuses is conducted on six healthy participants including three males and three females to enhance the diversity of the participant pool (Supplementary table 4). Each participant wears the AI-wearable ring on the finger for the measurements and wireless data acquisition (Supplementary fig. 34a-c). A centralized-charging ring pod was implemented to extend local battery management for convenience (Supplementary fig. 34d). To perform multiplexed sensing accurately, the flexible mainboard is designed with analogue front-end (AFE), data acquisition unit (DAQ), and power management unit (PMU) (Supplementary figs. 35, 36a-b). The wearable ring is designed to be highly mechanical wearability with both the finger-conformal cases (Supplementary fig. 37a-b). The dynamic sensing profiles from the sensor chip are utilized to automatically extract the responses and perform analysis to accurately classify dietary intake and physical activity statuses (Fig. 4a-b, supplementary figs. 38-42).

For each dietary intake experiment, on-body physiological metabolism exhibits significant variations in response to individual sensors. During the alcohol intake experiment, the physiological responses of the sensor chip are detected after intake, consistent with previous reports on transdermal dynamics related to alcohol consumption26. Additionally, the sensor chip exhibits the most pronounced response among the six types of dietary intake. In the fruit intake experiment, significant modulation in the sensing response is observed, primarily attributed to fructolysis in the liver27. In the meat intake experiment, two response peaks are observed sequentially, resulting from the stepwise protein hydrolysis during the gastric and intestinal phases 28. During the carbohydrate intake experiment, substantial differences in the response patterns and the shortest time delay among the six dietary intakes can be explained by the metabolism of high glycemic index carbohydrates29. In the nut intake experiment, a profound activation of unsaturated fatty acid metabolism during the intestinal phase resulted in significant modulations of skin VOC emissions30. During the vegetable intake experiment, we observe intermediate differences in the response curves. It is ascribed to the indigestible properties of cellulose and the limited fermentation by microorganisms31. To validate the sensor chip reproducibility of the classification of various food intake across different individuals, we performed a merged analysis incorporating samples from all individuals for these six food types, employing LDA combined with KNN classification. The result indicated clear classification of the overall patterns for different foods, confirming that the classification algorithm could reliably separate food types across diverse individuals (Supplementary fig. 43-44).

The physiological states of the body can shift and subsequently affect skin VOC emissions in response to various physical activities through exercise-induced metabolism32. In this study, we utilize our olfactory sensor chip to monitor skin sensing patterns throughout the relax-exercise-recovery process. During the casual relax phase, the initial physiological responses reach equilibrium with a stabilized response from the sensor chip to serve as the baseline. During the vigorous exercise phase, we observe a gradual increase in the response curves of the sensor chip during exercise due to metabolic transitions and skin VOC emissions during exercise32. In the post-exercise recovery phase, we observe an abrupt drop in the responses. This phenomenon can be explained by the transition of metabolites from an exercise state to a relaxed state, resulting in the olfactory sensor chip response recovery compared to the peak response value. We demonstrate both the integrated functionality and the on-body testing of our AI-wearable biometric ring during real-life daily activities. These results indicate that the olfactory sensor chip can reliably monitor dietary-induced and activity-induced based on body metabolic skin-emitted VOC physiological signals.

Human dietary intakes and physical activity statuses assessment through on-body metabolic odor using the AI-wearable biometric ring
To embody human metabolic emanated VOC sensing patterns for diets and activities assessment, we design the AI-empowered assessment workflow (Fig. 5a). We tackle this challenge in three stages: signal feature fusion, pattern learning, and human diet/human status evaluation with the personalized AI agent. All pixel signals are normalized to ensure that the extracted responses are consistent against patch variations. Here we project the multidimensional data space into two-dimensional (2D) data space by means of principal component analysis (PCA) with stack marginal density analysis (Fig. 5b-c). The marginal density of the PCA illustrates the intuitive distribution trends of both dietary intakes and physical activities along the principal component axes with obvious separation.

Taking participant 01 measurement data as an example, we evaluate four AI models for classification on six types of dietary intake. The trained KNN outperformed other three selected AI models with an accuracy of 98.20%, including linear function support vector machines (SVMs), decision trees, and logistic regression (Fig. 5d). This improvement can be attributed to smoothing the decision boundary and mitigating the influence of noisy data points and outliers by the strategic adjustment of the parameter K (the number of neighbors). Based on the dietary intake classification results, we expand our analysis to the evaluation of physical activity status classification. We adopt a similar KNN model to differentiate three types of physical activity status. We demonstrate the efficacy of the physical activity status classification through the confusion matrix (Fig. 5e).

We conduct the human metabolic emanated VOC spectrum analysis from three human participants as the verification method (Supplementary fig. 45). Taking alcohol intake from participant 01 as a representative, we observe a notable difference spectrum compared to the baseline spectrum, suggesting the scientific evidence of skin VOC unique patterns generation from diet-induced metabolism (Fig. 5f, supplementary fig. 46). The six types of dietary intake in other two human participants are analyzed in spectrum difference by GC-MS (Supplementary figs. 47-48).

To assess the quantification ability of the dietary intake, taking one sensor pixel as the representative, the responses of the sensor pixel to varying alcohol volumes: 3 mL, 12 mL, 24 mL, and 72 mL are illustrated as a demonstration (Fig. 5g). The result indicates a linear correlation between alcohol intake volume and sensor responses, which could imply future sensor calibration and application in detecting alcohol and other dietary intake levels. Moreover, we apply the AA-sLSTM neural network to perform regression analysis. We observe that the predicted values closely align with the actual measurements across various intake levels with the coefficient of determination of 0.991, suggesting the model efficacy and reliability in accurately estimating alcohol consumption (Fig. 5h). In this regard, we foresee significant potential for the AI-wearable biometric ring as an embodied AI health agent for personalized monitoring of human physical fitness.

In summary, we report an AI-wearable biometric ring that facilitates continuous the emanated human metabolic VOC monitoring through a miniaturized olfactory sensor chip during real-time operations. By employing a miniaturized top-bottom 3D-VHI design, we establish a comprehensive approach for generating a response diversified olfactory sensor chip corresponding to multiple gas species. To implement feature fusion with time-series data inputs, we develop an axial attention stacked long short-term memory (AA-sLSTM) recurrent neural network, achieving an average 0.996 coefficient of determination for linear gas concentration regression under various humidity levels. Our system demonstrates real-time physiological signal monitoring of six categories of dietary intakes and three physical activity statuses. Different dietary intakes and physical activity statuses can be classified through the skin sense profiles with 98.20% and 97.78% classification accuracy. Furthermore, it is validated by GCMS to exhibit diet adherence. We establish the quantitative analysis of dietary intake volume by applying AA-sLSTM to accurately predict dietary consumption with a 0.991 determination coefficient. Our AI-wearable biometric ring platform demonstrates significant advancement in the path of digitizing human dietary assessments, offering an efficacious wearable technology with tremendous potency in the genre of human nutrition science.

Methods
SnO2 and Pd atomic layer deposition (ALD) on nano honeycomb membrane
A ~10 nm-thick uniform SnO2 thin film was deposited into side wall of the porous anodic aluminium oxide (AAO) membrane as the nano honeycomb membrane with the size of 2 cm by 2 cm and the thickness of 50 µm (purchased from topmembranes technology co., ltd..) by ALD (MNT Micro and Nanotech Co., Ltd.). For the ALD SnO2 deposition process, the reactor temperature was set to be 150 °C. A metal shadow mask with window of 300 µm × 300 µm was used to confine selective deposition device region on the nano honeycomb membrane. Then, 70 cycles of tetrakis (dimethylamino) tin (C8H24N4Sn, TDMAS, 99.999%) and deionized H2O were injected into the reactor (150 °C) alternatively with high purity N2 (99.999%) as the carrying and purging gas with an injecting sequence of “TDMAS (600 ms pulse, 5 s waiting) - N2 (30 s purging) - H2O (60 ms pulse, 5 s waiting) - N2 (30 s purging)”. After SnO2 ALD process, 450 °C annealing was conducted for 3 hours. Then Pd nano particles were deposited into the side wall of the nano honeycomb membrane by the ALD method with 8 cycles. Reactor temperature for Pd ALD was 200 °C, and precursors were Palladium (II) hexafluoroacetylacetonate (99.999%) and hydrazine anhydrous. High purity N2 (99.999%) was the carrying and purging gas. The injecting sequence was “Palladium (II) hexafluoroacetylacetonate (1000 ms pulse, 5 s waiting) - N2 (30 s purging) - hydrazine anhydrous (200 ms pulse, 5 s waiting) - N2 (30 s purging)”.

Olfactory sensor chip fabrication by photolithography and intersected sputtering ZnO, TiO2, WO3, Cr2O3, NiO, and In2O3
Photolithography and sputtering were employed to deposit ZnO, TiO2, WO3, Cr2O3, NiO, and In2O3 films onto the top and bottom electrodes of the nano honeycomb membrane selectively to form olfactory sensor array. To initiate the process, a photoresist AZ5214 was spin-coated onto the membrane at 4,000 revolutions per minute (rpm) for 30 seconds using a solitec coater. Subsequently, a hard bake was performed at 90 °C for 90 seconds to remove solvent and enhance the adhesion. To pattern the olfactory sensor array, a chromium photo mask with column 1 top electrode line (30 µm line width, 300 µm length) of the olfactory sensor array was applied on Karl Suss MA6 under 27s UV exposure time (power density 20 mW/cm2) and FHD-05 60 seconds development time. For the deposition of ZnO, a 50 nm thick film was sputtered using radio frequency (RF) sputtering at a power of 80 W, with base and working pressures set at 7×10-4 Pa and 0.4 Pa, respectively. A lift-off process was executed to remove the exposed photoresist, leaving behind the ZnO film. Similarly, a 50 nm TiO2 film was deposited using the same spin-coating and hard bake process. The second column of the top electrode of the sensor array was defined using the chromium photo mask, and the development time and exposure to UV light were consistent with the previous step. RF sputtering was conducted at a power of 120 W, with base and working pressures of 7×10-4 Pa and 0.5 Pa, respectively. The lift-off process was then performed to eliminate the photoresist, resulting in the formation of the TiO2 film. For the deposition of WO3, a 50 nm thick film, the spin-coating and hard bake steps were repeated. The third column of the top electrode of the sensor array was defined using the chromium photo mask, followed by the same development and exposure times. RF sputtering was carried out at a power of 80 W, with base and working pressures set at 7×10-4 Pa and 0.4 Pa, respectively. The lift-off process was subsequently conducted to remove the photoresist and obtain the WO3 film. The deposition process was further extended to include Cr2O3, NiO, and In2O3 films, each with a thickness of 50 nm at bottom electrodes. The spin-coating and hard bake steps were repeated for each film, and the appropriate 3 chromium photo masks (row 1: Cr2O3 row 2: NiO row 3: In2O3) were applied to define the corresponding bottom electrode lines (each is 30 µm line width, 300 µm length). The development and exposure times were maintained as mentioned earlier. RF sputtering was performed at powers of 120 W, 80 W, and 80 W, respectively, while the base and working pressures were maintained at 7×10-4 Pa and 0.5 Pa for Cr2O3, and 7×10-4 Pa and 0.4 Pa for NiO and In2O3. The lift-off process was carried out after each deposition step to remove the photoresist and reveal the desired films on the nano honeycomb membrane. After the sputtering process, annealing treatment of 450 C was conducted for 3 hours.

