Bioinspired triboelectric droplet sensor for ammonia monitoring

Domain: neuroscience
Published: 2026-01-29
Editorial outcome: minor_revision

Abstract
Gassensorswithfastresponseareinhighdemandforenvironmentalandhealthapplications.Conventionalsolid-statesensingmaterialsareinherentlyconstrainedbyresponsedelaysarisingfromchemicalbondtransformations,posingsignificantchallengesinovercomingresponsetimelag.Inthiswork,inspiredbythealveoliofourrespiratorysystem,wedevelopatriboelectricnanogeneratorprobesensordrivenbytheformationofwaterdropletscontaininganaircavity,whichincorporatesNH3molecules.Thesensorachievesrapidresponsethroughinstantaneouselectrontransferattheliquid-solidinterface,bypassingtheneedforgasadsorptionanddesorptiononthesurfaceofsolid-statesensingmaterials.Thishydro-electrochemicalsensingmechanismachievesaresponsetimeof1.4s,surpassingthatofmostreportedammoniasensors.Throughtheintegrationofdeeplearningalgorithmsforoptimization,thesensordemonstratesanammoniadetectionaccuracyof96.2%.Thisstudyindicatesapromisingstrategyfortherationaldesignofgassensorsbasedonhydro-electrochemicalsensingmechanisms.

Introduction
Approximately182milliontonsofammoniagasareproducedgloballyineachyear,withabout80%ultimatelybeingreleasedintotheatmosphere1,2.Theapplicationofammonia(NH₃)spansawiderangeofsectors,includingindustry,agriculture,andhealthcare3.Ammoniaisacolorlessgaswithapungentodor,anditishighlycorrosiveandtoxic4.Prolongedexposuretoammoniaconcentrationsgreaterthan50ppmcanleadtosevererespiratorydiseasesandevendeath5.Efficientandaccuratedetectionofammoniaisofparamountimportanceforbothecologicalenvironmentandhumanhealth.

Ammonia’s unique boiling point (−33.3°C), refractive index (1.33), and dipole moment (1.42 D) make it a targeted gas of interest3. Based on the fundamental physical properties of ammonia, sensors utilizing resistance, optical, triboelectric, and microbalance principles have been developed, with resistive sensing being the mainstream commercial technology4–8. Resistive sensors rely on the adsorption and desorption processes of ammonia on the surface of materials9. This dynamic transformation of chemical bonds inevitably introduces a reaction cycle, limiting the response speed of the sensor10. Triboelectric nanogenerator probe (TENG-P) offers an in situ rapid detection solution, providing millisecond-level response capabilities through transient electron transfer at the liquid-solid interface11–13. Nevertheless, conventional TENG-P lacks gas-sensing capabilities14,15, and rationally designing gas sensors based on the hydroelectrochemicalsensingmechanismsremainsachallenge.

Alveoliarethecorefunctionalunitsofthebiologicalrespiratorysensingandregulatorysystem16. Through natural evolution and adaptation, alveoli have developed a spherical structure with internal cavities, endowing them with a highly efficient gas exchange interface and exceptional physiological stability (Fig. 1a-i). Oxygen diffuses from the alveoli into the bloodstream, binding with hemoglobin to form oxyhemoglobin, which is subsequently sensed by peripheral chemoreceptors to maintain blood oxygen concentrations within appropriate ranges (Fig. 1a-ii). Inspired by this natural design, an air-cavity droplet (A-droplet) driven ammonia gas sensor was developed in this work (Fig. 1b-i). Within the A-droplet, ammonia diffuses and dissolves in water, and its accompanying ionization generates a multitude of ions that regulate the contact electrification process at the liquid-solid interface (Fig. 1b-ii). This operational principle enables the use of droplets as carriers for ammonia detection, offering the following potential performance advantages: (1) The gas response cycle of the sensor is shortened to 1.4 s, enhancing its potential for rapid ammonia detection; (2) the sensor’s output stability is enhanced to 96.2%, contributing to improved sensing accuracy.

Results
Designofthedroplet-drivenammoniasensor
TheammoniasensoriscomposedofanA-dropletpreparationsystemandaTENG-P(Fig.2a).TheA-dropletisdispensedusingacoaxialinjectionsystem,withdropletsizecontrolledbythegas-liquidflowrateratio(Fig.2b,SupplementaryMovie1).Due to thin-film interference caused by bubbles within the A-droplet, the captured optical images exhibit irregular light and dark patterns. A gas-liquid flow rate ratio of 5:1 was selected due to its optimal TENG-P output stability (Supplementary Fig. 1). The TENG-P consists of a top electrode, a tribo-layer, a bottom electrode, and a connecting circuit. An insulated copper wire is used to connect the top electrode, ensuring its electrical isolation from the bottom electrode (Supplementary Fig. 2). When an A-droplet impacts the TENG-P, the sensor forms a closed-loop circuit and generates an output signal (Fig. 2c). Supplementary Movie 2 visually presents the real-time process of output generation driven by the A-droplet and demonstrates the sensor’s continuous operation at a frequency of 2.5 Hz. The sensor exhibits an instantaneous output cycle of 2.1 ms, facilitating rapid target analyte detection (Supplementary Fig. 3).

When ammonia is loaded into the A-droplet, the sensor reaches a stable output after approximately four cycles (Fig. 2d). The sensing response time, defined as the interval from the onset of ammonia exposure to the attainment of a stable output, is determined to be 1.4 s (Fig. 2e). The response hysteresis of the sensor mainly arises from concentration gradient variations within the tube, caused by the thermal motion of ammonia molecules (Supplementary Fig. 4). The sensor’s response time is primarily governed by the droplet release frequency (Supplementary Fig. 5), rather than the ammonia concentration (Supplementary Fig. 6). The operating frequency exerts no significant influence on the sensor’s output, indicating the feasibility of multiple working conditions (Supplementary Fig. 7). Moreover, direct blowing of ammonia does not elicit any sensor response, underscoring the necessity of introducing the A-droplet (Supplementary Fig. 8). These findings indicate that the sensor operates beyond the conventional response mechanisms of solid-state sensing materials, achieving a response speed that surpasses existing reports on ammonia sensors4–6,17–23. Furthermore, the dual-fluid core-shell structure of the A-droplet exhibits impact resistance, effectively suppressing Kelvin-Helmholtz instability caused by droplet impact24. The output stability of the TENG-P is enhanced to 96.2% by the A-droplet (Fig. 2f), surpassing that of most liquid-solid TENGs (Fig. 2g)25–34.

Triboelectricresponsebehavior
The output of the TENG-P is primarily governed by the liquid-solid contact area35, as observed through high-speed camera imaging of the A-droplet impact and spreading process (Fig. 3a). Figure 3b illustrates the operational mechanism of the A-droplet-driven TENG-P. In the stationary state, the circuit of the TENG-P is open, and no electron flow occurs in the external circuit (State i). The impact of the A-droplet transitions the TENG-P to a “closed” state, inducing rapid electron flow towards the bottom electrode due to the potential difference between the top and bottom electrodes, generating the first current peak (Fig. 3c). Under the influence of gravitational potential and kinetic energy, the A-droplet gradually spreads outward, increasing the liquid-solid contact area, during which a slow rise in charge and voltage is detected (State ii). Upon reaching the maximum liquid surface spread, the potential between the electrodes equilibrates, ceasing electron flow (State iii). Subsequently, the droplet retracts and detaches from the top electrode, generating a reverse electron flow (State iv). Fluorinated ethylene propylene (FEP) was selected as the triboelectric layer material due to its superior sensing sensitivity, high resolution, and broad detection range (Supplementary Fig. 9). The material of the top electrode does not significantly affect the output (Supplementary Fig. 10). However, increasing its size inevitably limits the output, attributed to the finite liquid-solid contact area (Fig. 3d).

Characteristic images recorded by the high-speed camera show that the fluid morphology of the A-droplet is relatively smooth as it impacts and spreads to its maximum surface area (Fig. 3e). In contrast, conventional droplets exhibit numerous wrinkles and ripples (Supplementary Movie 3). The wrinkles formed by conventional droplets primarily originate during the initial spreading phase, rather than at the maximum spreading area (Supplementary Fig. 11). This phenomenon is attributed to Kelvin-Helmholtz instability generated by high-speed fluid motion24, resulting in “noise” in the liquid surface spreading velocity (Fig. 3f, g). The irregular formation of wrinkles and ripples results in the difficulty of replicating the droplet’s diffusion area (Supplementary Fig. 12), with conventional droplets displaying a maximum area deviation rate of 8.3% after repeated impacts (Fig. 3h). In contrast, the A-droplet’s solid droplet exhibits an area deviation rate of only 1.9% (Fig. 3i). Consequently, the output fluctuations of the A-droplet-driven TENG-P are significantly lower than those of conventional droplets (Fig. 3j, k).

Optimizationofsensingstability
Careful observation of the droplet impact phenomenon on solid surfaces provides further insight into the fluid characteristics of A-droplet. Both conventional droplets and A-droplets are released from the same height and impact the solid surface, with selected snapshots of the impact-rebound evolution shown in Fig. 4a, b. In contrast to the rebound observed with conventional droplets, A-droplets exhibit oscillation after collision and rapidly remain on the surface (Supplementary Movie 4). Flow field distribution simulations reveal that during the contraction phase of conventional droplets, the fluid converges toward the center under the influence of surface tension and subsequently flows upward, resulting in a bounce (Supplementary Movie 5). In contrast, the A-droplet is influenced by the interplay of its inner and outer gas-liquid interfaces and simultaneously experiences both upward and downward surface tensions (Supplementary Fig. 13). Moreover, during the oscillation of the gas-liquid interface, the flow velocity alternates between upward and downward directions, thereby suppressing the rebound of the droplet (Supplementary Fig. 14).

When the release height of the droplet exceeds a critical threshold, the surface energy of the liquid is insufficient to counteract the impact kinetic energy, causing the droplet to break and form satellite droplets under slight perturbations (Fig. 4c). Observation of 100 droplet impact events revealed that the typical separation characteristic of conventional droplets is the formation of two satellite droplets (probability of 60.5%) (Supplementary Fig. 15). In contrast, A-droplet rarely produces satellite droplets, with a probability of only 8.8% for the formation of a single satellite droplet (Fig. 4d). The formation of satellite droplets detaches mass from the main droplet and reduces its kinetic energy. Therefore, kinetic energy (Ek) can be used to evaluate the instability during the droplet rebound process. Detailed calculations are provided in Section S1 (Supplementary Fig. 16). Compared to conventional droplets, the rebound kinetic energy of A-droplet is reduced by 72.3% (Supplementary Fig. 17), indicating that most of the rebound kinetic energy is dissipated by the air cushion, thereby suppressing droplet rebound and fragmentation (Supplementary Movie 6).

The impact kinetic energy of the A-droplet can be modulated by adjusting the release height, with output intensity exhibiting a positive correlation with release height (Fig. 4e). When the release height is increased to 10 cm, output fluctuation increases to 9.8% (Fig. 4f). This is attributed to excessive droplet stretching (Supplementary Fig. 18), leading to unstable droplet spreading. An optimal output and stability are achieved at an impact angle of 45° (Fig. 4g, h). This is due to the balance between tangential and normal forces experienced by the droplet after impact at a 45° angle (Supplementary Fig. 19), resulting in optimal fluid spreading area post-impact.

Responsemechanismsforammoniamonitoring
Building on the stability optimization of the A-droplet and an understanding of its underlying principles, it is crucial to further elucidate the mechanism of its response to ammonia. A-droplet exhibits stable existence at room temperature without noticeable volume shrinkage (Fig. 5a), negating the influence of droplet morphology on the ammonia response mechanism. After ammonia absorption by the solution, the water contact angle on the dielectric surface exhibits no significant deviation (Supplementary Fig. 20), thereby excluding the influence of wettability on liquid–solid contact electrification. Molecular simulations reveal that the interaction energies of H2O-H2O, NH3-H2O and NH3-NH3 are −4.53 kcal mol−1, −7.06 kcal mol−1 and −3.41 kcal mol−1, respectively (Fig. 5b), indicating that ammonia gas is more inclined to combine with water. FTIR analysis reveals that the dissolution of ammonia causes a shift in the O-H peak, confirming that the dissolved ammonia molecules disturb the hydrogen bonding network of the water molecules (Fig. 5c)36–38. Such significant intermolecular interactions are not observed in other gases, such as H2S, CH4, O2, N2, and CO (Supplementary Fig. 21), rendering them poorly absorbed by water. The sensor’s response to these common gases is negligible (Fig. 5d). Even when these gases are mixed with ammonia, no significant signal deviation is observed (Supplementary Fig. 22), underscoring ammonia as the critical marker for the sensor’s selective recognition.

The sensor is capable of detecting ammonia concentration variations within the 0-200 ppm range, with its sensitivity defined by the slope of the output change, [(I0 − I) / I0] (Fig. 5e), where I0 and I represent the currents in the absence and presence of ammonia, respectively. The sensor exhibits an excellent linear response, indicating high accuracy in its measurements. Moreover, the sensor output remains relatively stable across different ammonia concentrations (Fig. 5f), demonstrating reliable operation under a constant ammonia atmosphere. By optimizing the gas injection setup, the sensor can be directly interfaced with the ambient environment, highlighting its potential for applications in open-air ammonia detection (Supplementary Fig. 23). Continuous monitoring over 192 h was conducted to investigate the influence of varying temperature and humidity in open environments on the sensor performance (Supplementary Fig. 24). The sensor shows an output deviation of less than 10% within the 0–50°C range and less than 15% under 50–90% RH (Supplementary Figs. 25 and 26), with signal variations likely arising from changes in ammonia solubility and humidity-induced suppression of contact electrification. Notably, these environmentally induced signal deviations can be further corrected through algorithmic calibration5.

Upon dissolution in water, ammonia (NH3) readily accepts a proton from H2O, forming ammonium ions (NH4+) and generating hydroxide ions (OH−)39. The spontaneous ionization of aqueous ammonia leads to an increase in solution conductivity and pH (Supplementary Fig. 27). Compared to the initial solution, the A-droplet infused with ammonia carries a substantial ionic load (Fig. 5g). Electron transfer and ion adsorption proceed in parallel during the contact electrification process at the liquid-solid interface40. For aqueous solutions with low ion concentrations, electron transfer predominates41. When droplets impact the dielectric surface, fluorine-containing functional groups with strong electron retention abilities acquire electrons from water molecules or even ions (Fig. 5h-i). Conversely, for A-droplet containing dissolved ammonia and abundant ions, competitive ion adsorption occupies a significant number of electron transfer sites, limiting the ability of fluorine-containing functional groups to extract electrons from water (Fig. 5h-ii). Further increases in solution ion concentration result in an excess of free ions, leading to a more pronounced shielding effect. As the A-droplet departs from the FEP surface, it carries away a substantial quantity of ions, resulting in negligible residual nitrogen on the FEP surface (Supplementary Fig. 28). The structural and elemental stability of the FEP surface indirectly corroborates the sensor’s output consistency. This ion-mediated contact electrification process at the liquid-solid interface provides an effective pathway for utilizing A-droplet to respond to ammonia concentrations.

Real-timeammoniaconcentrationresponse
Approximately 90% of global ammonia emissions originate from agriculture, primarily from fertilizers and animal manure2. In typical greenhouse environments42, efficient and accurate ammonia monitoring is crucial for both plant growth and human health (Supplementary Fig. 29). The integration of ammonia sensors in agricultural production, coupled with wireless sensing and artificial intelligence technologies, enables interdisciplinary intelligent data analysis and facilitates more precise environmental monitoring in agriculture (Fig. 6a). A coaxial injector, a triboelectric droplet sensor, and a wireless sensing module are integrated into a single ammonia sensing platform, with the sensor directly connected to the wireless module (Fig. 6b). When the A-droplet is generated by the top coaxial injector and released downward, the electrical signal produced by the triboelectric droplet sensor enters a signal modulation circuit composed of transistors and resistors. The circuit adjusts the signal amplitude before it is transmitted to the analog-to-digital converter (ADC) of a microcontroller, converting multi-channel analog signals into digital form for processing by the central processing unit. Finally, the Bluetooth module transmits the processed digital signals in real time to intelligent display devices at high transmission rates (Fig. 6c).

Based on the triboelectric sensor’s capability to respond in open environments (Supplementary Fig. 23), the ammonia sensing platform can be applied to environmental monitoring in greenhouses (Fig. 6d). Supplementary Movie 7 visualizes the wireless sensing capability of the platform, with the intelligent display device providing real-time responses without latency. When ammonia contamination occurs in the ambient environment, the platform generates a pronounced concentration response (Fig. 6e). The transmitted data signals to the intelligent display device can be further analyzed using a convolutional neural network (CNN) architecture to enhance ammonia concentration recognition43. This model employs a hierarchical feature-extraction framework, comprising an input layer, multi-level convolutional and pooling modules, fully connected layers, and a Softmax classifier, enabling stepwise extraction of time-frequency image features for ammonia concentration classification (Supplementary Fig. 30). With increasing training iterations, the model demonstrates high training accuracy (Supplementary Fig. 31), indicating stable convergence during training. Following extensive training, testing, and validation, the model’s recognition accuracy for ammonia concentrations improved from 97.8% to 98.4% (Fig. 6f). These results demonstrate that the ammonia sensing platform can accurately detect ammonia and holds potential for developing multifunctional gas-sensing platforms.

Methods
PreparationofA-droplet
Twosyringepumps(LINA-8A,LAIN)wereemployedtodriveaco-flowmicrofluidicdeviceforproducingA-droplets,withairandliquidservingastheinnerandouterphasefluids,respectively.A0.1wt%solutionofsodiumdodecylbenzenesulfonate(Macklin,95%)wasusedastheliquidphase,whileambientairrepresentedthegasphase.Theinnerdiameterofthecoaxialneedlewas0.86mm,andtheinnerdiameteroftheoutercapillarywas1.55mm.TosustaincontinuousA-dropletproduction,theflowrateoftheouterliquidphasewasmaintainedbetween20mL·h-¹and200mL·h-¹,withtheinnergasflowratecontrolledat20mL·h-¹.OrdinarydropletscanbeconsideredasA-dropletswithzerogascontentandweregeneratedusingthesamesolution.Ammoniagaswasgeneratedviaheatedevaporationofaqueousammoniaandsubsequentlystoredinsamplebags.Differentconcentrationsofammoniawerepreparedbymixingammoniagaswithairatpredeterminedratios,withconcentrationsverifiedusinganammoniadetector(JXBS-4001,BAIBOLI).Theammoniagaswasthenintroducedasthegassourceintoaco-flowmicrofluidicdeviceforthegenerationofA-droplets.

FabricationofTENG-P
Alasercutterwasusedtoetchcircularholeswithapproximately1mmdiameterinanacrylicplate(1mmthickness),servingasthesupportingsubstrate.Coppertape(100μmthickness)andFEPtape(80μmthickness)weresequentiallyadheredontotheacrylicplate,functioningasthebottomelectrodeandtribo-layer,respectively.Subsequently,aninsulatedcopperwire(0.3mmdiameter)withaninsulatinglayerwasinsertedthroughthecircularholeoftheacrylicplate,penetratingthebottomelectrodeandtribo-layer.Theprotrudingcopperwirewasaffixedtotheuppersurfaceofthetribo-layerwithacoppersheet(~3mmdiameter),formingthetopelectrode.Acopperwire(0.3mmdiameter)wasledoutfromthebottomelectrode,yieldingaTENG-Pwithadual-electrodestructure(SupplementaryFig.2).

Observationofdropletmorphology
Ahigh-speedcamera(FASTCAMMiniAX200,Photron)wasusedtorecordtheimpactprocessatframeratesrangingfrom1000FPSto5000FPS.Fortheobservationofthedroplet’sbottommorphology,thehigh-speedcamerapositionedvertically,withahorizontaltransparentacrylicplate(2mmthickness)placedabovethecamera.Dropletswerereleasedfromaheightof10cmabovetheacrylicplate.

Characterizationandtesting
Ametallurgicalmicroscope(TD-D-4KE,SANQTID)wasusedtodocumentthegenerationprocessofA-dropletsat60FPS.TheoutputoftheTENG-Pwasrecordedusinganelectrometer(6514,KEITHLEY),acquiringtheshort-circuitcurrentgeneratedbyA-dropletandconventionaldropletimpacts.Unlessotherwisespecified,theoperatingfrequencyoftheA-dropletismaintainedat2.5Hz.Fouriertransforminfraredspectroscopy(TENSORII,BRUKER)wasusedtoanalyzechangesinchemicalfunctionalgroups.Surfacemorphologyandelementalcompositionofthematerialswerecharacterizedusingascanningelectronmicroscopecoupledwithenergy-dispersiveX-rayspectroscopy(GeminiSEM300,ZEISS).Watercontactanglesweremeasuredemployingacontactanglegoniometer(DSA100,KRUSS).

Deeplearning
SignalclassificationwasimplementedusingtheYOLO11algorithmwithintheUltralyticsframework,employingaCNNarchitecturetoanalyzethetime–frequencyimagesderivedfromtriboelectricsignals.Themodelarchitecturecomprisesaninitialconvolutionallayerforfeatureextractionacrossthreestages,withC3k2andC2PSAmodulesenablingmulti-scalefeaturefusion,andafinalClassifylayeroutputtingfiveammoniaconcentrationlevels.Thedatasetconsistsof6000images,partitionedintotraining,validation,andtestsetsina6:3:1ratio.TheYOLO11classificationmodelwasconfiguredfortransferlearningbasedonthepre-trainedYOLO11x-clsweights,successfullytransferring234outof236weightparameters.TrainingwasconductedonanNVIDIARTX4070GPUfor100epochs,employinganadaptivelearningratescheduletooptimizeconvergence,witheightdata-loadingthreadsforparallelprocessingandabatchsizeof16.TheoutputlayerutilizesaSoftmaxactivationfunctiontoclassifyfiveammoniaconcentrationlevels(0,50,100,150,200ppm),andclassificationaccuracywasoptimizedusingthecross-entropylossfunction.