Top and bottom sensing electrode deposition, insulating layer deposition, and heating electrode deposition
To pattern top and bottom electrodes on nano honeycomb membrane, photolithography method was utilized starting from photo resist spin coating. First, photo resist AZ5124 was spined on the membrane under 4,000 revolutions per minute (rpm) by using solitec coater for 30 s and hard bake was conducted at 90 °C for 90 s, following by a chromium photo mask with 3 top electrode lines (30 µm line width, 40 µm pitch, 600 µm × 600 µm electrode pad) used to pattern top electrode the membrane by Karl suss MA6 under 27s UV exposure time and FHD-05 45 seconds development time. 100 nm platinum was sputtered on the membrane with rate of 50 Å/s under pressure 1.3 Pa and 100 W DC power and further immersed into acetone for lift-off process. Then the membrane was flipped side and photo resist APZ5124 was spined on membrane under 4,000 revolutions per minute (rpm) by using solitec coater for 30 s and soft bake was conducted at 90 °C for 90 s, following by a chromium photo mask with 3 bottom electrode lines (30 µm line width, 40 µm pitch, 600 µm × 600 µm electrode pad) used to pattern top electrode on the membrane by Karl suss MA6 under 27 s UV exposure time and FHD-05 45 seconds development time. 100 nm platinum was sputtered on the membrane with rate of 50 Å/s under pressure 1.3Pa and 100 W DC power and further immersed into acetone for lift-off process. After top and bottom electrode deposition, 1.5 µm silicon dioxide was deposited as insulating layer at bottom side of the membrane by plasma enhanced chemical vapor deposition. For heating electrode fabrication, photo resist spin coating and hard bake were performed and silicon dioxide plane was patterned with heater electrode after develop process. 200 nm Pt was deposited by sputtering.

Olfactory sensor chip packaging
To package the olfactory sensor chip, a laser cutter was used with power 24kW, mark speed 1,200 mm/s, jump speed 2,000 mm/s, and cutting frequency of 30 kHz to cut the whole 2 cm × 2 cm membrane into 18 pieces of 2.8 mm × 2.8 mm area olfactory sensor array. Then, silver paste was dipped by using 1mL syringe and 0.15 mm diameter stainless needle on an MEMS chip carrier whose dimension is 3 mm × 3 mm with 8 gold electrodes whose size are 600 µm × 600 µm for each. The Olfactory sensor array was laid on the silver paste and baked at 120 °C, 10 min. to form conduction.

Material characterization
Morphology of the olfactory sensor chip was characterized by scanning electron microscope (SEM, JSM-7100F, JEOL). SnO2 and Pd catalyst on side wall were characterized by high-resolution transmission electron microscope (HR-TEM, JEM 2010F, JEOL). EDS mapping was achieved by high-resolution transmission electron microscope (HR-TEM, JEM 2010F, JEOL). Lattice characterization of six types of metal oxide was done by X-ray diffraction (XRD, X’pert Pro (PANalytical)) with scanning angle from 10° - 80° (ZnO, hexagonal, JCPDS no. 36-1451; TiO2, anatase, JCPDS no. 83-2243; WO3, orthorhombic, JCPDS no. 20-1324; In2O3, cubic, JCPDS no. 65-3170; NiO, rhombohedral, JCPDS no. 22-1189; Cr2O3, rhombohedral, JCPDS no. 38-1479). XPS analysis was conducted to characterize oxide-oxide junctions of SnO2-ZnO, SnO2-WO3, SnO2-TiO2, SnO2-Cr2O3, SnO2-NiO, and SnO2-In2O3 samples (XPS, Axis Ultra DLD). The thickness of the oxide layer was characterized by the surface profiler (KLA-Tencor Alpha-Step Stylus Profiler D300).

Gas sensing measurement setup
To measure gas sensing performance of the olfactory sensor chip, two mass flow controllers (MFC) (10, and 500 sccm) were used, where MFC 1(10 sccm) served as controllers for target gases injection, and MFC 2(500 sccm) flew compress air as carrying and diluting gas. After being mixed at a certain ratio, the diluted target gas was introduced into a stainless gas chamber, and the resistance signals were read out from cross bar array suppression customized circuit board and processed by the home-built python program with timestamp insertion.

Gas sensing response definition
Gas sensing response is defined as (Ra - Rg)/Ra × 100% (reducing gases) and (Rg - Ra)/Ra × 100% (oxidizing gases), where Ra and Rg are the resistances of sensors exposed to background gases (dry/wet air) and target gases, respectively.

COMSOL simulation of the integrated heater
Temperature distribution of the integrated heater was simulated by COMSOL Multiphysics 6.1. The simulation model contained three physics, namely heat transfer in solid (ht), electric currents in shells interface (ecis), and heat transfer in shells (htlsh). A substrate with the dimensions 2000 × 2000 × 50 𝜇m3 was modeled to simulate the temperature distribution. To simulate the real conditions and reduce computation power at the same time, Al2O3 with anisotropic thermal conductivity was assigned to the planar membrane, where vertical thermal conductivity was set as 1.38 W/(m∙K), and horizontal thermal conductivity was set as 0.046 W/(m∙K)33. The integrated heater used platinum as the conducting material with 40 𝜇m linewidth, 30 𝜇m line gap, and 450 𝜇m × 450 𝜇m overall dimension. For the ecis part, electric potential of 0.7, 0.9, 1.2, 1.5 V were applied to the heater. The heat conducts from the heater to AAO via thermal conduction and radiation. The electrode and the heater were set as convective heat flux and surface-to-ambient radiation separately. The heat transfer coefficient was defined with empirical setting based on the conditions of convective heat flux and surface-to-ambient radiation. The surrounding environment was set as atmosphere (1 atm., dry air, and 293.15 K temperature). In htlsh, the surface-to-ambient radiation was applied to the heater and the nano honeycomb membrane. The power of the heater was calculated from the simulation result of htlsh.

Human participant body metabolic skin-emitted VOC sampling method
A bag of polyvinyl fluoride (PVF) film with inlet and outlet valve, a Tenax TA tube, and a gas sampling pump were used to sample skin emitted volatile organic compound (VOC). First, to get rid of impurities inside PVF film, the PVF sampling bag with a dimension of 60 cm×40 cm was flushed by high purity helium (99.999%) 3 times and the Tenax TA tube was purged by the thermal desorption unit for 3 times. Then two edges of PVF sampling bag were cut into 20 cm diameter circle acting as entrance and exit for the participant hand to fit into, and the edges of cut PVF sampling bag were wrapped and attached in such a way that scotch tape was not in contact with inside of PVF sampling bag. The gas in valves at the PVF sampling bag was connected to another 5L PVF sampling bag which was filled in 5L high purity helium and the gas out valve was connected to one side of stainless absorption tube which was baked at 400 °C for 12 hours to remove residual VOC inside the tube. The Tenax TA tube was connected to the outlet of PVF film wrapping with participant hand for further sampling. After 40 min. of conditioning in ambient environment at room temperature, the 5L helium PVF sampling bag was suctioned off via setting gas sampling pump at a constant flow rate of 250 mL/min to flush the VOC accumulated inside the hand wrapped PVF sampling bag through Tenax TA tube for sample enrichment. After around 20 min. of sampling, the tube was sealed by two metal caps with o-ring and passed to GC-MS for analysis. The participant was required to fast overnight and the emitted skin VOC was sampled before dietary intake as baseline with a duration of half an hour and stored in one thermal desorption tube (Tenax TA tube), followed by another sampling duration of half an hour with capture in another thermal desorption tube after dietary intake. Both tubes were transferred into thermal desorption unit and further desorbed with injection into gold-standard laboratory gas chromatography-mass spectrometry (GC - MS) for spectrum acquisition.

GC-MS spectrum analysis
Samples were analyzed by GC-MS connected to a thermal desorption system (TD30R). The GC-MS instrument (GCMS-QP2020 NX, Shimadzu Corporation) was equipped with an Rtx-624 column (60 m in length; 0.32 mm inner diameter; 1.8 µm film thickness; max temperature 240 °C). First, the absorption tube was passed into TD30 to desorb the sample absorbed by activated carbon into the trap through a temperature programmed. Then the trap was heated up to desorb and inject the sample into column. Samples were analyzed in the split mode (33%) at a 38.7 cm/s constant linear velocity and in a 2.3 mL/min. column flow. The following oven temperature profile was set: (a) 3 min. at 40 °C, (b) 10 °C/min ramping up until 230 °C, (c) a hold time of 5 min. (∼27 min in total). The mass (m/z) scan was set between 35 and 300. The chromatograms and mass spectrometry were first analyzed by GC-MS solution Real Time Analysis (version 4.50 SP1, Shimadzu Corporation) at a minimum match factor of 60% and utilizing the NIST 20 library for compound identification. With the quantitative analysis tool of by GCMS solution Post Run Analysis (version 4.50 SP1, Shimadzu Corporation), the area under the curve (AUC) of the peaks in each sample was extracted and aligned between the samples. Compounds that did not appear in most samples or appeared to be noisy were excluded from the list.