Numericalsimulation
FlowdistributionwascalculatedusingANSYSFluentsoftware,andtheVolumeofFluidmethodwasemployedtosimulatethedynamicprocessesonthesurfaceofA-dropletandconventionaldroplets.Thedropletwassimulatedwithinacylindricaldomainwithawidthof12mmandaheightof10mm.Asuperhydrophobiccontactanglewasappliedtothebottomregion,andthesurfaceoftheotherregionswassetasapressureoutletboundary.ThePressure-VelocityCouplingmethodwasusedtosolvethepressureandvelocitycouplingequations.Forthetwophases,theprimaryphasewassetasair,andthesecondaryphasewassetasliquid.Intheinitialstage,thewaterdropletwassimulatedasaspherewithadownwardimpactvelocity.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this article, the authors primarily focus on two aspects of the research: On one hand, they designed an ammonia gas sensor based on triboelectric nanogenerator probe (TENG-P) and detailed its working principles. Compared to the second-level response time of traditional gas sensors, this sensor achieves a millisecond-level response time. Its integration with deep learning technology also demonstrates exceptional accuracy in ammonia concentration detection. On the other hand, they analyzed the advantages of A-droplet with a bilayer structure over conventional droplets, and demonstrated that using ammonia-dissolved A-droplet as a medium can effectively enhance the output stability of the sensor. This study provides a high-precision, rapid-response and stable solution for ammonia concentration detection in agricultural greenhouse environments. However, the article still has several critical issues that need to be addressed before publication to enhance its persuasiveness and scientific value.\n1. The article only provides a schematic diagram of the sensor structure in Fig.2-a, but omits photographs of the physical devices. It is recommended to supplement images of the fabricated sensor to facilitate readers’ intuitive understanding of its working mechanism. Additionally, it is also recommended to supplement the schematic diagram of the fabrication process of the sensor.\n2. All sensor performance data were obtained under controlled laboratory conditions. Practical application scenarios, such as greenhouse, have certain differences from the laboratory environment (e.g.,temperature and humidity), which often fluctuate over time and other factors. Therefore, it is recommended to include performance tests under realistic environmental conditions to enhance the credibility of the test data significantly, and further demonstrate the robustness and stability of the device.\n3. The article does not directly compare the detection capabilities of multiple sensors, so it is recommended to add a test of multiple sensors under identical conditions, and compare the consistency of their output. In addition, the short-term stability of the sensor was tested through 100 cycles, but the long-term stability over weeks or even months was not explicitly evaluated. Considering that the normal operation of the sensor relies on the electron-capturing function of the Teflon-based tribo-layer, prolonged exposure may lead to dust and other contaminants adhering to its surface, thereby degrading sensor performance. Therefore, it is recommended to add long-term stability experiments for the sensor.", "reviewer_2": "This manuscript presents a new method for ammonia monitoring based on a droplet triboelectric nanogenerator. The reported stability is maybe a plus. However, I have some serious concerns, particularly regarding the claimed 2.1 ms response time. It seems to me that the overall system response is fundamentally limited by the droplet down-drop frequency, which casts significant doubt on that rapid response time 2.1 ms claim. Given this key limitation, I can't recommend this manuscript for publication in Nature Communications.\n1. The sensor relies on the formation of A droplets from ammonia and sodium dodecylbenzene sulfonate. I'm wondering, could the sensor's performance be affected by the presence of carbon dioxide or water vapor? Figure 5d shows a relatively low response to other gases, which is encouraging. However, what happens when these gases are mixed with ammonia? Will the sensor still accurately detect the ammonia concentration in the presence of CO2 and water vapor? This is a critical point that needs clarification.\n2. While the sensor's intrinsic response time is fast, the overall system response is clearly limited by the slow rate of A-droplet formation. It appears the response time of the sensor system isn't really governed by the TENG output speed, but rather by the rate at which the A-droplets are generated and dropped. Given this bottleneck, how can the authors justify claiming a fast response time for the entire system?\nTo be precise, the response time should reflect the time interval between a change in the physical quantity being measured (in this case, ammonia concentration) and the time it takes for the TENG sensor to register that change. So, how long does it actually take for the TENG sensor to detect a sudden spike in ammonia concentration?\nFurthermore, the data presented in Figure 2d, where the ammonia concentration changes abruptly from 0 ppm to 50 ppm, is concerning. The time it takes for the sensor to detect ammonia from 0 ppm to 50 ppm is also not the reported 2.1ms. The sensor seemingly fails to capture any intermediate states. This suggests a significant delay or a lack of sensitivity to gradual changes in ammonia concentration, which further undermines the claim of a fast response. The authors need to address this discrepancy directly.\n3. Authors tweaked the height and angle to get a more stable reading. But I'm wondering, does that stability hold up when the ammonia concentration starts fluctuating?\n4. Authors explain that the stability of A-droplet comes from two substances. I'm curious, can we apply this finding to other droplet-based friction nanogenerators, or is this just a special case?\n5. The paper doesn't really get into optimizing the sensor's structure or materials to improve performance like sensitivity, range, detection limit, or resolution.", "reviewer_3": "In the proposed manuscript entitled “Bioinspired triboelectric droplet sensor for ammonia monitoring”, the authors have developed a liquid-solid triboelectric nanogenerator for rapid and accurate sensing of ammonia gas. In the proposed setup, the authors have utilized an air-cavity droplet (A-droplet) using a coaxial injection system and a triboelectric nanogenerator probe (TENG-P) for sensing purposes. For optimization purposes, the ratio of the flow rate of liquid and gas, frequency of impacting droplet, size of the top electrode, impacting height, and sliding angle are optimized to obtain the maximum stable output. The proposed system has shown a remarkable enhancement in response time of the ammonia sensing and integration of deep learning efficiently provided the recognition of ammonia concentration. Following are some comments to improve the quality of this manuscript:\n1. In order to validate the enhancement in the diffusion rate of NH3 molecule on the triboelectric surface after interacting with water molecules, the authors are suggested to provide the triboelectric current data upon direct exposure of different amounts of NH3 gas to the triboelectric surface as similar to the different amount of NH3 given in A-droplet for sensing.\n2. In order to show the effect of NH3 concentration on hydrophobicity, the authors are suggested to provide contact angle data of A-droplets with different concentrations of NH3.\n3. The authors are suggested to provide a discussion about the potential of the proposed ammonia sensing system for usage in practical situations, such as collection of gas from targeted areas and implementation for sensing. It is also recommended to provide the sensing data for gases collected from a real environment.\n4. It seems that the authors haven’t provided the details about how they have changed the concentration of ammonia gas in A-droplet for sensing data provided in Fig. 6b. In order to make the manuscript more informative and enrich its understanding, the authors should provide these experimental details.\n5. The authors are suggested to discuss the motivation for selecting Teflon as the tribo-layer and provide a systematic comparison of sensing performance for different tribo-surfaces.\n6. Regarding the information provided about tribo-layer in the methods section (Teflon), and in Fig. 3b (FEP) is not consistent. The authors are suggested to rectify this mistake.\n7. (a) Regarding the schematic presented in Fig. 6e related to deep learning, the authors are encouraged to briefly discuss the CNN model architecture employed, details about the datasets used, and the methodology applied to validate the classification results in the present study. (b) Given that the dataset shown in Fig. 6b appears relatively simple, the authors are requested to clarify why a complex model such as a CNN was chosen over traditional machine learning approaches like SVM or Random Forest.\n8. In Fig. 2a and Fig. 2c, the labeling for different components of TENG-P looks confusing because the color of the tribo-layer is not consistent over Fig. 2a and Fig. 2c. Please rectify this kind of mistake to enhance the readability of this manuscript.\n9. Regarding Fig. 2b, where the authors have shown a change in the size of A-droplet upon change in the ratio of liquid and NH3. However, despite the size of the A-droplet, the appearance of the A-droplet also looks different. The authors should clarify that the changes in bright and dark areas are just a part of reflectance during photography or shape of air inside the A-droplet is changing.\n10. The authors are suggested to provide the analysis of surface morphology and chemical compositions of tribo-layer before and after the sensing of ammonia to show the stability and reusability of the proposed sensor as the authors have shown generation of stable output over 100 cycles."}, "author_response": "Point-by-point Response to Reviewers' Comments\nReviewer #1:\nIn this article, the authors primarily focus on two aspects of the research: On one\nhand, they designed an ammonia gas sensor based on triboelectric nanogenerator\nprobe (TENG-P) and detailed its working principles. Compared to the second-\nlevel response time of traditional gas sensors, this sensor achieves a millisecond-\nlevel response time. Its integration with deep learning technology also\ndemonstrates exceptional accuracy in ammonia concentration detection. On the\nother hand, they analyzed the advantages of A-droplet with a bilayer structure\nover conventional droplets, and demonstrated that using ammonia-dissolved A-\ndroplet as a medium can effectively enhance the output stability of the sensor. This\nstudy provides a high-precision, rapid-response and stable solution for ammonia\nconcentration detection in agricultural greenhouse environments. However, the\narticle still has several critical issues that need to be addressed before publication\nto enhance its persuasiveness and scientific value.\nResponse: We sincerely appreciate your thorough review and patient summary of our\nmanuscript. We highly value this opportunity to revise and improve our work, and have\nmade substantial enhancements in this revision. In response to the reviewers’ concerns,\nwe have supplemented the manuscript with schematic diagrams of the sensor\nfabrication process and photographs of the assembled device to further clarify the\nsensor’s working principle. Additionally, we conducted comprehensive tests of the\nsensor’s response under various temperature and humidity conditions to better simulate\nreal-world environments. All new experimental data and revisions are detailed in the\nrevised manuscript and clearly highlighted with a yellow background. We are deeply\ngrateful for the reviewers’ insightful evaluations and your guidance, which have driven\nus to refine the scientific rigor and logical coherence of this work. Based on these\nextensive revisions and supplemental data, we sincerely hope the manuscript will be\nreconsidered for publication.\n1\n1. The article only provides a schematic diagram of the sensor structure in Fig.2-\na, but omits photographs of the physical devices. It is recommended to supplement\nimages of the fabricated sensor to facilitate readers’ intuitive understanding of its\nworking mechanism. Additionally, it is also recommended to supplement the\nschematic diagram of the fabrication process of the sensor.\nResponse: We greatly appreciate the reviewer’s professional comments, which are of\ngreat help to our work. The fabrication process of the sensor is illustrated in the\nSupplementary Information (Supplementary Fig. 2). Concurrently, a photograph of the\nassembled sensor is provided (Fig. R1) to offer readers an intuitive understanding of its\noperational mechanism. Specifically, a custom acrylic substrate was prepared via laser\nengraving. Copper tape was adhered onto the acrylic as the electrode, followed by\napplication of an FEP film as the triboelectric layer atop the copper tape. A copper wire\nwas threaded sequentially through the acrylic board, copper tape, and FEP film,\nconnecting to the upper copper electrode to complete the triboelectric sensor assembly.\nRelevant details have been incorporated into the Methods section of the revised\nmanuscript as follows:\nSupplementary Fig. 2. Schematic illustration of the fabrication process of the TENG-P.\n“The TENG-P consists of a top electrode, a tribo-layer, a bottom electrode, and a\nconnecting circuit. An insulated copper wire is used to connect the top electrode,\nensuring its electrical isolation from the bottom electrode (Supplementary Fig. 2).”