AI-wearable biometric ring system design
The detailed system design of the AI-wearable biometric ring is illustrated in supplementary figs. 34-37. The fully integrated AI-wearable biometric ring consists of a flexible printed circuit mainboard for the signal processing and bluetooth wireless communication, a flexible LED screen for real-time result display, and an integrated fast-charging ring pod for continuous charging function implementation. The AI-wearable biometric ring mainboard readout circuit overview is shown in supplementary fig. 35. The system consists of power management unit (PMU), analog frontend unit (AFU), and data acquisition unit (DAQ). The PMU of the wearable system included a buck-boost converter to offer system operating voltage of 3.3 V within a broad input range of a single cell lithium-ion battery and an adjustable heating voltage manipulator with the use of digital-to-analog converter. The AFE was designed to readout 3 × 3 cross bar electrode based olfactory sensor chip. The readout circuit included word line selection, bit line selection, and cross talking suppression based on op-amp feedback loop. Word line selection module was responsible for word line multiplexing. Bit line selection module was responsible for bit line multiplexing. When the specific word line and bit line were selected, the electronic single pole single throw (SPST) switch connecting with the specific word line was turned off to prevent short circuit situation of word line. Other SPST switches were turned on to impose equivalent voltage potential on unselected word line resistor terminals. For bit selection, each bit line has a bit line buffer to impose equivalent voltage potential between unselected bit line resistor. Combining bit buffers and SPST switch array, unselected resistors would maintain same voltage potential at its two terminals to prevent sneaky current path. The selected resistor would form voltage divider circuit with resistor divider and reference voltage, and the measurement voltage was further scaled down by non-inverting amplifier to fit in the allowable voltage measurement range of the analogue-to-digital converter. The charging unit was designed with a switching mode buck-boost converter to charge up a single cell lithium-ion battery with reverse polarity, over voltage, over current, and over heat protection. The charging unit circuit and the integrated ring board were further encapsulated into the 3D-printed charging pod and ring case with pogo pin connectors (2 pins) to fit with, enhancing mechanical adhesion and ensuring comfortable wearability (supplementary fig. 37).

AI health agent demonstration with the AI-wearable biometric ring
The olfactory sensor chip was bonded on MEMS chip carrier and connected to AI-wearable biometric ring for signal processing. The classified result was updated on the screen of an APP with Azure OpenAI API Service (gpt-4o model) through HKUST API developer portal based on a self-built application.

Ethics and On-body participant recruitment
Every experiment involving healthy human participants were carried out following the human research ethic protocols (HREP-2024-0229) approved by the Human and Artefacts Research Ethics Committee at the Hong Kong University of Science and Technology (HKUST). Participants (supplementary table 4) were recruited from the HKUST campus through oral advertisement. All participants were given informed consents before participation in the study. The study was fully voluntary, and no compensation was given.

On-body participant experiment flow on dietary intakes and physical activities
Each participant was asked to consume alcohol (200 mL with 20% alcoholic percentage), fruit (200 mL pure orange juice without add sugar), meat (100 g chicken breast), carbs (100 g rice), nuts (20 g), and vegetables (20 g lettuce). The dinning time of each participant was between 5 min. and 10 min.. It was confirmed that all participants did not have allergy to the food/beverage intake of this study. During the experiment, each participant was wearing the ring with wireless data collection on one hand and the sampling of the skin VOC on the other hand was conducted simultaneously. The sampling time of the skin VOC from hand was fixed to be 20 min.. Each participant was instructed to wear the ring and maintain a stationary position for a duration of 10 min. prior to engaging in a running exercise. This initial period was designed to allow for the stabilization of physiological parameters. Subsequently, each individual was required to run on a treadmill for 30 min. at a specified speed of 8 km/hour, ensuring a consistent exercise intensity throughout the session. Following the running activity, participants were directed to cease exercise and rest for an additional 30 min.. Wireless data transmission was conducted during the whole experiment.

Model specification for multiple gas classification
The classification and multidimensional data space reduction models for gas classification were built using Python (v.3.9) based on the data collected from the olfactory sensor chip. All the individual sensor pixels were adopted to extract sensing response as the feature construction for the input dataset. We adopted LDA to compute eigenvectors and select discriminant features as two-dimensional format for input dataset construction. We employed KNN for gas discrimination by decision spaces generation for automatic unknown gas classification. The decision space for each gas is represented by one color and determined by the calculation of the Euclidean distance metric based on the majority class of k-nearest neighbors. Here, to achieve better classification accuracy, the k hyperparameter is set to be 46.

Model specification for multiple single gas component regression
The AA-sLSTM neural network was deployed on a local computer with an individual graphic processing unit module Nvidia GTX 3070 Ti. The entire dataset was constructed on the three sets of repeated concentration gradient measurements under four humidity conditions from seven gas analytes selected with extraction of 400 response data point from each sensor pixel. The input data was reshaped into three rows and three columns for further axial-attention processing. Then the processed data was regrouped into cascading time series data based on the look back window size. The LSTM fed into the processed data and generated hidden states, where it would undergo the hyperbolic tangent activation function to produce non-linearity. Finally, the transformed hidden state was passed through an output layer to generate predictions, where the first element along the last dimension of the predictions was extracted to produce the final output.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "From a technological perspective, the device described in this study is both innovative and well-detailed, showcasing a\nsignificant level of engineering sophistication.\nHowever, several aspects of the experimental setup require further clarification and refinement to enhance the scientific rigor\nand reliability of the results.\n1. Calibration Procedure:\nThe calibration of each volatile organic compound (VOC) should be performed using a randomized concentration order,\nrather than in a strictly decreasing sequence. This is essential to prevent potential biases that could arise from systematic\npatterns in the experimental methodology. Randomization ensures that any observed effects are more likely to reflect true\nsensor responses rather than artifacts of the testing protocol.\n2. VOC Mixtures:\nThe current study appears to lack measurements involving mixtures of the tested VOCs. This represents a significant\nlimitation, as real-world conditions typically involve complex mixtures rather than isolated compounds. For the results to be\napplicable to practical scenarios—such as those involving skin emissions, or environmental exposures—testing with VOC\nmixtures is essential. Incorporating mixture measurements would provide a more realistic assessment of the device's\nperformance and selectivity.\n3. Human Subject Testing – Limitations:\nThe experimental design for tests involving healthy subjects presents two major concerns:\n• Sample Size: The study involves only three participants, which is insufficient given the complexity introduced by evaluating\nsix dietary conditions and three physical activity levels. This results in a highly multifactorial scenario that cannot yield\nstatistically meaningful or generalizable insights with such a small sample size. A larger and more diverse participant pool is\nnecessary to draw robust conclusions.\n• Testing Locations on the Human Body: Although the use of a wearable ring provides a non-invasive and user-friendly\napproach, it is important to compare results obtained from the ring with VOC measurements from other areas of the skin. This\nwould help determine whether the ring's placement captures representative emissions and how location affects sensor\nreadings.\n4. Selection and Selectivity of VOCs and Sensing Materials:\nThe rationale behind the selection of specific VOCs for detection should be clearly articulated. Moreover, a thorough\ndiscussion is needed regarding the selectivity of the sensing materials—not only in relation to the target VOCs but also in\ncomparison to other potentially relevant compounds associated with dietary intake and physical activity. Without this\nanalysis, it is difficult to assess the reliability and specificity of the device under realistic conditions.", "reviewer_2": "This manuscript by “Miniaturized Multi-modal Olfactory Sensor Chip-based AI-wearable\nBiometric Ring for Human Body Metabolic Odor Analysis” by Wenhao Ye et al. presents the development of a miniaturized, AI-integrated wearable olfactory\nsensor ring for non-invasive, real-time monitoring of metabolic VOCs emitted from the human body. The authors address the\nimportant yet underexplored challenge of decoding physiologically relevant dermal VOC signals. The highlight of the work\nlies in the monolithic integration of a 3D vertical heterointerface-based olfactory sensor chip with an attention-based multi-\nmodal feature fusion algorithm for pattern recognition. The system demonstrates high accuracy in distinguishing diet-related\nVOC profiles (98.20%), physical activity states (99.12%), and quantifying food intake. This wearable platform holds promise\nfor next-generation personalized health monitoring through digital olfaction. While the manuscript is technically sound and\nthe system architecture is compelling, the novelty relative to previously reported work, specifically “Biomimetic olfactory\nchips based on large-scale monolithically integrated nanotube sensor arrays” (Wang et al., Nat. Electron., 2024), should be\nclearly articulated and differentiated. Moreover, several technical and scientific concerns must be addressed prior to further\nacceptance.\nMajor concerns:\n1.The manuscript states that SnO2 forms heterogeneous interfaces with various metal oxides and is modified with Pd atoms\nor nanoparticles. Please provide experimental evidence clarifying whether Pd exists as interfacial embedding, surface\ndecoration, or single atoms. The associated sensing mechanisms should also be discussed.\n2.Please provide scientific reasoning for the selection of the six metal oxides (ZnO, TiO2, WO3, In2O3, NiO, Cr2O3). Are\nthese based on electronic properties, catalytic activity, or prior empirical success in VOC sensing?\n3.Clarify the correlation between sensor units 1–9 and their specific sensing targets. A schematic or table mapping unit\nstructure to gas response would be helpful.\n4.The observed decay in the response signal after VOC exposure suggests potential poisoning or irreversible reactions.\nPlease explain this behavior with supporting data.\n5.VOCs resulting from food metabolism typically require a multi-step physiological process before being emitted via the skin.\nGiven that the fingers are not sebum-rich regions, is the sampling time sufficient to capture such changes? Please support\nthis with kinetic data or time-series measurements.\n6.Exercise significantly alters skin temperature, humidity, and ion concentration. Were these parameters independently\ncontrolled and tested? Please provide data showing how the sensor distinguishes VOC concentration changes from\nphysical condition fluctuations.\n7.Given inter-individual and dietary variability, the study should include statistically significant comparisons across multiple\nfood types and subject groups. Please confirm reproducibility of sensor responses in different individuals.\n8.Several gases tested (e.g., toluene, isobutene) are typically linked to environmental exposure rather than endogenous\nhuman metabolism. Please provide literature references or GC-MS data confirming their presence in human dermal\nemissions.\n9.Commonly detected skin-emitted compounds (e.g., short-chain fatty acids, aldehydes, thiols, amines) were not included.\nPlease explain the exclusion of these biologically relevant targets. Is this due to material selectivity or oversight?\n10.Assess the cross-responses of the sensor array to non-target gases and interfering substances such as lactic acid,\npyruvic acid, water vapor, or electrolytes. Data supporting specificity under real skin conditions are essential."}, "author_response": "Response: We would like to thank the reviewer for the positive comment on our olfactory\nsensor chip and the AI-wearable ring platform. Such productive feedback from the experts in\nthis field helped us to further improve our manuscript. Here we provide a point-by-point\nresponse below to the reviewers’ questions. We also carefully revised our manuscript to comply\nwith the reviewers’ comments and have highlighted the reviewer comments and the revised\nmanuscript in red. The supplementary figure caption numbers are arranged according to\nsupplementary file, while the revised figures are arranged within the response letter.\n1.Calibration Procedure:\nThe calibration of each volatile organic compound (VOC) should be performed using a\nrandomized concentration order, rather than in a strictly decreasing sequence. This is essential\nto prevent potential biases that could arise from systematic patterns in the experimental\nmethodology. Randomization ensures that any observed effects are more likely to reflect true\nsensor responses rather than artifacts of the testing protocol.\nResponse: We would like to thank the reviewer for the constructive suggestion. According to\nthe requirement from the reviewer, we have tested the device of all the gases in a randomized\nconcentration order: 400-1000-800-80-200-40-100-20 ppb instead of the sequential\nconcentration that we have previously tested in the manuscript (1000-800-400-200-100-80-40-\n20 ppb). As shown in the revised figure R1-7, the olfactory sensor chip was tested towards 7\ntypes of gases under 4 types of relative humidity atmosphere. As shown in supplementary\nfigure 24a-d, we further compared and visualized the olfactory chip result distribution in\nrandomized concentration order with the results in sequential order under 4 types of humidity\natmosphere in terms of group box plot, indicating the sensor responses are similar across these\ntwo testing protocols. To better quantify the average squared difference between two testing\nprotocol measurement results, we adopted the minimum square error (MSE) to serve as a robust\nmetric to assess the similarity between 2 sets of response across 4 humidity atmospheres. As\nshown in supplementary figure 24e-h, results show that all MSE values are consistently\nbelow 2.5. These results indicate that the sensor responses are nearly identical regardless of the\ntesting methodology, supporting the conclusion that the observed responses reflect the true\nsensor response rather than artifacts introduced by the pre-defined order of gas concentration\nmeasurement. The potential bias can be prevented via randomized concentration order\nconfiguration on gas sensing measurement. It was revised in the manuscript marked with red\n(page 8): “: To further reflect reduce the measurement bias caused by the pre-defined sequential\ngas concentration order, the olfactory sensor chip is tested under 7 gas species in a randomized\norder (400-1000-800-80-200-40-100-20 ppb) under 4 different humidity atmospheres. The\nsensing response under both sequential and randomized concentration order is compared and\nvisualized in group box plot, indicating the sensor responses are similar across these two testing\nprotocols (Supplementary fig. 24a-d). To better assess the similarity between two testing\nprotocol measurement results, we adopted the minimum square error (MSE) to serve as a robust\nmetric between 2 sets of the sensing response across 4 humidity atmospheres. The results show\nthat all MSE values are below 2.5, which indicates that the sensor responses are nearly identical\nregardless of the testing methodology (Supplementary fig. 24e-h).”.\n2. VOC Mixtures:\nThe current study appears to lack measurements involving mixtures of the tested VOCs. This\nrepresents a significant limitation, as real-world conditions typically involve complex mixtures\nrather than isolated compounds. For the results to be applicable to practical scenarios—such\nas those involving skin emissions, or environmental exposures—testing with VOC mixtures is\nessential. Incorporating mixture measurements would provide a more realistic assessment of\nthe device's performance and selectivity.