\n“A laser cutter was used to etch circular holes with approximately 1 mm diameter in\nan acrylic plate (1 mm thickness), serving as the supporting substrate. Copper tape\n(100 μm thickness) and FEP tape (80 μm thickness) were sequentially adhered onto the\nacrylic plate, functioning as the bottom electrode and tribo-layer, respectively.\nSubsequently, an insulated copper wire (0.3 mm diameter) with an insulating layer was\ninserted through the circular hole of the acrylic plate, penetrating the bottom electrode\nand tribo-layer. The protruding copper wire was affixed to the upper surface of the\ntribo-layer with a copper sheet (approximately 3 mm diameter), forming the top\nelectrode. A copper wire (0.3 mm diameter) was led out from the bottom electrode,\nyielding a TENG-P with a dual-electrode structure (Supplementary Fig. 2).”\n2. All sensor performance data were obtained under controlled laboratory\nconditions. Practical application scenarios, such as greenhouse, have certain\ndifferences from the laboratory environment (e.g.,temperature and humidity),\nwhich often fluctuate over time and other factors. Therefore, it is recommended to\ninclude performance tests under realistic environmental conditions to enhance the\n3\ncredibility of the test data significantly, and further demonstrate the robustness\nand stability of the device.\nResponse: We greatly appreciate the reviewer’s professional comments, which are of\ngreat help to our work. The revised manuscript includes additional testing of the\ntemperature effects on sensor performance (Supplementary Fig. 24). Consistent with\nprevious reports (Adv. Funct. Mater. 2021, 31, 2010220), temperature variations alter\nthe water contact angle of the droplets, thereby influencing the triboelectric charging\nprocess at the droplet interface and resulting in reduced output. However, the\nsuppression rate of output due to temperature changes remains below 10%, which is\ndeemed acceptable. Furthermore, the influence of ambient humidity on sensor\nperformance was also investigated (Supplementary Fig. 25). Under high humidity\nconditions, surface charges on the FEP film are readily neutralized by water molecules\nin the air, leading to a decreased surface charge density and consequently affecting\nsensor output. Signal deviations caused by environmental factors can be mitigated\nthrough compensation algorithms, addressing practical challenges encountered by the\nsensor in real-world applications (Nat. Commun. 2024, 15, 6936). Relevant discussions\nhave been incorporated into the revised manuscript as follows:\nSupplementary Fig. 24. Effect of temperature on sensor performance.\n“The sensor’s output remains stable under specified temperature and humidity\nconditions (Supplementary Figs. 24 and 25), with signal deviations induced by\nenvironmental factors amenable to correction through compensation algorithms.”\n3. The article does not directly compare the detection capabilities of multiple\nsensors, so it is recommended to add a test of multiple sensors under identical\nconditions, and compare the consistency of their output. In addition, the short-\nterm stability of the sensor was tested through 100 cycles, but the long-term\nstability over weeks or even months was not explicitly evaluated. Considering that\nthe normal operation of the sensor relies on the electron-capturing function of the\nTeflon-based tribo-layer, prolonged exposure may lead to dust and other\ncontaminants adhering to its surface, thereby degrading sensor performance.\nTherefore, it is recommended to add long-term stability experiments for the sensor.\nResponse: We greatly appreciate the reviewer’s professional comments, which are of\ngreat help to our work. In response to the reviewers’ suggestions, simultaneous output\nmeasurements of four TENG-P sensors under identical conditions were conducted (Fig.\nR2). The results demonstrate consistent outputs across multiple sensors, with an output\ndeviation rate below 5%. Additionally, the long-term output stability of the TENG-P\nwas evaluated over a continuous period of 21 days (3 weeks) (Fig. R3). Owing to the\nhydrophobicity and corrosion resistance of FEP, the sensor maintained stable\n5\nperformance throughout the extended testing period. Furthermore, elemental mapping\nof the FEP surface was provided in the revised manuscript. No significant changes in\nsurface morphology or chemical composition were observed following ammonia\nsensing (Supplementary Fig. 22). The nitrogen content on the FEP surface after sensing\nwas below 0.1%, likely attributable to instrumental measurement error. The structural\nand elemental stability of the FEP surface thereby indirectly corroborates the sensor’s\ndurability. Relevant discussions have been incorporated into the revised manuscript as\nfollows:\nFig. R2. Output performance of multiple TENG-P devices.\nFig. R3. Long-term stability performance of the sensor.\ntriboelectric layer before and after ammonia sensing.\n“As the A-droplet departs from the FEP surface, it carries away a substantial quantity\nof ions, resulting in negligible residual nitrogen on the FEP surface (Supplementary\nFig. 22). The structural and elemental stability of the FEP surface indirectly\ncorroborates the sensor’s output consistency.”\n“Surface morphology and elemental composition of the materials were characterized\nusing a scanning electron microscope coupled with energy-dispersive X-ray\nspectroscopy (GeminiSEM 300, ZEISS).”\nThis manuscript presents a new method for ammonia monitoring based on a\ndroplet triboelectric nanogenerator. The reported stability is maybe a plus.\nHowever, I have some serious concerns, particularly regarding the claimed 2.1 ms\nresponse time. It seems to me that the overall system response is fundamentally\nlimited by the droplet down-drop frequency, which casts significant doubt on that\nrapid response time 2.1 ms claim. Given this key limitation, I can't recommend\nthis manuscript for publication in Nature Communications.\nResponse: We sincerely thank the reviewers for their careful evaluation and\nconstructive comments, particularly for recognizing the stability of the triboelectric\nnanogenerator-based droplet sensor in ammonia monitoring. Regarding the key concern\nraised about the rapid 2.1 ms response time, we have undertaken thorough reflection\nand substantial revisions. We candidly acknowledge that the original manuscript lacked\na precise definition of “response time,” which may have caused misunderstandings.\nIn the revised manuscript, we have explicitly distinguished between the sensor’s\n“electrical output response time” and the “sensing response time.” Supplementary\nexperiments confirm that the ammonia sensing response time is approximately 1.4 s,\nwhile the 2.1 ms corresponds to the device-level electrical signal response time. To\naddress additional concerns, we have supplemented the manuscript with evaluations of\nhow sensor materials and structural parameters influence sensing performance, thereby\nclarifying the sensor assembly principles. Furthermore, the effect of various gas\natmospheres on ammonia sensing was investigated, and the droplet-induced liquid-\nsolid contact electrification phenomenon was also demonstrated on other liquid-solid\ntriboelectric nanogenerators.\nAll supplementary experimental data and revisions are detailed in the revised\nmanuscript and clearly highlighted in yellow. We hope these additions and clarifications\n8\nwill resolve the reviewer’s concerns and merit reconsideration of our manuscript.\n1. The sensor relies on the formation of A droplets from ammonia and sodium\ndodecylbenzene sulfonate. I'm wondering, could the sensor's performance be\naffected by the presence of carbon dioxide or water vapor? Figure 5d shows a\nrelatively low response to other gases, which is encouraging. However, what\nhappens when these gases are mixed with ammonia? Will the sensor still\naccurately detect the ammonia concentration in the presence of CO2 and water\nvapor? This is a critical point that needs clarification.\nResponse: We greatly appreciate the reviewer’s professional comments, which are of\ngreat help to our work. We evaluated the impact of carbon dioxide and water vapor\ninjection into A-droplets on ammonia sensing performance. Specifically, composite\ngases were prepared by mixing air, water vapor, or carbon dioxide with ammonia (at a\nconcentration of 50 ppm) in a 1:1 volumetric ratio, and subsequently introduced into\nthe A-droplet for performance testing. Compared to air, the addition of water vapor\nexhibited negligible influence on sensor output. Although carbon dioxide admixture\ncaused some suppression of the output, the deviation remained below 5% (Fig. R4).\nSince water serves as the primary component within the A-droplet, the partial\ncondensation of water vapor does not significantly alter the droplet volume, rendering\nthe effect of water vapor on output negligible. Carbon dioxide dissolves in water along\nwith ammonia to form ammonium carbonate, increasing ion concentration and thereby\nrestricting charge transfer at the liquid-solid interface, which leads to output reduction.\nHowever, the solubility of carbon dioxide in water is approximately 1:0.878 (1 L water\ndissolves 0.878 L CO ), roughly 800 times lower than that of ammonia, thus its impact\n2\non output is minimal. This is attributed to the limited influence of carbon dioxide\ndissolution on ion concentration within the solution system, maintaining relatively\n9\nstable charge transfer at the interface.\nThe findings in Fig. 5d, showing various gases mixed with ammonia, further\nvalidate the minimal effect of composite gases on sensor output. Specifically, each gas\nwas mixed with ammonia at a 1:1 ratio as a composite gas source and injected into the\nA-droplet for performance evaluation. Similar to carbon dioxide, these gases exhibit\nlow solubility in water and almost no direct reaction with ammonia, resulting in\nminimal impact on sensor output (Supplementary Fig. 21). Corresponding discussions\nhave been incorporated into the revised manuscript as follows:\nFig. R4. Influence of carbon dioxide and water vapor on output performance.\nSupplementary Fig. 21. Effect of mixed gases on sensor performance.\n“The sensor’s response to these common gases is negligible (Fig. 5d). Even when these\n10\ngases are mixed with ammonia, no significant signal deviation is observed\n(Supplementary Fig. 21), underscoring ammonia as the critical marker for the sensor’s\nselective recognition.”\n2. While the sensor's intrinsic response time is fast, the overall system response is\nclearly limited by the slow rate of A-droplet formation. It appears the response\ntime of the sensor system isn't really governed by the TENG output speed, but\nrather by the rate at which the A-droplets are generated and dropped. Given this\nbottleneck, how can the authors justify claiming a fast response time for the entire\nsystem?\nResponse: We sincerely appreciate the reviewer’s thoughtful reflections and\nconstructive comments regarding the sensor’s response time, which have been\ninvaluable to our work. As the reviewer rightly pointed out, the sensor’s response is\nconstrained by the droplet formation rate, leading to ambiguities in defining the sensing\nresponse time. In our previous experiments, two separate systems were used to inject\ndroplets simultaneously to validate ammonia sensing, overlooking the droplet\nformation rate and thereby yielding a reported response time of 2.1 ms. In the revised\nmanuscript, the droplet generation time has been synchronously incorporated, allowing\nus to precisely redefine the ammonia sensing response time (detailed experiments are\npresented below). The droplet release cycle introduces an intermediate sensing state\nconsistent with response cycles observed in conventional gas sensors. Accordingly, we\nnow define the 2.1 ms interval as the device-level electrical signal response time. We\nhope this redefinition of the ammonia sensing response time will meet the reviewer’s\napproval. Once again, we thank you for highlighting this critical technical issue,\nenabling us to further refine our work.\n11\n“The sensor exhibits an instantaneous output cycle of 2.1 ms, facilitating rapid target\nanalyte detection (Supplementary Fig. 3).”\nTo be precise, the response time should reflect the time interval between a change\nin the physical quantity being measured (in this case, ammonia concentration) and\nthe time it takes for the TENG sensor to register that change. So, how long does it\nactually take for the TENG sensor to detect a sudden spike in ammonia\nconcentration?\nResponse: We fully concur with the reviewer’s definition that “response time” should\nreflect the interval between the variation of the target physical quantity and the sensor’s\ncorresponding response. In this study, the sensor’s output response reflects changes in\nambient ammonia concentration. To rigorously examine this process, we have\nundertaken substantial revisions in the manuscript, redefining the sensor’s response\ntime as 1.4 s. Specifically, during continuous testing, ammonia gas was abruptly\nintroduced while the output was monitored in real time to determine the sensor’s\nresponse latency. Upon injection of high-concentration ammonia, the sensor stabilized\nafter approximately four output cycles (Fig. 2d), with the response time confirmed as\n1.4 s (Fig. 2e). Similar results were obtained under varying ammonia concentrations,\nas demonstrated in Supplementary Fig. 6. Corresponding revisions have been\nincorporated into both the Abstract and Results sections. We sincerely thank the\nreviewer for their thorough evaluation, which has significantly enhanced the clarity and\ncomprehensiveness of our study. Detailed modifications are as follows:\nsensor output.