\nResponse: We would like to thank the reviewer for the constructive suggestion for the VOC\nmixture study. To explore the VOC mixture analysis, we selected four representative skin-\nemitted VOCs encompassing various key functional group classes - acetone (ketone),\nformaldehyde (aldehyde), ethanol, and isopropanol (alcohol), along with toluene as a common\nenvironmental pollutant. As shown in supplementary figure 32a-b, we mixed 10 sets of VOC\nmixtures and each mixture contains 9 concentration gradient groups (each gas component\nwithin the mixture group has 3 concentration gradients: 800, 400, and 100 ppb). The dataset\nincludes 90 kinds of mixtures in total. The response patterns of all mixture groups were\nmeasured and visualized in radar plots. To further examine the mixture classification feasibility\nof the olfactory sensor chip, the responses were extracted as input features and analysed by 2D\nLDA. Simultaneously, the responses of the single gas component were extracted and analysed\nby 2D LDA for the differentiation with the gas mixture. Supplementary figure 32c shows the\nacetone - based mixtures and single gas component sample projection along LDA 1 and LDA\n2 direction. It is noticed that the mixture samples are directionally located between single gas\ncomponent samples within the mixture (with red arrow labelled). Similarly, supplementary\nfigure 32d-f shows the ethanol-based, formaldehyde-based, and isopropanol-based mixtures\nare directionally located between single gas component samples within the mixture. To verify\nthe mixture classification capability of the olfactory sensor chip, we applied KNN for\nclassification on both the mixture and single gas. As shown in supplementary figure 32g, the\npredicted and true labels are in the row and the column of the confusion matrix. The confusion\nmatrix shows the classification result of the tested samples with minor wrong predicted samples,\nwhich shows good classification capability on mixture and single gas component. To further\ncompare different classifier performance on the mixture classification, we trained 4 classifier\n(KNN, SVM, LR, and DT) and tested to obtain the receiver operating characteristic curve. As\nshown in supplementary figure 32h, KNN has the highest area under curve (AUC) value of\n0.985 compared to SVM, LR, and DT, which indicates the KNN ability in leveraging gas\nresponse patterns for better discrimination between VOC gas mixture classes. The quantitative\nprediction of the mixture gas component concentration is shown in supplementary figure 33a\n- t, where the mixed gas concentration can be individually predicted. To evaluate the error of\nthe regression, mean square error is selected as the evaluating metric and the definition is shown\nas follows:\n𝐸𝑟𝑟𝑜𝑟 = 𝑦 −𝑦 (1)\n𝑝𝑟𝑒𝑑𝑖𝑐𝑡 𝑡𝑟𝑢𝑒\n2 1\n𝑀𝑆𝐸 = √ ∗(‖𝐸𝑟𝑟𝑜𝑟‖)2 (2)\n𝑛\nwhere 𝑦 denotes the predicted gas concentration. 𝑦 denotes the true gas\n𝑝𝑟𝑒𝑑𝑖𝑐𝑡 𝑡𝑟𝑢𝑒\nconcentration. n denotes the total number of the testing samples. The results of acetone-based\ngas component concentration regression are shown in supplementary figure 33a-h. The\nresults of ethanol-based gas component concentration regression are shown in supplementary\nfigure 33i-n. The results of formaldehyde-based gas component concentration regression are\nshown in supplementary figure 33o-r. The results of isopropanol-based gas component\nconcentration regression are shown in supplementary figure 33s-t. Overall, it can be\nconcluded that involving multi-component gas mixtures from both skin-emitted and\nenvironment-exposed VOCs approaches the olfactory sensor chip towards the applicable\nscenario by predicting the mixture class and gas component concentration respectively. It was\nrevised in the manuscript marked with red (page 10): manuscript: “To further evaluate the\nperformance of the olfactory sensor chip under the practical scenarios of the VOCs complex\nmixtures in terms of various concentration and gas species rather than a single gas component,\nwe performed a quantitative analysis of the VOC mixtures to demonstrate the olfactory sensor\nchip capability of identifying the gas mixture composition. We construct the dataset of every\ntwo of five gases (acetone, ethanol, formaldehyde, isopropanol, and toluene) of 10 categorical\nVOC mixtures. Each categorical VOC mixture includes 9 concentration gradient groups (800,\n400, and 100 ppb per gas component), producing 90 kinds of mixture dataset. Radar plots\nvisualize sensor response patterns of each categorical VOC mixture (Supplementary fig. 32a-\nb). To assess the mixture classification feasibility, we performed 2D LDA with extracted\nresponses as input features and compared mixture and single-gas response projection. It is\nnoticed that acetone-, ethanol-, formaldehyde-, and isopropanol-based mixture samples are\ndirectionally located between corresponding single gas component samples within the mixture\n(with red arrow labelled, Supplementary fig. 32c-e). The confusion matrix reveals high\naccuracy and minimal misclassifications for mixtures and single gases (Supplementary fig. 32f).\nFour classifiers (KNN, Supporting Vector Machine (SVM), Logistic Regression (LR), and\nDecision Tree (DT)) undergo comparison via receiver operating characteristic (ROC) curves,\nwhere KNN achieves the highest area under curve value of 0.985 (Supplementary Fig. 32g).\nWe further performed quantitative regression analysis of individual gas component\nconcentrations within mixtures. Predicted concentrations match actual values with relatively\nsmall MSE (Supplementary fig. 33a-t). This outcome confirms the capability of the olfactory\nsensor chip to classify individual gas species and predict individual gas concentrations within\ncomplex mixtures.”.\n3. Human Subject Testing – Limitations:\nThe experimental design for tests involving healthy subjects presents two major concerns:\nSample Size: The study involves only three participants, which is insufficient given the\ncomplexity introduced by evaluating six dietary conditions and three physical activity levels.\nThis results in a highly multifactorial scenario that cannot yield statistically meaningful or\ngeneralizable insights with such a small sample size. A larger and more diverse participant\npool is necessary to draw robust conclusions.\nResponse: Thanks for the reviewer’s constructive comment. To address the concern regarding\nthe small sample size and the multifactorial nature of the experimental design, we expanded\nthe study by including three additional female participants, as shown in the supplementary\ntable 4. The participants include three males and three females, which increases the sample\nsize and enhances the diversity of the participant pool by incorporating gender variation. As\nshown in supplementary figure 40-42, additional dietary statuses assessment monitoring was\nperformed on three female participants with alcoholic, fruit, meat, carbohydrate, nut, and\nvegetable intake. Such measurement could support statistically meaningful conclusion across\nmore diverse conditions in the larger participant pool. It was revised in the manuscript marked\nwith red (page 12): “The evaluation of the olfactory sensor chip for six human dietary intakes\nand three physical activity statuses is conducted on six healthy participants (Supplementary\ntable 4), including three males and three females to enhance the diversity of the participant\npool.”\nTesting Locations on the Human Body: Although the use of a wearable ring provides a non-\ninvasive and user-friendly approach, it is important to compare results obtained from the ring\nwith VOC measurements from other areas of the skin. This would help determine whether the\nring's placement captures representative emissions and how location affects sensor readings.\nResponse: We would like to thank the reviewer for the constructive comments on the testing\nlocation of the human body. To address the reviewer's comment about comparing VOC\nmeasurements from other skin areas, we conducted additional experiments to evaluate the\nsensor responses across different body location. As shown in revised figure 8a-d, a subject\nworn the device at three representative skin areas with close-up photographs: forearm, dorsal\nhand, and anterior thigh. A flatten flexible mainboard is prepared for VOC measurement to\nensure tight contact with the selected human body location. We tested the olfactory sensor chip\nresponse at these sites following fruit intake in the healthy subject. As shown in revised figure\n8e, our measurement results demonstrate that the wearable ring platform successfully detects\nVOC emissions across multiple skin locations, indicating that the ring can capture\nrepresentative VOC emissions regardless of its placement on the body. In the box plot, our\nexperimental results indicate that the skin VOC responses from the finger and the back of the\nhand are similar, with the forearm showing a slightly higher response, and the anterior thigh\nexhibiting the most pronounced response. Such result aligns with the previous studies which\ncan be attributed to variations in gland distribution (eccrine, apocrine, and sebaceous glands),\nskin physiological properties (thickness, moisture, and area), and microbial composition1-3.\nDespite the location-dependent variations on the sensor response, our data indicates that when\nthe ring is fixed at a specific location (here we choose the finger), the collected VOC signals\nexhibit a consistent and distinguishable correlation with various dietary intakes and physical\nactivities. The ring platform can reliably capture the representative skin VOC emission patterns\nat the finger that are indicative of physiological conditions for continuous monitoring.\n4. Selection and Selectivity of VOCs and Sensing Materials:\nThe rationale behind the selection of specific VOCs for detection should be clearly articulated.\nMoreover, a thorough discussion is needed regarding the selectivity of the sensing materials—\nnot only in relation to the target VOCs but also in comparison to other potentially relevant\ncompounds associated with dietary intake and physical activity. Without this analysis, it is\ndifficult to assess the reliability and specificity of the device under realistic conditions.\nResponse: We would like to thank the reviewer for the comment on the selection of VOCs for\nmeasurement and selectivity of the sensing material. The VOCs selected for detection in this\nstudy - acetone, formaldehyde, ethanol, isopropanol, ammonia, isobutylene, and toluene - were\nchosen strategically to represent a range of functional groups commonly associated with human\nmetabolic processes following dietary intake, as well as potential environmental interferents31-\n33. Here, acetone, formaldehyde, ethanol, isopropanol, and ammonia serve as representative\nbiomarkers for ketones (acetone), aldehydes (formaldehyde), alcohols (ethanol and\nisopropanol), and nitrogenous gas (ammonia), which are endogenously produced through\nvarious metabolic pathways. These VOCs are endogenously emitted through skin, reflecting\nmetabolic states influenced by diet and activity4. By targeting these VOCs, our sensor chip\naims to capture physiologically relevant signals for non-invasive monitoring. In contrast,\nisobutylene and toluene were considered to evaluate the sensor chip potential for detecting\nexogenous environmental interferents, such as those from indoor or outdoor trace level toxic\ngases5. The exogenous environmental VOCs monitoring extends the sensor chip applicability\nto real-world scenarios. As detailed in our response to comment 2, we conducted systematic\ntests involving mixtures of skin-emitted VOCs and the environmentally exposed VOC.\nAnalysis of these results allowed us to examine the olfactory sensor chip capability for\nclassifying complex mixtures, enhancing its practicality under realistic conditions with\npotential interferences.\nTo thoroughly assess the selectivity of the sensing materials, we would like to introduce the\nrationale of the gas sensing material selection in the first place. Our approach begins with the\ngas-sensitive metal oxide semiconductors. Although the metal oxide gas sensing materials\nexhibit broad sensitivity, they possess high sensitivity, and can be integrated with CMOS\nprocesses to fabricate miniaturized devices, making them highly suitable for the highly\nintegrated systems in wearable applications6-7. We selected the typical Pd@SnO₂ as the\nbackbone sensitive material due to its high response characteristics for VOC detection8.\nAdditionally, to utilize the broad sensitivity characteristic, we further adopted a hetero-interface\ndevice structure design by selecting the heterogeneous oxide semiconductor materials with\nvarying band structures (here we selected ZnO, TiO , WO₃, Cr O , NiO, and In O ), enabling\n2 2 3 2 3\nthe distinct interface energy barrier at the equilibrium state. The energy barrier height\nmodulation would happen in response to the VOC analyte adsorption/desorption state and the\nelectron transfer would take place, which results in the distinct output electrical signals.\nTherefore, we constructed unique response patterns for different target gases based on the\nresponse differentiation among the chip units and combined them with AI algorithms to realize\nthe selectivity in terms of the machine learning methodology9. We evaluated the responses of\nthe nine sensor units on the chip to seven VOCs (acetone, formaldehyde, ethanol, isopropanol,\nammonia, isobutylene, and toluene) at 200 ppb, as illustrated in the revised figure 9. The data\nindicates the distinct patterns of each VOC across the units of entire sensor chip. These\ndistinguishable responses enable the construction of unique \"odour fingerprints\" for each VOC,\nwhich can be leveraged by integrated AI algorithms to achieve high-accuracy classification and\nregression.\nBeyond the target VOCs, we considered the potential influence of other organic compounds\nassociated with dietary intake and physical activity regarding the selectivity of the sensing\nmaterial. For instance, pyruvic acid, also known as pyruvate, is primarily generated in the\nhuman body through glycolysis, a cytoplasmic metabolic pathway where glucose is broken\ndown into the molecules of pyruvate per glucose molecule. Pyruvate serves as a key\nintermediate that can enter the mitochondrial tricarboxylic acid (TCA) cycle for further energy\nproduction or be converted to other metabolites or directly excreted through eccrine sweat10-11.\nLactic acid is produced from pyruvate via reduction by lactate dehydrogenase (LDH), which\nuses NADH to regenerate NAD+ and sustain glycolysis12. Lactic acid is excreted through\neccrine sweat glands, where it originates from local glycolytic metabolism within the sweat\ngland cells rather than blood filtration during the intensive exercise13.\nTo address this, we performed simulated tests under real skin conditions by applying solutions\ncontaining 25 mM lactic acid, and 1mM pyruvic acid in the artificial sweat solution to mimic\nsweat composition, followed by exposure to the target VOCs (taking formaldehyde as the\nrepresentative) at 200 ppb. As shown in revised figure 10a-d, the measurement result\ndemonstrates negligible changes in the sensor responses to the VOCs in the presence of these\ncompounds. This indicates that our sensing materials maintain high selectivity toward the\ngaseous VOCs, with minimal cross-interference from non-volatile liquid-phase organic\ncompound serving as metabolic intermediates. Consequently, the device exhibits robust\nreliability and specificity under realistic physiological conditions, supporting its potential for\npractical applications in personalized digital olfaction towards health monitoring.\nReviewer #2 (Remarks to the Author):\nThis manuscript by “Miniaturized Multi-modal Olfactory Sensor Chip-based AI-wearable\nBiometric Ring for Human Body Metabolic Odor Analysis” by Wenhao Ye et al. presents the\ndevelopment of a miniaturized, AI-integrated wearable olfactory sensor ring for non-invasive,\nreal-time monitoring of metabolic VOCs emitted from the human body. The authors address\nthe important yet underexplored challenge of decoding physiologically relevant dermal VOC\nsignals. The highlight of the work lies in the monolithic integration of a 3D vertical\nheterointerface-based olfactory sensor chip with an attention-based multi-modal feature fusion\nalgorithm for pattern recognition. The system demonstrates high accuracy in distinguishing\ndiet-related VOC profiles (98.20%), physical activity states (99.12%), and quantifying food\nintake. This wearable platform holds promise for next-generation personalized health\nmonitoring through digital olfaction. While the manuscript is technically sound and the system\narchitecture is compelling, the novelty relative to previously reported work, specifically\n“Biomimetic olfactory chips based on large-scale monolithically integrated nanotube sensor\narrays” (Wang et al., Nat. Electron., 2024), should be clearly articulated and differentiated.\nMoreover, several technical and scientific concerns must be addressed prior to further\nacceptance.\nResponse: We would like to express our sincere gratitude to the reviewer for the positive\ncomments of our manuscript. We are thankful for the insightful feedback and suggestions,\nwhich has strengthened the manuscript and enhanced its contribution to the field. Regarding\nthe novelty comparison to the previously reported work “Biomimetic olfactory chips based on\nlarge-scale monolithically integrated nanotube sensor arrays” which was published by our\ngroup, this work is differentiated in 3 perspectives: From the perspective of application\nscenarios, the biomimetic olfactory chip (BOC) mimics diverse olfactory receptors to generate\nmassive types of sensors and target versatile uses for general tasks. Demonstrated by\nclassification of 24 odours in daily life, the BOC integrates with vision sensors on a\nquadrupedal robot for dynamic odour discrimination. In contrast, our wearable olfactory sensor\nring focuses on personalized health monitoring via real-time analysis of metabolic VOCs from\nhuman skin to distinguish diet-related profiles (98.20% accuracy), physical activity states\n(99.12% accuracy), and quantify food intake, making it highly promising for the next\ngeneration of digital olfaction in health monitoring applications. From the perspective of the\nencapsulation and miniaturization for the wearable integration, the 100*100 pixelized BOC\npackaging on C-PGA chip (50 mm by 50 mm) with massive electrode pads areas are not\ndesigned and optimized for wearable applications. Our ring, however, employs a fully\nminiaturized ring-form factor with ultra-small olfactory sensor chip size for packaging (3 mm\nby 3mm, which is ~278 times smaller in packaging size compared to BOC), ensuring the\ncomfort and ring form factor system design for the trendy ring-form factor based\nwearable applications. From the perspective of the material and the device architecture design,\nBOC features large-scale monolithic integration of 10,000 individually addressable vertical\nmetal oxide nanotube sensors on nanoporous alumina substrates, using suspended mask-\nassisted sputtering to create a 2D material composition gradient on the top surface, mimicking\nthe biological receptor diversity for ppb-level sensitivity and achieving gas classification via\nconvolutional neural networks. Our ring, on the other hand, introduces a 3D vertical\nheterointerface-based olfactory sensor chip with top and bottom oxide hetero-interface\nintegration paired with an attention-based multi-modal feature fusion algorithm, enabling\nprecise recognition of complex human metabolic VOC signals and addressing the challenge of\ndecoding human metabolic odours with high sensitivity and robustness.\nMajor concerns:\n1. The manuscript states that SnO forms heterogeneous interfaces with various metal oxides\n2\nand is modified with Pd atoms or nanoparticles. Please provide experimental evidence\nclarifying whether Pd exists as interfacial embedding, surface decoration, or single atoms. The\nassociated sensing mechanisms should also be discussed.\nResponse: We would like to thank the reviewer for the insightful comment on Pd existence\nclarification. Regarding the Pd existence, we have done TEM characterization of Pd@SnO to\n2\nshow Pd distribution on the side wall of the nano-honeycomb membrane, confirming the Pd\nnanoparticle successful surface decoration with the peak of (111) (supplementary fig. 3a-c).\nHowever, we notice that it is vague to clarify the Pd existence with a wider scope of the device.\nTo further clarify the Pd existence, we fabricated and characterized SnO -ZnO and SnO -Cr O\n2 2 2 3\nhetero-interfaces as representatives. As shown in supplementary figure 4a-b, the cross-\nsection EDS of the hetero-interfaces clearly shows that Pd majorly exists and distributes along\nthe side wall of the nano honeycomb membrane while the ZnO and Cr O are deposited on the\n2 3\nsurface. Regarding the Pd sensing mechanism, it can be contributed to chemical and electronic\nsensitization. Chemical sensitization is the process of facilitating the conversion of the target\ngas molecule into the intermediate product through the catalytic behaviour, which enhances the\nsensitivity through additional interaction between the gas molecule and the gas sensing material.\nThe spillover effect is a fundamental mechanism behind the chemical sensitization. In the\nexpanded mechanism of the adsorbed O species on the Pd atom surface, O traps electron from\n2 2\nthe Pd atom to form negative ion species such as O -, O-, and O2-. Such ion species further\n2\nmigrate to the gas sensing material surface, increase the active sites, and enhance the interaction\nwith the target gas molecule, contributing to the boosted sensitivity14. Taking the formaldehyde\nsensing as an example, the Pd decorated on the surface of the metal oxide gas sensing material\naccelerates the conversion of the adsorbed O species into the negative charge ions catalytically,\n2\nenhancing the concentration of the active sites. The increased active sites of negative oxygen\nions increase the depletion layer thickness, resulting in the higher baseline resistance in the air,\nlarger modulation of the resistance with the adsorbed formaldehyde molecule\nadsorption/desorption, and enhanced gas sensitivity. The process is described as follows15:\n𝐻𝐶𝐻𝑂(𝑔𝑎𝑠) → 𝐻𝐶𝐻𝑂(𝑎𝑑𝑠) (3)\n𝐻𝐶𝐻𝑂(𝑎𝑑𝑠)+2𝑂(𝑎𝑑𝑠)2− → 𝐶𝑂 + 𝐻 𝑂+4𝑒− (4)\n2 2\nElectronic sensitization in gas sensing refers to adjusting the electronic properties of the\nsemiconductor materials, such as carrier mobility, recombination rate, and carrier concentration,\nby incorporating noble metals. Such changes modify the electron behaviour during interactions\nbetween the target gases and the gas sensing materials, thus improving the gas sensing\nsensitivity in terms of electrical signal amplification16. The working principle behind the\nelectronic sensitization is the schottky junction formation between the noble metal and the n-\ntype semiconductor contact. In detail, the higher work function of the noble metal compared to\nthe gas sensing semiconductor material leads to the electron transfer from the semiconductor\nside to the noble metal side, forming the electron depletion region at the interface. Such\ndepletion region increases the baseline resistance in the air, induces substantial modulation of\nthe schottky barrier height and depletion width when the reducing gas molecule adsorption\nprocess is happening and donating the electron back to the depletion region. The final electrical\nsignal shows magnificent modulation as well14. Pd has a larger work function of 5.6 eV\ncompared to SnO 4.49 - 4.90 eV. Pd-SnO contact forms schottky junction and alters the\n2 2\nresistance significantly when the electron transfers from the adsorbed gas molecule to the\ndepletion region of the schottky junction, which result in the higher sensitivity compared to\nnon-noble metal decorated gas sensing material17. In fact, chemical and electronic sensitization\nof Pd work synergistically to improve the gas sensing performance. Pd can not only accelerate\nthe spillover effect of the adsorbed oxygen species to migrate the substantial amount of the\nnegative oxygen ions, but it can also induce the electron transfer to form the electron depletion\nregion and alter the electron concentration of the gas sensing material significantly when the\ngas molecule adsorption/desorption takes place. These all contribute to the response\nenhancement of the gas sensing applications18.\nIt was revised in the manuscript marked with red (page 6): “The cross-section EDS of the top\nand bottom hetero-interface representatives (SnO -ZnO, and SnO -Cr O ) clearly shows that\n2 2 2 3\nPd majorly exists and distributes along the side wall of the nano honeycomb membrane while\nthe ZnO and Cr O are deposited on the surface (Supplementary fig. 4a-b).”\n2 3\n2. Please provide scientific reasoning for the selection of the six metal oxides (ZnO, TiO , WO ,\n2 3\nIn O , NiO, Cr O ). Are these based on electronic properties, catalytic activity, or prior\n2 3 2 3\nempirical success in VOC sensing?\nResponse: We would like to thank the reviewer for the comment on the reason for selecting the\nsix oxides (ZnO, TiO , WO , In O , NiO, and Cr O ) in the olfactory sensor chip. The selection\n2 3 2 3 2 3\ncriteria require the material to be a gas sensing material capable of constructing different\ninterface energy barriers to build unique sensing patterns for testing multiple target gases.\nAdditionally, tailoring interface energy barriers enables distinct electronic interactions at the\nhetero-interfaces, facilitating unique response signatures to differentiate various gases in\ncomplex mixtures. Regarding the material gas sensing property, these six gas sensing materials\n(ZnO, TiO₂, WO₃, In₂O₃, NiO, Cr₂O₃) have been widely used for VOC gas detection owing to\nchemo-resistive properties, cost-effectiveness, thermal stability, and ease of fabrication18.\nRegarding the tailor-made potential energy barrier at the hetero-interfaces, it was primarily\ndriven by their varying work functions (𝜙) and electron affinity to engineer distinct depletion\nregions with SnO , rather than catalytic activity or empirical precedents in the volatile organic\n2\ncompound (VOC) sensing.\nIn the oxide-oxide semiconductor heterojunctions, band alignment takes place upon the contact\nbetween two heterogeneous materials, resulting in Fermi level equalization and subsequent\nband bending at the interface, which forms a surface energy barrier. The work function is\ndefined as the fermi level position relative to the vacuum level, and its mismatch between two\nheterogeneous materials induces a space charge region under a certain depletion width,\nresulting in the surface energy barrier at the interface. The charge transfer from the redox\nreaction between the adsorbed/desorbed gas molecule and negative oxygen ion species\nmodulates the energy barrier height of the hetero-interfaces.