\nSupplementary Fig. 6. Effect of ammonia concentration on the sensor response time.\n“When ammonia is loaded into the A-droplet, the sensor reaches a stable output after\napproximately four cycles (Fig. 2d). The sensing response time, defined as the interval\nfrom the onset of ammonia exposure to the attainment of a stable output, is determined\nto be 1.4 s (Fig. 2e).”\n“This hydro-electrochemical sensing mechanism achieves an unprecedented response\nrate, with a response time of 1.4 s, surpassing that of currently reported ammonia\nsensors.”\n“(1) The gas response cycle of the sensor is shortened to 1.4 s, enhancing its potential\nfor rapid ammonia detection.”\n13\n“The sensor's response time is primarily governed by the droplet release frequency\n(Supplementary Fig. 5), rather than the ammonia concentration (Supplementary Fig.\n6).”\nFurthermore, the data presented in Figure 2d, where the ammonia concentration\nchanges abruptly from 0 ppm to 50 ppm, is concerning. The time it takes for the\nsensor to detect ammonia from 0 ppm to 50 ppm is also not the reported 2.1ms.\nThe sensor seemingly fails to capture any intermediate states. This suggests a\nsignificant delay or a lack of sensitivity to gradual changes in ammonia\nconcentration, which further undermines the claim of a fast response. The authors\nneed to address this discrepancy directly.\nResponse: We fully agree with the reviewer’s observation that the sensor must exhibit\nan intermediate state reflecting changes in the external physical quantity. In the revised\nmanuscript, this intermediate variation has been experimentally validated (Fig. 2d),\nestablishing the sensor’s response time at 1.4 s. We further clarify the origin of this\nintermediate state. Specifically, the dissolution rate of ammonia in water far exceeds\nthe droplet formation cycle, meaning the ammonia concentration during the droplet\nrelease and monitoring process remains relatively stable. Additionally, electron transfer\nat the liquid-solid interface occurs instantaneously; thus, liquid-solid contact\nelectrification does not induce a pronounced intermediate state. Therefore, the\nintermediate state observed in the sensor response originates from gas diffusion within\nthe delivery tubing (Supplementary Fig. 4). In the tubing, high-concentration ammonia\ngradually diffuses into the surrounding air, causing sequential droplets to contain\nvarying ammonia concentrations, thereby producing the intermediate response state.\nFurthermore, our experiments demonstrate that the sensor’s response time is primarily\nlimited by the droplet release frequency. Increasing the droplet generation rate\n14\naccelerates gas evacuation from the tubing, shortening the response cycle\n(Supplementary Fig. 5). Currently, our setup supports a maximum operating frequency\nof approximately 3 Hz, corresponding to a gas sensor response cycle of 1.4 s. Relevant\ndiscussions have been incorporated into the revised manuscript as follows:\nSupplementary Fig. 4. Schematic illustration of ammonia diffusion within the gas\ndelivery tubing.\nSupplementary Fig. 5. Effect of droplet release frequency on the ammonia sensing\nresponse time.\n“The intermediate transition phase during ammonia sensing is attributed to\nconcentration diffusion within the delivery tubing (Supplementary Fig. 4).”\n“The sensor's response time is primarily governed by the droplet release frequency\n15\n(Supplementary Fig. 5), rather than the ammonia concentration (Supplementary Fig.\n6). These findings indicate that the sensor operates beyond the conventional response\nmechanisms of solid-state sensing materials, achieving a response speed that surpasses\nexisting reports on ammonia sensors.”\n3. Authors tweaked the height and angle to get a more stable reading. But I'm\nwondering, does that stability hold up when the ammonia concentration starts\nfluctuating?\nResponse: We greatly appreciate the reviewer’s professional comments, which are of\ngreat help to our work. As shown in Fig. 2d, upon the sudden introduction of high-\nconcentration ammonia, the sensor’s output exhibits a pronounced decrease. This\nconsistent decline is attributed to the sensor’s real-time response to ammonia,\nafter\nwhich the output stabilizes. Similar results were observed under varying ammonia\nconcentrations (Supplementary Fig. 6), demonstrating that changes in ammonia\nconcentration do not significantly affect sensor stability. To further verify the impact of\nammonia concentration on output stability, we conducted supplementary tests under\nthree ammonia concentration conditions—0, 50, and 100 ppm—while maintaining\nidentical testing parameters. Variations in ammonia concentration did not notably\ninfluence the sensor’s output stability (Fig. R6). Given ammonia’s high solubility in\nwater, the internal cavity volume of the A-droplet remains nearly constant during\ndroplet release. Moreover, as demonstrated in Fig. 5a, ammonia remains stably retained\nwithin the A-droplet for extended periods. Consequently, the A-droplet maintains a\nstable volume throughout testing, enabling the sensor to sustain relatively stable output.\nRelevant discussions have been incorporated into the revised manuscript as follows:\nSupplementary Fig. 6. Effect of ammonia concentration on the sensor response time.\nFig. R6. Impact of ammonia concentration on sensor output stability.\n“When ammonia is loaded into the A-droplet, the sensor reaches a stable output after\n17\napproximately four cycles (Fig. 2d).”\n“The sensor's response time is primarily governed by the droplet release frequency\n(Supplementary Fig. 5), rather than the ammonia concentration (Supplementary Fig.\n6).”\n4. Authors explain that the stability of A-droplet comes from two substances. I'm\ncurious, can we apply this finding to other droplet-based friction nanogenerators,\nor is this just a special case?\nResponse: We highly appreciate the reviewer’s professional comments and we are\npleased to have the opportunity to answer the questions. Here, we present three types\nof droplet-based triboelectric nanogenerators (Fig. R7), with detailed structural\nschematics displayed below each corresponding photograph. Device 1 represents the\nconfiguration used in this work. Device 2 is based on a previously reported structure\n(Nature 2020, 578, 392-396), while Device 3 follows a design reported in Energy &\nEnvironmental Science 2023, 16, 1071-1081. Under identical testing conditions, all\nthree devices generated distinct and measurable output signals. These findings\ndemonstrate that the sensing principles and mechanisms established in this study are\napplicable to other droplet-based triboelectric nanogenerators. Detailed data are\nprovided as follows:\n5. The paper doesn't really get into optimizing the sensor's structure or materials\nto improve performance like sensitivity, range, detection limit, or resolution.\nResponse: We greatly appreciate the reviewer’s professional comments, which are of\ngreat help to our work. In the revised manuscript, we supplemented tests to evaluate the\ninfluence of triboelectric materials on sensing performance. Three materials—PTFE,\nFEP, and PDMS—were used as the triboelectric layer under identical testing conditions.\nAmong them, FEP exhibited the best sensing performance, with a sensitivity of 4.03\nnA ppm-1, a detection range of 0-250 ppm, a minimum detection limit of 10 ppm, and\na resolution of 10 ppm. In contrast, PDMS showed weaker sensing performance, with\na sensitivity of 0.56 nA ppm-1, a detection range of 0-150 ppm, a minimum detection\nlimit of 50 ppm, and a resolution of 50 ppm (Supplementary Fig. 7). This is attributed\nto the superior output performance of FEP, reaching up to 1.5 μA. Since the output peak\ndirectly determines the sensor’s sensitivity and detection limit, a higher output enables\nmore distinct differentiation of signal changes caused by variations in ammonia\nconcentration. Conversely, reduced output peaks inevitably hinder the sensor’s ability\nto respond to subtle changes in ammonia levels. Corresponding discussion has been\nadded to the revised manuscript.\nIn addition, we investigated the effect of electrode size on the sensor’s detection\nlimit and resolution for ammonia sensing. An increase in electrode size led to a\nreduction in output (as shown in Fig. 3d of the main text), thereby limiting the dynamic\noutput range. Consequently, the detection limit and resolution of ammonia sensing\n19\nshowed a negative correlation with electrode size (Fig. R8). Specifically, when the\nelectrode width was set to 1 mm, optimal output was achieved, allowing a detection\nlimit and resolution of 10 ppm. As the electrode size increased to 7 mm, both the\ndetection limit and resolution decreased to 50 ppm. Detailed revisions have been\nincorporated as follows:\nSupplementary Fig. 7. Effect of triboelectric materials on sensing performance.\nFig. R8. Effect of electrode size on sensing performance.\n“Fluorinated ethylene propylene (FEP) was selected as the triboelectric layer material\ndue to its superior sensing sensitivity, high resolution, and broad detection range\n(Supplementary Fig. 7).”\nIn the proposed manuscript entitled “Bioinspired triboelectric droplet sensor for\nammonia monitoring”, the authors have developed a liquid-solid triboelectric\nnanogenerator for rapid and accurate sensing of ammonia gas. In the proposed\nsetup, the authors have utilized an air-cavity droplet (A-droplet) using a coaxial\ninjection system and a triboelectric nanogenerator probe (TENG-P) for sensing\npurposes. For optimization purposes, the ratio of the flow rate of liquid and gas,\nfrequency of impacting droplet, size of the top electrode, impacting height, and\nsliding angle are optimized to obtain the maximum stable output. The proposed\nsystem has shown a remarkable enhancement in response time of the ammonia\nsensing and integration of deep learning efficiently provided the recognition of\nammonia concentration. Following are some comments to improve the quality of\nthis manuscript:\nResponse: We sincerely thank the reviewer for the comprehensive evaluation and\npositive recognition of our work. We are also truly grateful for your constructive\nsuggestions for improvement. In response, the revised manuscript includes additional\nexperiments investigating the influence of environmental conditions (temperature and\nhumidity) on sensor performance, clarifying the sensor’s applicability under real-world\nscenarios. We have also examined the effect of ammonia on the contact angle of the\nsolution, as well as the impact of different triboelectric materials on output performance.\nFurthermore, the discussion of the deep learning framework (CNN) has been expanded,\nand numerous technical details have been revised accordingly. All new experimental\ndata and revisions have been presented in detail in the revised manuscript, with changes\nclearly highlighted in yellow. We strongly agree with the reviewer’s suggestions and\nthank the reviewer again. We hope to get the approval of the reviewers.\n1. In order to validate the enhancement in the diffusion rate of NH molecule on\n3\nthe triboelectric surface after interacting with water molecules, the authors are\nsuggested to provide the triboelectric current data upon direct exposure of\ndifferent amounts of NH gas to the triboelectric surface as similar to the different\n3\n21\namount of NH given in A-droplet for sensing.\ngreat help to our work. In the revised manuscript, we investigated the sensor’s output\nwhen directly exposed to ammonia gas at various concentrations. Specifically,\nammonia at the same concentrations used in droplet-based sensing was blown directly\nonto the surface of the triboelectric material, while all other testing conditions were\nheld constant. Unlike the scenario in which ammonia is introduced via the A-droplet,\nthe sensor did not produce a noticeable output under direct gas exposure (Fig. R9). This\nis attributed to the chemical inertness of the triboelectric material FEP, which renders it\nvirtually unreactive with ammonia. Additionally, current triboelectric nanogenerator\ntechnologies lack the capability to harvest energy or generate sensing signals directly\nfrom gaseous media, making gas-triggered sensing highly challenging. Therefore, the\nuse of A-droplets as ammonia carriers is essential for enabling effective sensing\nresponses (Fig. R10). Relevant modifications have been incorporated as follows:\nFig. R9. Sensor responsiveness under ammonia atmosphere.\n2. In order to show the effect of NH concentration on hydrophobicity, the authors\n3\nare suggested to provide contact angle data of A-droplets with different\nconcentrations of NH .\ngreat help to our work. In the revised manuscript, we supplemented the measurement\nof water contact angles on FEP surfaces after ammonia adsorption by droplets. It is\nimportant to clarify that due to the core-shell structure of the A-droplet and the\nassociated light refraction, as well as its non-standard elliptical shape, image-based\ncontact angle algorithms are not applicable to A-droplets. Therefore, conventional\ndroplets containing the same NH concentrations were used for contact angle\n3\nmeasurements. The results show that the introduction of ammonia causes a slight\ndecrease in the water contact angle, which continues to decline with increasing\nammonia concentration (Supplementary Fig. 19). This is attributed to the altered\npolarity of the solution upon ammonia dissolution, which affects the surface tension of\nthe droplet. The revised manuscript includes corresponding discussion as follows:\n“After ammonia absorption, the water contact angle of the droplet on the triboelectric\nsurface remains largely unchanged (Supplementary Fig. 19), effectively excluding the\ninfluence of wettability.”