\nWhen n-type SnO (𝜙 ≈ 4.49 eV)19-20 material forms hetero-interfaces with different oxide\n2\nmaterials including n-type ZnO (𝜙 ≈ 4.80 eV)21, TiO (𝜙 ≈ 5.20 eV)22-23, WO (𝜙 ≈ 5.70 eV)24,\n2 3\nIn O (𝜙 ≈ 4.30 eV)25, and p-type NiO (𝜙 ≈ 5.30 eV) 21, Cr O (𝜙 ≈ 4.50 eV)26, the interface\n2 3 2 3\nenergy barrier at the equilibrium state would be distinct (revised figure 11a-b). The energy\nbarrier height modulation would happen in response to the VOC analyte adsorption/desorption\nstate. Such band bending enhances the sensitivity (here define as the resistance ratio for\nsimplicity of explanation) to VOCs by amplifying resistance changes with the following\nequation27:\n𝑅 𝑞∆𝜙𝑏\n∝ exp ( ) (5)\n𝑅0 𝑘𝑇\nwhere R is the resistance under the target gas, R0 is the baseline resistance, ∆𝜙𝑏 is the barrier\nheight difference between the gas adsorption/desorption and the ambient air, k is the Boltzmann\nconstant, q is the charge, and T is temperature. Due to energy barrier height changing from\ncarrier depletion or accumulation on the hetero-interface when the gas adsorption/desorption\nhappening, this results in the unique resistance changes towards the same VOC target analyte,\nenabling the construction of the unique VOC sensing pattern. By pixelating the sensor chip\nwith these heterostructures, we achieve a multidimensional response matrix that facilitates\nselective discrimination among multiple VOC species through pattern recognition algorithms,\nenhancing the olfactory chip classification capability under practical applications with the aid\nof the AI algorithms. It was revised in the manuscript marked with red (page 5): “The various\noxide-oxide semiconductor interfaces could construct different interface energy barriers based\non their electronic property of work function, and electron affinity to build unique sensing\npatterns for testing multiple target gases. The energy barrier height modulation would happen\nin response to the VOC analyte adsorption/desorption state, which significantly enhances the\nsensing performance.”.\n3. Clarify the correlation between sensor units 1–9 and their specific sensing targets. A\nschematic or table mapping unit structure to gas response would be helpful.\nResponse: We would like to thank the reviewer for the suggestion on clarifying the olfactory\nchip sensor response with respect to the measured target gas species. We need to clarify that\nthe nine sensors in our array are not highly specific to a particular gas or certain gases. Our\ngoal is to have these nine sensors generate distinguishable signal outputs, which combines with\nthe AI algorithm to achieve classification. Here, we summarized the table mapping of the gas\nresponses between 7 target gases and sensor units 1-9 (Revised table 1).\n4.The observed decay in the response signal after VOC exposure suggests potential poisoning\nor irreversible reactions. Please explain this behavior with supporting data.\nResponse: We would like to thank the reviewer for the comment. Regarding the potential\npoisoning issue, we firstly observed and analysed the statistical result of the sensing response\nfrom the olfactory sensor chip. Taking the formaldehyde as the VOC representative, we\nconducted a 20-day test for 200 ppb formaldehyde measurement in dry air. As shown in revised\nfigure 12, the gas sensing response of the olfactory chip and the average gas response with the\nstandard deviation are calculated. According to the above results, there was only a small\nresponse decay with the standard deviation less than 0.3% in 20 days. The possible reason\nbehind this phenomenon could be noble metal catalyst poisoning with the VOC molecule\nduring the adsorption or the residual vapor bonding with the surface-active sites of the metal\noxide material during the ALD process. It is noted that typical poisons for noble metal catalysts\nare molecules containing elements from groups VA (P, As, Sb) and VIA (O, S, Se, Te). The\nadsorption of the typical poisons preferentially occupies Pd active sites, thereby reducing the\ncatalytic performance towards gas sensing28-29.\nDespite the observed decay, we further analysed the classification capability of the olfactory\nsensor chip of the measurement results of the seven target gas species respectively (revised\nfigure 13-15). We evaluated the classification result by using the day-0, day-8, and day-12\nresults. Although there was an evolution of the decision boundary due to the observed decay,\nit could be further compensated through the adapting adjustment of the hyperparameters of the\nKNN classifier, which allows the results to retain the sufficient sensitivity margin to accurately\ndistinguish VOC profiles. Even though this is out of the scope of the paper, it is still validating\nthe feasibility of our olfactory sensor chip for practical applications.\n5.VOCs resulting from food metabolism typically require a multi-step physiological process\nbefore being emitted via the skin. Given that the fingers are not sebum-rich regions, is the\nsampling time sufficient to capture such changes? Please support this with kinetic data or time-\nseries measurements.\nResponse: We would like to thank the reviewer for the comment on the signal capture during\nthe food metabolism of the subject. We agree that a clearer articulation of the temporal\ndynamics is essential to demonstrate the sufficiency of our sampling protocol. To address this\nconcern, we incorporated a detailed description of the signal capture process, provided an\nexplicit time-series data measurement with step-by-step breakdown of key time nodes below,\nand highlighted through the rectangles with multiple colours (revised figure 16). Taking the\nparticipant 01 as an example, the whole dietary assessment lasted two days. The participant\nwas required to conduct the fasting overnight on the day of the trial and further confirmed via\nself-report. The ring integrated with the olfactory sensor chip was worn on the participant 10\nminutes before the start of each food intake trial, allowing to create the baseline for further\nsignal processing of the sensor chip gas response. Since the baseline generation part is not the\nkey process for the application analysis, we did not introduce this signal capturing process to\nsimplify the analysis procedure. After the baseline generation, the ring would capture and set\nthe baseline value, following the official signal capturing from the beginning to the end of the\ndietary assessment. The participant started to consume the food with different categories:\nalcohol beverage, fruit, meat, carbohydrate, nut, and vegetable. The participant took different\ntime duration to finish different type of food, as highlighted in grey rectangles (alcohol intake\nduration: 200s, fruit intake duration: 200s, meat intake duration: 300s, carbohydrate intake\nduration: 250s, nut intake duration: 300s, vegetable intake duration: 300s). Then the ring kept\non measuring the skin-emitted gas response from the participant. After some time for digestion\nand systematic distribution via bloodstream (highlighted by the pink rectangles), the skin-\nemitted gas response signal start to become dominant and visualized compared to the initial\nstage. Here, we extracted the peak response signal (highlighted in sky-blue rectangle) to further\nperform AI algorithm for multiple food intake analysis. The highlighted key time nodes\ndemonstrate that our signal capture is sufficient to account for the multi-step metabolic\nprocesses signal capture. Additionally, the finger site area supports effective VOC capture with\nsufficient time capture, regardless of the concerns about low sebum levels. Combined with our\nsystematic measurement data from standard experiments, we can validate the reliability of\nthese measurements under realistic scenarios.\n6. Exercise significantly alters skin temperature, humidity, and ion concentration. Were these\nparameters independently controlled and tested? Please provide data showing how the\nsensor distinguishes VOC concentration changes from physical condition fluctuations.\nResponse: We would like to thank the reviewer for the comment on physical conditions\nfluctuation during the olfactory sensor chip measurement. Regarding the skin temperature\ncondition fluctuation, the normal range of skin temperature varies from 29.8 to 35.0 C30. Our\nolfactory sensor chip normally works under 180 C with the temperature control circuit\n(revised figure 17). The temperature control circuit calibrates the voltage-temperature curve\nof the heater and embeds the fitting formula into the MCU. Then, the digital-to-analogue\nconvertor equipped on the MCU can dynamically set various analogue voltage to adjust the\nheater temperature through the buck boost convertor. The temperature control circuit can\nachieve efficient and localized heating. Due to the 4-6 times higher operating temperature\ncompared to the skin temperature, such fluctuation has negligible impact on the heater’s\nperformance, ensuring stable and reliable operation of the olfactory sensor chip. Regarding the\nhumidity condition variation, we measured the gas sensing performance of the olfactory sensor\nchip under various humidity atmosphere independently (supplementary figure 10-23). The\nresponses were further extracted to evaluate both VOC distinguishable capability and\nregression analysis of the olfactory sensor chip. It is confirmed that the olfactory sensor chip\nis capable of distinguishing seven target gases and predicting the concentration under 4 types\nof humidity atmosphere variations (figure 3a-h, supplementary figure 26-31), showing the\ncapability of the olfactory sensor chip under humidity fluctuation. Regarding the ion\nconcentration, the olfactory sensor chip is designed to detect gaseous molecules rather than ion\nconcentrations in the liquid phase and all the experiments are tailored to this objective. To\naddress the concern, we conducted comparative tests using a measurement setup simulating the\npractical scenario of the skin condition with the sweat composition as the interference. We\nprepared for different solutions including the artificial sweat as the electrolyte (PBS with 10\nmM NaCl, 1 mM NH Cl, 10 mM KCl, 1 mM urea) to mimic the real skin conditions, as shown\n4\nin revised figure 19a-e. Indeed, the olfactory sensor chip specificity was evaluated under\nseveral ions in the solutions such as Na+, K+, Cl-, NH +. The measurement results can indicate\n4\nthe good specificity towards target gas molecules rather than the ions in the liquid with\nnegligible response reduction.\n7. Given inter-individual and dietary variability, the study should include statistically\nsignificant comparisons across multiple food types and subject groups. Please confirm\nreproducibility of sensor responses in different individuals.\nResponse: We would like to thank the reviewer for the comment of the reproducibility of the\nolfactory sensor chip across different individuals. To validate the sensor chip reproducibility of\nthe classification of various food intake across different individuals, as shown in\nsupplementary figure 43, we conducted principal component analysis (PCA) on the sensor\nchip response collected across the enlarged different individuals for multiple food types as the\npurpose of observing the cluster separation existence. For each individual, the PCA scatter plots\nrevealed clear separation of the data points into six distinct clusters, with each cluster\ncorresponding to a specific food type. The results demonstrated that the responses to the six\nfood types were distinctly separated into different clusters, indicating robust intra-individual\nreproducibility in metabolic odour profiles. Furthermore, we performed a merged analysis\nincorporating samples from all individuals for these six food types, employing linear\ndiscriminant analysis (LDA) combined with k-nearest neighbours (KNN) classification. As\nshown in supplementary figure 44, the result indicated clear classification of the overall\npatterns for different foods, confirming that the classification algorithm could reliably separate\nfood types across diverse individuals. This underscores the reproducibility of our olfactory\nsensor chip responses, as they enable clear discrimination of various foods under different\nindividual conditions despite variability. It was revised in the manuscript marked with red\n(page 13): “To validate the sensor chip reproducibility of the classification of various food\nintake across different individuals, we performed a merged analysis incorporating samples\nfrom all individuals for these six food types, employing linear discriminant analysis (LDA)\ncombined with k-nearest neighbours (KNN) classification. The result indicated clear\nclassification of the overall patterns for different foods, confirming that the classification\nalgorithm could reliably separate food types across diverse individuals (Supplementary fig. 43-\n44).”\n8. Several gases tested (e.g., toluene, isobutylene) are typically linked to environmental\nexposure rather than endogenous human metabolism. Please provide literature references or\nGC-MS data confirming their presence in human dermal emissions.\nResponse: We sincerely thank the reviewer for raising this important point regarding the\ninclusion of toluene and isobutylene, which are more commonly associated with environmental\nexposure than endogenous human metabolism. We acknowledge the need to clarify the\nrationale for testing these gases and provide supporting literature references for their relevance.\nHere, we address the reviewer’s concern by providing literature references to the necessity for\ndetection and explaining the purpose of including toluene and isobutylene in our study.\nToluene and isobutylene are prevalent volatile organic compounds (VOCs) in ambient air, often\noriginating from sources such as vehicle exhaust, industrial emissions, and household\nproducts31-32. These two VOCs could be present in trace amounts on human skin due to\nenvironmental exposure, as skin acts as a passive interface that can adsorb in the ambient\nenvironment. Previous studies have reported the presence of environmental hydrocarbons on\nskin surfaces due to prolonged exposure in the polluted atmosphere33-34.\nThe inclusion of toluene and isobutylene in the standard gas performance measurement of our\nstudy was primarily intended to construct a robust training and testing dataset for classification\nand concentration prediction tasks, rather than to imply their direct origin from human dermal\nemissions. As detailed in our response to reviewer 1 comment 2 and shown in\nsupplementary figure 32-33, we conducted systematic tests with mixtures of skin-emitted\nVOCs (ketone, alcohol, and aldehyde groups) and environmental interferents (toluene as a\nrepresentative). The preliminary mixture tests demonstrate that our sensor array can reliably\ndistinguish between skin-emitted and environmental VOCs, as evidenced by the high\nclassification accuracy and ROC results. By integrating the response patterns with AI-driven\nalgorithms, we achieved effective discrimination of target VOCs even in the presence of\nenvironmental interferents. This capability is essential for broadening the its capability to detect\nand differentiate endogenous VOCs among the environmentally relevant toxic gases that may\nact as interferents in the real-world scenarios.\n9. Commonly detected skin-emitted compounds (e.g., short-chain fatty acids, aldehydes, thiols,\namines) were not included. Please explain the exclusion of these biologically relevant targets.\nIs this due to material selectivity or oversight?\nResponse: We would like to thank the reviewer for raising this important point regarding the\nexclusion of certain biologically relevant organic compounds, including short-chain fatty acids,\naldehydes, thiols, and amines. To address this concern, we would like to clarify the rationale\nbehind the exclusion. Here, we outline the reasons for excluding the specific compounds and\nexplain point by point to show that the exclusion was driven by our study focus and technical\nconsiderations instead of material selectivity or oversight.\nShort-chain fatty acids (SCFA): Short-chain fatty acid (SCFA) refers to the saturated fatty\nacid with the chain length of 1-6 carbon atoms. The most abundant representatives of SCFA\nare acetate, propionate, and butyrate. SCFAs originates from intestinal microbial (particularly\nanaerobic fermentation) metabolizing dietary fibre and indigestible carbohydrates, which\nproduces SCFAs and releases them into the intestinal lumen. The majority of SCFAs are\nabsorbed from the intestinal cavity and further metabolized by colonocytes or liver. Butyrate,\nin particular, is used as a primary energy source by colonocytes via H+-dependent or sodium-\ndependent monocarboxylate transporters (MCT1). On the other hand, the remaining SCFAs\n(such as acetate and propionate) enter the liver via the portal vein, regulating lipid, glucose\nmetabolism, and inflammation. Hence, the majority of the absorbed SCFAs are directly\nmetabolized instead of being excreted through the skin35-37. Indeed, 95% of the produced\nSCFAs are absorbed by colonocytes or liver, while the rest are secreted in the feces, serving as\nthe indicator for developing non-invasive methods of gastrointestinal diseases diagnosis38-39.\nTherefore, skin-based detection of SCFAs is not biologically relevant to their production\nand distribution pathways.\nAldehyde: For aldehydes, they are indeed representative functional group gases secreted by\nthe skin and provide diagnostic information on endogenous metabolic processes40. We did\ninclude formaldehyde as a representative VOC with an aldehyde functional group in our testing\nprotocol. Formaldehyde is well-documented in relation to non-invasive disease diagnosis such\nas lung cancer41. Its inclusion was strategic to capture the aldehyde class while maintaining a\nmanageable set of target gases for robust sensor classification and regression. However, we\nfully agree that other aldehydes, such as acetaldehyde or propionaldehyde, could provide\nvaluable insights into human metabolic odor analysis42. We could expand our future research\nto include a broader panel of aldehydes, using a multi-aldehyde detection strategy to explore\ntheir collective contributions. This will build on our current findings and offer a more\ncomprehensive understanding.\nThiols: Biological thiols are the compounds that contains the -SH function group, which is the\nsulfur analogue to the alcohol group. The main source of the sulfur in the diet are sulfur-\ncontaining amino acids (mainly methionine, and cysteine) from the rich-sulfur dietary (such as\nonion, garlic, and dietary protein), which are further metabolized for biological thiols\ngeneration43. The ingested methionine can be metabolized into methanethiol (CH SH) by the\n3\nenzyme L-methionine 𝛾-lyase in the human tissue as well as by the anaerobic bacteria in the\nintestinal tract. Methanethiol may also be formed through the transamination of the methionine\ninto 3-methylthiopropionate. The enzyme thiol S-methyltransferase can also convert the\nhydrogen sulfide into methanethiol by the process of methylation44. Methanethiol is diffused\ninto the portal circulation via the bloodstream after intestinal absorption45. The excretion of the\nmethanethiol is primarily detected and analyzed through human flatus, urine, feces, and\nexhaled breath rather than skin emission, making the dermal emission negligible with respect\nto our olfactory sensor chip application44, 46.\nAmines: For amines, we mainly focus on volatile amines which includes ammonia,\ndimethylamine (DMA), and trimethylamine (TMA) due to the detection principle of our\nolfactory sensor chip47. In our study, we systematically tested for ammonia because it is a\nsignificant volatile amine produced through nitrogen metabolism, particularly via protein and\namino acid breakdown, and excreted through skin. However, we did not consider other volatile\namines such as dimethylamine (DMA), and trimethylamine (TMA) because they are not only\nprimarily associated with food spoilage indicators47-48, but also their metabolic and excretion\npathways merely involve skin secretion. Trimethylamine (TMA) is produced in the human gut\nthrough microbial metabolism of dietary amine precursors such as choline, L-carnitine. The\nmajor pathway in the formation of TMA involved gut bacteria (proteobacteria (in particular\ngamma- and delta-proteobacteria), firmicutes (mainly clostridia and some bacilli), and some\nactinobacteria) with key genes. The bacteria metabolize choline using enzymatic pathways\nwith the choline TMA-lyase (CutC) and its activator CutD to cleave choline into TMA and\nacetaldehyde, with CutD activating the glycine radical enzyme CutC for efficient TMA\nproduction49-50. Upon entering the bloodstream, TMA is typically rapidly oxidized in the liver\nby flavin-containing monooxygenase 3 (FMO3) into the odourless trimethylamine N-oxide\n(TMAO). Some researches confirm that the predominant excretion of TMA and TMAO\nfollowing dietary choline metabolism goes through urine. It also notes that, under normal\nconditions, TMA excretion through breath, skin, or feces is minimal51-52. Trace amounts of\nTMA and TMAO may be excreted via feces or breath, particularly when FMO3 function is\nimpaired53. We hope our response can clarify the exclusion of these four types of compounds\nin the present study and address your concerns.\n10. Assess the cross-responses of the sensor array to non-target gases and interfering\nsubstances such as lactic acid, pyruvic acid, water vapor, or electrolytes. Data supporting\nspecificity under real skin conditions are essential.\nResponse: We would like to thank the reviewer for the insightful comment regarding the cross-\nresponses of the sensor array to non-target gases and interfering substances. We definitely agree\nthat the organic compounds such as lactic acid (concentration range: 3.7-50.0 mM) and pyruvic\nacid (concentration range: 0.3-2.0 mM)54-55 could act as interfering substances in the sweat\nduring the skin VOC measurement. However, these compounds are non-volatile organic\ncompounds and do not vaporize into gas phase at skin temperature. To evaluate the sensor chip\nspecificity under real skin conditions, we conducted comparative tests using a measurement\nsetup simulating the practical scenario of the skin condition with the sweat composition as the\ninterference. We prepared for solutions including 25 mM lactic acid in PBS (phosphate\nbuffered saline), 1 mM pyruvic acid in PBS, and the artificial sweat as the electrolyte (PBS\nwith 10 mM NaCl, 1 mM NH Cl, 10 mM KCl, and 1 mM urea) to mimic the real skin\n4\nconditions56.\nThe experimental setup, as illustrated in revised figure 18, facilitated performance comparison\nbetween sensor responses to target gases alone and in the presence of these interferents. The\ngas cylinder offers the target gas and the carrier gas, while the mass flow controller units set\nup a constant flow rate for the gas injection into the gas chamber. Notably, the scrubbing bottle\nis utilized for the water vapor injection as the generation of various humidity atmosphere. As\nshown in revised figure 19, the results showed minimal differences in sensor response,\nindicating high specificity towards the target gas compared to lactic acid, pyruvic acid, and\nother electrolytes substances. Regarding the water vapor interference, we previously conducted\nsystematic measurements to assess the sensor chip performance under varying humidity levels,\nconfirming its gas sensing performance and distinguishable capability in the presence of the\nwater vapor. Additionally, our AI-wearable ring platform is equipped with the integrated\ntemperature and humidity sensor, enabling real-time calibration to mitigate water vapor\ninterference, thereby ensuring reliable performance in practical applications."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have carefully and thoroughly addressed the revision requests and have carried out additional calibration\nmeasurements, which are mainly included in the supplementary materials. There is one remaining request from the\nreviewer: the new calibrations performed in random sequence report resistance values on the axes, whereas the figure\nshown in the paper is expressed in percentages (and it should also be specified how this percentage is calculated).\nLikewise, the same plots now included in the supplementary materials are presented in the response to the reviewers in\npercentage form. This discrepancy should be resolved to allow for an appropriate comparison.", "reviewer_2": "In the revised manuscript by Ye et al., the authors have responded to the comments raised during the previous review round.\nI have carefully examined the rebuttal letter and the corresponding revisions in the manuscript, and I am satisfied that my\nmajor and technical concerns have been addressed in a scientifically convincing manner.\nParticularly, the revision clearly articulates the manuscript’s novelty in relation to the authors’ previously published work in\nNature Electronics and provides the additional technical substantiation requested. Overall, the responses are complete, and\nthe added data and analyses materially enhance the rigor, clarity, and credibility of the study. No further revisions are\nrequired from my side, and I therefore recommend acceptance without further revision."}, "author_response": "Response: We would like to sincerely thank the reviewer for the positive comments based on\nour response letter. Regarding the new calibration scheme, we would like to clarify how the\ngas response is calculated in percentage form. The gas sensing response (or gas response in\nshort) is defined as (Ra - Rg)/Ra × 100% (reducing gases) and (Rg - Ra)/Ra × 100% (oxidizing\ngases), where Ra and Rg are the resistances of sensors exposed to background gases (dry/wet\nair) and target gases, respectively (Mentioned in the Methods section). Taking the resistance\nmeasurement curves in the supplementary file as an example, the gas response would be\nconverted by extracting the Ra as the baseline value and Rg value of each gas on/off\nmeasurement sequence and using the gas sensing response formula. All the gas responses in\nthe calibration measurement, the response letter, the manuscript and the supplementary\nmaterials follow this calculation method as the consistency for the plot visualization."}]

Decision letter