\n“Water contact angles were measured employing a contact angle goniometer (DSA35,\nKruss).”\n3. The authors are suggested to provide a discussion about the potential of the\nproposed ammonia sensing system for usage in practical situations, such as\ncollection of gas from targeted areas and implementation for sensing. It is also\nrecommended to provide the sensing data for gases collected from a real\nenvironment.\nResponse: We greatly appreciate the reviewer’s professional comments, which are of\ngreat help to our work. To evaluate the sensor’s potential for real-world applications,\nthe revised manuscript includes additional tests examining the effect of temperature on\noutput performance (Supplementary Fig. 24). Consistent with previous reports (Adv.\nFunct. Mater. 2021, 31, 2010220), temperature variations alter the contact angle of the\ndroplet, thereby influencing the contact electrification process and resulting in a\nreduction in output. However, the suppression rate remains below 10%, which is\n24\nconsidered acceptable. Furthermore, the revised manuscript also investigates the\ninfluence of ambient humidity on sensor output (Supplementary Fig. 25). Under high-\nhumidity conditions, surface charges on the material are more easily neutralized by\nwater molecules in the air, reducing the surface charge density and consequently\naffecting sensor performance. The signal deviations induced by environmental factors\ncan be addressed through compensation-based calibration strategies, offering a feasible\napproach to mitigate challenges in practical deployment (Nat. Commun. 2024, 15,\n6936). Related discussions have been added to revised manuscript as follows:\nSupplementary Fig. 24. Effect of temperature on sensor performance.\nSupplementary Fig. 25. Effect of humidity on sensor performance.\n“The sensor’s output remains stable under specified temperature and humidity\n25\nconditions (Supplementary Figs. 24 and 25), with signal deviations induced by\nenvironmental factors amenable to correction through compensation algorithms.”\n4. It seems that the authors haven’t provided the details about how they have\nchanged the concentration of ammonia gas in A-droplet for sensing data provided\nin Fig. 6b. In order to make the manuscript more informative and enrich its\nunderstanding, the authors should provide these experimental details.\nResponse: Thank you for the meticulous reviewing, which are of great help to our work.\nIn the revised manuscript’s Methods section, we have included additional details\nregarding the preparation of ammonia gas and the experimental procedures for loading\nammonia into A-droplets. Specifically, ammonia gas was generated by heating an\naqueous ammonia solution, followed by mixing with air at controlled volume ratios to\nobtain target concentrations. The resulting composite gas mixtures were then used as\nthe source for generating A-droplets, which were subsequently employed for sensing\nperformance tests. The detailed modifications are as follows:\n“Ammonia gas was generated via heated evaporation of aqueous ammonia and\nsubsequently stored in sample bags. Different concentrations of ammonia were\nprepared by mixing ammonia gas with air at predetermined ratios, with concentrations\nverified using an ammonia detector (JXBS-4001). The ammonia gas was then\nintroduced as the gas source into a co-flow microfluidic device for the generation of A-\ndroplets.”\n5. The authors are suggested to discuss the motivation for selecting Teflon as the\ntribo-layer and provide a systematic comparison of sensing performance for\ndifferent tribo-surfaces.\nResponse: We sincerely appreciate the reviewer’s professional suggestions and\n26\napologize for the oversight regarding details. In this work, we employed fluorinated\nethylene propylene (FEP), as illustrated in the subsequent question and Fig. 3b. The\nrevised manuscript includes an expanded discussion on the rationale for selecting FEP.\nSpecifically, FEP’s advantages—such as strong electron affinity, high surface charge\ndensity, and chemical inertness—make it an optimal choice for the triboelectric layer\nin liquid-solid triboelectric nanogenerators. Additionally, we supplemented\nexperiments evaluating the influence of triboelectric layers on sensing performance.\nSensors incorporating FEP, PTFE, and PDMS as the triboelectric layer were tested\nunder identical conditions. FEP demonstrated superior output, resulting in the best\ndetection limit and resolution. The output peak directly affects sensor sensitivity and\ndetection limits, with higher outputs facilitating clearer differentiation of signal changes\ninduced by varying ammonia concentrations. Conversely, PDMS exhibited the weakest\noutput, corresponding to lower resolution and detection limits for ammonia sensing\n(Supplementary Fig. 7). Relevant discussions have been added to the revised\nmanuscript as follows:\nSupplementary Fig. 7. Effect of triboelectric materials on sensing performance.\n“Fluorinated ethylene propylene (FEP) was selected as the triboelectric layer material\ndue to its superior sensing sensitivity, high resolution, and broad detection range\n27\n(Supplementary Fig. 7).”\n6. The authors are suggested to discuss the motivation for selecting Teflon as the\ntribo-layer and provide a systematic comparison of sensing performance for\ndifferent tribo-surfaces.\nResponse: We thank the reviewer for the meticulous evaluation and sincerely apologize\nfor our oversight. In this work, we employed FEP, as shown in Fig. 3b. In the revised\nmanuscript, we have corrected the inaccuracies in the Methods section accordingly.\nAdditionally, we conducted a thorough review of the entire manuscript and addressed\nfurther minor errors. Detailed revisions are as follows:\n“serving as the supporting substrate. Copper tape (100 μm thickness) and FEP tape\n(80 μm thickness) were sequentially adhered onto the acrylic plate,”\n“Fluorinated ethylene propylene (FEP) was selected as the triboelectric layer material\ndue to its superior sensing sensitivity, high resolution, and broad detection range\n(Supplementary Fig. 7).”\n“a gas sensor driven by an air-cavity droplet (A-droplet) was developed in this study”\n“The application of ammonia (NH₃) spans a wide range of sectors”\n“an A-droplet-driven ammonia gas sensor was developed in this work (Fig. 1bi)”\n“In contrast, the A-droplet is influenced by the interplay of its inner and outer gas-\nliquid interfaces and simultaneously experiences both upward and downward surface\ntensions.”\n“the A-droplet infused with ammonia carries a substantial ionic load”\n7. (a) Regarding the schematic presented in Fig. 6e related to deep learning, the\n28\nauthors are encouraged to briefly discuss the CNN model architecture employed,\ndetails about the datasets used, and the methodology applied to validate the\nclassification results in the present study. (b) Given that the dataset shown in Fig.\n6b appears relatively simple, the authors are requested to clarify why a complex\nmodel such as a CNN was chosen over traditional machine learning approaches\nlike SVM or Random Forest.\nResponse: We greatly appreciate the reviewer’s professional comments, which are of\ngreat help to our work. In the revised manuscript, we have supplemented discussions\non the CNN framework architecture, data specifics, and validation methods.\nAdditionally, we clarified the rationale behind selecting the CNN model. A point-by-\npoint response addressing the reviewer’s concerns has been provided. Detailed\nrevisions are as follows:\n(a) Supplementary discussion on the CNN framework architecture: The CNN model\nemployed in this work comprises an input layer, multiple convolution-pooling modules,\nfully connected layers, and a Softmax classifier. This hierarchical structure enables\nhigh-accuracy recognition of sensing signals through progressive feature extraction.\nThe dataset was constructed by processing response signals from varying ammonia\nconcentrations into images, with 70% of the data allocated for training and 30% for\ntesting. Model validation was performed by evaluating classification accuracy on the\ntest set, and model parameters were optimized through increased training iterations to\nenhance recognition performance. Relevant discussion has been added to the revised\nmanuscript as follows:\n“The model employs a hierarchical feature extraction architecture comprising an input\nlayer, multi-level convolutional and pooling modules, fully connected layers, and a\nSoftmax classifier, enabling ammonia concentration classification through progressive\n29\nextraction of time-frequency image features (Fig. 6e). The dataset consists of time-\nseries signals at varying concentration gradients (0 ppm, 50 ppm, 100 ppm, 150 ppm,\n200 ppm), which are processed and transformed into frequency-domain image\nrepresentations. Following systematic categorization by concentration, the data are\npartitioned into training and independent test sets at a 7:3 ratio (Supplementary Fig.\n26). With increasing training iterations, the model exhibits a high level of training\naccuracy (Supplementary Fig. 27), indicating stable convergence during training.\nValidation on the test set demonstrates that the trained model achieves a classification\naccuracy of 98.8% for ammonia concentration signals (Fig. 6f).”\n(b) Clarification on the CNN model: The sensor’s electrical response curves to varying\nammonia concentrations exhibit numerous subtle differences, such as peak amplitudes,\nslopes of rising and falling edges, waveform smoothness, and abrupt transitions.\nTraditional methods, such as Support Vector Machines (SVM), rely on manual feature\nextraction and may overlook these critical nuances. In contrast, the CNN model\nleverages convolutional operations to automatically learn fine-grained local features\nwithin the waveform images, enabling more precise capture of information correlated\nwith the sensor’s output.\n8. In Fig. 2a and Fig. 2c, the labeling for different components of TENG-P looks\nconfusing because the color of the tribo-layer is not consistent over Fig. 2a and Fig.\n2c. Please rectify this kind of mistake to enhance the readability of this manuscript.\nResponse: Thank you for the meticulous reviewing, which are of great help to our work.\nIn the revised manuscript, the triboelectric layer color has been standardized to purple,\nand the electrodes have been changed to yellow to clarify the sensor’s structural features.\nFig. 17. Detailed revisions are as follows:\nFig. 1 | b Schematic diagram of the structural features of the triboelectric droplet sensor\nand ammonia diffusion process.\nFig. 2 | a Schematic diagram of the sensor structure. b The effect of gas-liquid flow\nrate ratio on the shape and size of A-droplet. c Schematic diagram of the sensor's\nequivalent circuit.\nhigh-speed camera. b The working principle of the sensor. c Output curve\ncorresponding to the fluid morphology.\nFig. 4 | e Effects of A-droplet release height on output and stability. g Effects of TENG-\nP tilt angle on output and stability.\nFig. 6 | a Conceptual schematic of ammonia sensing for greenhouse detection, with an\n32\ninset showing the framework structure for combining sensing and deep learning.\nSupplementary Fig. 17. Force analysis of A-droplet.\n9. Regarding Fig. 2b, where the authors have shown a change in the size of A-\ndroplet upon change in the ratio of liquid and NH . However, despite the size of\n3\nthe A-droplet, the appearance of the A-droplet also looks different. The authors\nshould clarify that the changes in bright and dark areas are just a part of\nreflectance during photography or shape of air inside the A-droplet is changing.\nResponse: Thank you for the meticulous reviewing, which are of great help to our work.\nAs the reviewer pointed out, the differences in droplet appearance in Fig. 2b arise from\nlight reflections during photography. This phenomenon is attributed to the core–shell\ndouble-fluid structure of the A-droplet, where multiple gas-liquid interfaces induce\noptical refraction and reflection, resulting in irregular light and dark regions. Relevant\ndiscussion has been added in the revised manuscript as follows:\n“Due to thin-film interference caused by bubbles within the A-droplet, the captured\noptical images exhibit irregular light and dark patterns.”\n10. The authors are suggested to provide the analysis of surface morphology and\nchemical compositions of tribo-layer before and after the sensing of ammonia to\nshow the stability and reusability of the proposed sensor as the authors have shown\ngeneration of stable output over 100 cycles.\nResponse: We greatly appreciate the reviewer’s professional comments, which are of\n33\ngreat help to our work. In the revised manuscript, we supplemented analyses of the\nsurface morphology and chemical composition of FEP before and after ammonia\nsensing. No significant changes were observed in the surface morphology or chemical\ncomposition of FEP following ammonia exposure (Supplementary Fig. 22). Special\nattention was given to the nitrogen content to clarify whether ammonia adsorption\noccurred. Due to the chemical inertness of FEP, it does not react with ammonia.\nMoreover, the departing droplet carries away a substantial number of ions, resulting in\na nitrogen content on the FEP surface below 0.1% after sensing, which may be\nattributed to measurement instrument error. The stability of FEP’s surface structure and\nelemental composition indirectly confirms the sensor’s overall stability. Relevant\ndiscussion has been incorporated into the revised manuscript as follows:\nSupplementary Fig. 22. Surface morphology and elemental distribution of the\ntriboelectric layer before and after ammonia sensing.\n“As the A-droplet departs from the FEP surface, it carries away a substantial quantity\nof ions, resulting in negligible residual nitrogen on the FEP surface (Supplementary\nFig. 22). The structural and elemental stability of the FEP surface indirectly\ncorroborates the sensor’s output consistency.”\n“Surface morphology and elemental composition of the materials were characterized\nusing a scanning electron microscope coupled with energy-dispersive X-ray\nspectroscopy (GeminiSEM 300, ZEISS).”\n34"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors responded to my comments. I recommend for a publication.", "reviewer_2": "I have doubts about the data presented in the paper. The reported response time has been drastically revised from a claimed 2.1ms to 1400ms, a 666-fold increase. Furthermore, the critical advantage of TENG stability is undermined by inaccurate representation and flawed experimental design. Figure 2f, purporting to show stability testing, appears to have been conducted under conditions significantly different from those simulating the claimed high-speed response time 1400ms. The response time and stability tests were performed under disparate experimental setups. High-speed droplet impact, which can affect TENG stability (through thermal effects, material wear, interfacial charge accumulation and dissipation kinetics), was not adequately considered in the stability testing. I do not recommend publication in Nature Communications.\n1.The stability test in Figure 2f exhibits a significantly different droplet impact frequency compared to the \"response time\" tests in Figures 2d and 2e. Judging from the waveforms in 2d and 2e, the response time tests appear to have been conducted at a rate of approximately three droplets per second, representing a relatively high droplet impact rate. However, the stability test in Figure 2f, estimated from the data points, shows approximately 22 droplets impacting the sample over a 150-200 second period. This translates to an average droplet impact frequency significantly below one per second, starkly contrasting with the implied high-speed impact in the response time tests. This disparate droplet impact frequency across different testing phases constitutes a serious methodological flaw. Figures 3d, k, 4e-h, and 6b suffer from the same issue.\nFurthermore, high-speed video of the electrical testing with droplets impacting the FEP membrane is required to ensure no interference between successive droplets.\n2. Why does the ammonia concentration in the water within the tube stabilize after 1 second (Fig. S4)? This conclusion requires verification with precise ammonia concentration detection equipment.\n3. The consistency shown in Figure 3i, with only 10 cycles, is far from sufficient. As a sensor, it needs to operate continuously for a much longer duration.\n4.The molecular dynamics simulations in Figure 5 only demonstrate the principle of ammonia concentration sensing; they do not provide a rationale for its high-speed and stable sensing capabilities.\n5. The paper's application of AI, while presented, lacks practical, real-world testing. There are no circuit, physical images, or demonstration videos. The innovative aspect of the AI is unclear. The CNN model architecture is not explained. The dimensionality of the input data is unknown. Real-world scenarios involve variations in ambient temperature, humidity, and atmospheric conditions throughout the day and night. More physical photographs and videos are needed to demonstrate the sensor's practical utility.\n6. Why does the sensor output remain constant despite changes in ambient temperature, given that the solubility of ammonia in water is temperature-dependent?\n7. The video that has already been shown does not show a physical image of the sensor, nor does it display the electrical signal output.", "reviewer_3": "The authors have responded to all the comments in the manuscript's revised version; however, some points still need to be provided with more details. The following are some suggestions before finalizing this manuscript:\n1. Regarding the comment No. 1 in the first revision, the triboelectric current data provided in Fig. R9 and R10 for without A-droplet, also needed to be include a zooming view which show the clarity of the data, such as it contains any peak with the blow of air or just a kind of noise. Moreover, these comparison data should be included in the supporting information for a detailed explanation, which is why it is essential to introduce an A-droplet.\n2. Regarding comment No. 2 in the first revision, the authors are suggested to include the pictures of A-droplets on FEP used for measurement of water contact angle (Supplementary Fig. 19) to enrich the visualization of the behaviour of A-droplets on FEP film with variation in the amount of ammonia.\n3. Regarding comment No.3 in the first revision, it seems the authors misunderstood the suggestion. The intent of the comment No. 3 was obvious: if there is a leak of ammonia gas in the real environment, how will the authors realize its sensing via A-droplet and TENG-P? It is worth detailing the possible process of making an A-droplet from the ammonia in a real and open environment, to demonstrate the potential of the proposed technique in the real world."}, "author_response": "Point-by-point Response to Reviewers' Comments\nReviewer #1 (Remarks to the Author):\nThe authors responded to my comments. I recommend for a publication.\nResponse: We highly appreciate the reviewer for the professional opinions and\nsuggestions, which have greatly helped us to improve the quality of our manuscript. We\nalso appreciate your acknowledgment of our work and the acceptance of our manuscript\nin Nature Communications.\nReviewer #2 (Remarks to the Author):\nI have doubts about the data presented in the paper. The reported response time has\nbeen drastically revised from a claimed 2.1ms to 1400 ms, a 666-fold increase.\nFurthermore, the critical advantage of TENG stability is undermined by inaccurate\nrepresentation and flawed experimental design. Figure 2f, purporting to show stability\ntesting, appears to have been conducted under conditions significantly different from\nthose simulating the claimed high-speed response time 1400 ms. The response time and\nstability tests were performed under disparate experimental setups. High-speed droplet\nimpact, which can affect TENG stability (through thermal effects, material wear,\ninterfacial charge accumulation and dissipation kinetics), was not adequately\nconsidered in the stability testing. I do not recommend publication in Nature\nCommunications.\nResponse: We deeply regret the errors in data presentation and the oversights in our\nexperimental design, and we extend our most sincere apologies. The reviewer’s overall\ncomments and the seven specific questions have provided us with highly valuable\nguidance for improving our work, for which we express our genuine appreciation and\nagreement. We sincerely ask for another opportunity to allow us to further review.\nRegardless of your final decision, we are truly grateful for your feedback, as it has\nreminded us of the importance of treating our work with utmost care. Our response can\nbe generally summarized in two main aspects.\n(1) Regarding your primary concern about the “reservations over the data presented in\n1\nthe manuscript,” particularly the correction of the response time from 2.1 ms to 1400\nms and issues related to TENG stability, we have made a series of substantial revisions\nto address these points. Concerning the consistency of data after correcting the response\ntime, we realized that in our first revision, we failed to update all datasets when\nsupplementary experiments were added, resulting in discrepancies under certain test\nconditions. To rectify this, we have retested all relevant data in the manuscript\n(including Figs. 2d-f, 3d, 3j-k, 4e-f, and Supplementary Figs. S5, S6, S8, S10, etc.)\nunder consistent operating frequencies to ensure experimental uniformity. Regarding\nthe definition of sensor response time: The initial submission did not clearly distinguish\nthe physical meaning of different response times, which may have led to\nmisunderstandings, for which we sincerely apologize. In the revised manuscript, we\nhave explicitly clarified this distinction: 2.1 ms is defined as the liquid–solid contact\nelectrification response time (reflecting the instantaneous charge generation process of\nthe TENG itself), while 1.4 s is defined as the sensor’s specific response time to\nammonia gas (representing the complete ammonia-sensing process). This distinction\naligns with conventional standards in the gas-sensing field, as seen in Nano Energy,\n2019, 63: 103829, and Nano Letters, 2025, 25(43): 15585-15593. In these studies,\n“contact electrification response time” is used to describe the intrinsic response\ncapability of TENGs, while “gas response time” characterizes the full interaction cycle\nbetween the target gas and the sensing interface, ensuring consistency between our\ndescription and the consensus of the field.\n(2) Regarding your conclusion that the manuscript is not suitable for publication in\nNature Communications, we understand that this assessment was based on the various\nshortcomings previously present in the submission. After several months of reflection,\nwe have systematically addressed core issues related to data consistency, experimental\ndesign flaws, and stability verification, and have carefully revised each of the seven\nspecific concerns you raised. These revisions include: standardizing testing conditions,\nfurther validating ammonia concentrations through precise experiments, supplementing\nsimulations of response hysteresis, clarifying distinctions between sensor stability and\nhigh-speed response, providing comprehensive photographs of the device and circuit\n2\ndiagrams, elaborating on the CNN model architecture, discussing ammonia sensing\nunder varying environmental temperatures, and including videos showing both the\nsensor in operation and the corresponding electrical signal outputs. We believe the\nrevised manuscript has achieved substantial improvements in scientific soundness,\nrigor, and completeness.\nOnce again, we sincerely thank you for your meticulous review and constructive\nfeedback. Your valuable comments have greatly guided and motivated us to enhance\nour research. We earnestly request that you reconsider the revised manuscript and hope\nit may now meet your expectations. We also remain open to further improvements based\non any additional feedback you may provide.\n1. The stability test in Figure 2f exhibits a significantly different droplet impact\nfrequency compared to the \"response time\" tests in Figures 2d and 2e. Judging from the\nwaveforms in 2d and 2e, the response time tests appear to have been conducted at a rate\nof approximately three droplets per second, representing a relatively high droplet\nimpact rate. However, the stability test in Figure 2f, estimated from the data points,\nshows approximately 22 droplets impacting the sample over a 150-200 second period.\nThis translates to an average droplet impact frequency significantly below one per\nsecond, starkly contrasting with the implied high-speed impact in the response time\ntests. This disparate droplet impact frequency across different testing phases constitutes\na serious methodological flaw. Figures 3d, k, 4e-h, and 6b suffer from the same issue.\nFurthermore, high-speed video of the electrical testing with droplets impacting the FEP\nmembrane is required to ensure no interference between successive droplets.\nResponse: We thank the reviewer for your thorough review. The methodological flaws\nyou identified have made us deeply aware of the shortcomings in our experimental\n3\ndesign. We sincerely regret these oversights and extend our heartfelt apologies. We have\ntaken substantial measures to address these issues comprehensively. First, we examined\nthe influence of working frequency on the output performance (Supplementary Fig. 7).\nThe results indicate that the working frequency has an insignificant effect on the output\nof the triboelectric droplet sensor. In addition, following the reviewer’s suggestion, we\nrecorded a high-speed video of an A-droplet impacting the FEP film (Supplementary\nMovie 2). The video was captured at 2500 frames per second, clearly showing the entire\nprocess of droplet impact, spreading, and rebound. Representative snapshots from the\nvideo are provided in Fig. R1. The experimental results show that even at a working\nfrequency of 2.5 Hz, A-droplets remain spatially separated, meaning that consecutive\ndrops do not interfere with each other. This finding is crucial for demonstrating the\nsensor’s operational stability at higher frequencies. Similar observations have been\nreported in related studies (e.g., Nature, 578, 392–396 (2020)), where stable outputs\nwere obtained at working frequencies between 1–3 Hz. The independent droplet cycles\nensure the effectiveness of contact electrification, suggesting that multiple testing\nfrequencies can yield consistent results comparable to those in this study.\nTo completely resolve this issue, we carefully verified and retested all relevant data in\nthe manuscript. As the reviewer noted, the droplet falling frequency during response\ntime measurement was approximately three drops per second. Upon rechecking, we\nconfirmed that the actual testing frequency was 2.5 Hz. Based on this, we retested all\ncorresponding data, including Figs. 2d-f, 3d, 3j-k, 4e-f, and Supplementary Figs. S5,\nS6, S8, and S10. To further clarify this issue, we have added a statement regarding the\n4\nworking frequency to the Experimental Methods section. In summary, we hope that\nthese substantial revisions will completely resolve the problem and gain the reviewers'\napproval. Thank you again for your detailed review and constructive comments. The\ndetailed revisions are as follows.\nSupplementary Fig. 7 | Effect of top electrode materials on sensor performance.\nFig. R1 | Photograph of the sensor in operation.\nsensor output. f Output curve of the sensor under long-term cycle.\nFig. 3d. Effect of electrode size on output.\nFig. 3 j, k Signal deviation rates of droplet and A-droplet driven TENG-P.\nTENG-P tilt angle on output and stability.\nSupplementary Fig. 5 | Effect of droplet release frequency on the ammonia sensing\nresponse time.\nSupplementary Fig. 8 | Ammonia response performance of the A-droplet.\nSupplementary Fig. 10 | Effect of top electrode materials on sensor performance.\n“Supplementary Movie 2 visually presents the real-time process of output generation\n8\ndriven by the A-droplet and demonstrates the sensor’s continuous operation at a\nfrequency of 2.5 Hz.”\n“The operating frequency exerts no significant influence on the sensor’s output,\nindicating the feasibility of multiple working conditions (Supplementary Fig. 7).”\n“Unless otherwise specified, the operating frequency of the A-droplet is maintained\nat 2.5 Hz.”\n2. Why does the ammonia concentration in the water within the tube stabilize after 1\nsecond (Fig. S4)? This conclusion requires verification with precise ammonia\nconcentration detection equipment.\nResponse: We thank the reviewer for your meticulous review and will respond point\nby point to provide a complete answer to this question.\n(1) Ammonia Concentration\nWe apologize for the unclear expression. Our manuscript did not claim that the\nammonia concentration stabilized after 1 second. Fig. S4 was intended to illustrate that\nthe thermal motion of ammonia molecules within the pipe created a concentration\ndifference. This graph supports the explanation for the sensor's response hysteresis,\nnamely, that the concentration gradient of ammonia within the pipe causes the\nhysteresis. To further clarify this issue, we further verified this phenomenon through\nsimulation (Fig. S4). Simulating the diffusion process of ammonia within the pipe, with\nammonia introduced from the left side, we observed that the ammonia gradually\ndiffused to the right, exhibiting a significant velocity gradient. This velocity gradient\n9\nrepresents the diffusion rate of ammonia; that is, the faster the diffusion rate, the more\neasily ammonia accumulates in the region. This further supports our conclusion, and\nthe relevant results have been updated in the revised manuscript's Fig. S4. The\ndiscussion has also been corrected in the revised manuscript. Detailed revisions are as\nfollows.\nSupplementary Fig. 4 | Schematic illustration of ammonia diffusion within the gas\ndelivery tubing.\n“The response hysteresis of the sensor mainly arises from concentration gradient\nvariations within the tube, caused by the thermal motion of ammonia molecules\n(Supplementary Fig. 4).”\n(2) Verification of Ammonia Concentration\nBased on the reviewers' suggestions, we further verified the ammonia concentration\nusing the international standard (ISO 7150-1:1984). We collected ammonia gas in\nchronological order from the other side of the pipeline as test samples. The collected\n10\nammonia gas was then reacted with alkaline Nessler's reagent to generate a yellow to\nbrown complex, the color intensity of which was proportional to the ammonia\nconcentration. The absorbance was then measured using a spectrophotometer to\nquantitatively analyze the ammonia content. The detection accuracy reached 1 ppm,\nwhich was sufficient to meet the detection requirements of this experiment. It can be\nseen that the absorbance of the solution increases as it approaches the ammonia inlet\n(Figure R2). The ammonia content was then calculated using the formula: (NH )\n3\n=[(A-A -a)·V ]/(b·V ·V ). Where A is the absorbance of the sample, A is the\n0 s nd 0 0\nabsorbance of the blank sample, a is the intercept of the calibration curve, b is the slope\nof the calibration curve, V is the total sample volume, V is the gas sample volume,\ns nd\nand V is the analytical liquid volume. After further unit conversion, the ammonia\n0\nconcentration met the range required for the study. The experimental results further\nsupport our conclusion that a significant concentration gradient of ammonia exists\nwithin the pipeline.\nFig. R2 | Absorbance corresponding to different ammonia concentrations.\n11\n3. The consistency shown in Figure 3i, with only 10 cycles, is far from sufficient. As a\nsensor, it needs to operate continuously for a much longer duration.\nResponse: We appreciate the reviewer’s meticulous examination, which helped us\nfurther refine the manuscript. In the revised manuscript, we increased the number of\ntests (Figs. 3h-i). Consistent with the initial data, the deviation rate of the A-droplet’s\nspreading area was only 1.9%, significantly lower than that of the Droplet (8.3%).\nDuring the liquid–solid contact electrification process, the droplet’s impact–spreading\narea determines the effective contact area, thereby influencing the output deviation of\nthe sensor. This finding supports the superior stability of A-droplets in driving the\nliquid–solid contact electrification process, and the corresponding results have been\nupdated in Figs. 3h-i of the revised manuscript. It is worth noting that this data\ndemonstrates the repeatability of liquid spreading, serving as a characterization of A-\ndroplet contact-electrification stability rather than long-term stability. The dedicated\nstability test in this study was performed through cyclic operation, with corresponding\nresults presented in Figs. 2f and 4h. The data in Fig. 3i were obtained by recording\n12\ndroplet spreading areas and processing the images with analysis software. Given that\n50 cycles have already demonstrated the stable spreading behavior of A-droplets,\nfurther increasing the number of cycles would not significantly enhance the strength of\nthe evidence. We hope this response satisfactorily addresses the reviewer’s concerns.\nOnce again, we sincerely thank you for your professional and valuable feedback, with\ndetailed revisions listed below.\nFig. 3 | h, i Deviation rates of droplet and A-droplet diffusion areas.\n4. The molecular dynamics simulations in Figure 5 only demonstrate the principle of\nammonia concentration sensing; they do not provide a rationale for its high-speed and\nstable sensing capabilities.\nResponse: We appreciate the reviewer’s meticulous examination, which helped us\nfurther refine the manuscript. In the revised manuscript, we increased the number of\ntests (Figs. 3h-i). Consistent with the initial data, the deviation rate of the A-droplet’s\nspreading area was only 1.9%, significantly lower than that of the Droplet (8.3%).\nDuring the liquid–solid contact electrification process, the droplet’s impact–spreading\narea determines the effective contact area, thereby influencing the output deviation of\nthe sensor. This finding supports the superior stability of A-droplets in driving the\nliquid–solid contact electrification process, and the corresponding results have been\nupdated in Figs. 3h-i of the revised manuscript. It is worth noting that this data\ndemonstrates the repeatability of liquid spreading, serving as a characterization of A-\ndroplet contact-electrification stability rather than long-term stability. The dedicated\nstability test in this study was performed through cyclic operation, with corresponding\nresults presented in Figs. 2f and 4h. The data in Fig. 3i were obtained by recording\n13\ndroplet spreading areas and processing the images with analysis software. Given that\n50 cycles have already demonstrated the stable spreading behavior of A-droplets,\nfurther increasing the number of cycles would not significantly enhance the strength of\nthe evidence. We hope this response satisfactorily addresses the reviewer’s concerns.\nOnce again, we sincerely thank you for your professional and valuable feedback, with\ndetailed revisions listed below.\n“Building on the stability optimization of the A-droplet and an understanding of its\nunderlying principles, it is crucial to further elucidate the mechanism of its response\nto ammonia.”\nTo address this issue in detail, our point-by-point response is as follows:\n(1) Molecular Simulation\nAs the reviewer noted, the molecular simulation in Fig. 5 is only used to support the\nprinciple of ammonia sensing, specifically to analyze the principle of ammonia\nadsorption and dissolution in A-droplets. Fig. 5b, through molecular simulation analysis\nof the direct molecular interaction energy between water and ammonia, reveals that the\ninteraction energies of H O-H O, NH -H O and NH -NH are -4.53 kcal mol-1, -7.06\n2 2 3 2 3 3\nkcal mol-1, and -3.41 kcal mol-1, respectively. This data demonstrates that ammonia\nreadily combines with water and supports the absorption of ammonia by A-droplets.\nSubsequently, we further simulated the interaction energies of water with other gases\n(Fig. S21). The results show that the interaction energies of water molecules with CH ，\n2 2 2\n14\nmol-1, and -1.66 kcal mol-1, respectively. This data demonstrates that the interaction\nenergy between other gas molecules and water is much lower than that of ammonia,\nindicating that ammonia combines with water more readily than other gases. In\nsummary, molecular simulations are primarily used to support the sensor's response\nprinciple to ammonia.\nFig. 5b Molecular simulations of NH and H O.\n3 2\nSupplementary Fig. 21 | Molecular numerical simulation of gas and water.\n(2) Sensor Stability\nThe stability of the sensor mainly stems from the hydrodynamic characteristics of the\nA-droplet, for which two pieces of evidence are provided in the manuscript. One is that\nthe A-droplet possesses a more stable diffusion surface (Fig. 3e). Analysis of high-speed\ncamera recordings revealed that the A-droplet has a more stable diffusion surface, thus\n15\nsuppressing Kelvin-Helmholtz instability. Electrostatics at the liquid-solid contact are\nsignificantly affected by the diffusion area of the liquid surface, and the more stable\ndiffusion surface supports the sensor's stability advantage. The other is that the A-\ndroplet can suppress droplet bouncing and breakup (Fig. 4b). High-speed camera\nrecordings and simulations revealed that the reverse convection of the fluid in the A-\ndroplet suppresses droplet elasticity, thereby limiting breakup. Droplet breakup leads\nto mass loss and is accompanied by unstable droplet fluctuations, which are also key\nfactors affecting sensor bias.\nFig. 3e Characteristic images of droplet and A-droplet impact diffusion.\nFig. 4b Characteristic images and fluid simulation of A-droplet impact rebound.\n16\n(3) High-Speed Response\nThe sensor's high-speed response stems from the in-situ, instantaneous response\ncapability of liquid-solid contact electrification. The impact of a droplet on a dielectric\nsurface typically occurs in a single instant, thus we detected an instantaneous response\ncurrent of 2.1 ms (Fig. S3). This rapid response capability is a fundamental advantage\nof liquid-solid TENGs and is commonly reported in numerous publications, such as Nat.\nCommun. 16, 5524 (2025), Nature 578, 392-396 (2020). This work utilizes the high-\nspeed response capability of liquid-solid TENGs to achieve efficient ammonia sensing.\nThe ammonia-loaded A-droplet achieves a response within a few cycles, with a single\ncycle taking only milliseconds. This gives the ammonia sensor proposed in this study\nan advantage in response time.\nIn summary, we once again sincerely thank the reviewer for the thorough and\nprofessional evaluation. We hope that our explanation satisfactorily addresses your\nconcerns and earns your approval.\nSupplementary Fig. 3 | Electrical signal response time of the TENG-P.\n5. The paper's application of AI, while presented, lacks practical, real-world testing.\nof the AI is unclear. The CNN model architecture is not explained. The dimensionality\nof the input data is unknown. Real-world scenarios involve variations in ambient\ntemperature, humidity, and atmospheric conditions throughout the day and night. More\nphysical photographs and videos are needed to demonstrate the sensor's practical utility.\nResponse: We sincerely thank the reviewer for their thorough evaluation, which has\nhighlighted the shortcomings of our work in terms of practical validation and detailed\npresentation. In response to the reviewer’s suggestions, Fig. 6 in the revised manuscript\nhas undergone substantial revision. It now presents both the physical device and a\nschematic of the circuitry. Supplementary Movie 7 demonstrates the device operating\nin\noperating in real-world conditions. The Methods section has been updated to clearly describe the\narchitecture of the convolutional neural network model. Additionally, Supplementary\nSupplementary Fig. 24 verifies the sensor’s stability over 192 hours under ambient\nconditions, with testing encompassing diurnal cycles, including variations in\ntemperature and humidity. Detailed revisions are as follows:\nTechnical framework of the ammonia sensing platform. b Photograph of the\nammonia sensing platform, with an inset illustrating the schematic of the wireless\nsensor and electronic architecture. c Schematic of the wireless connection between\nthe sensor and the intelligent display device. d Demonstration photograph of the\nplatform in application. e Wireless response curves. f Confusion matrix for ammonia\nconcentration identification results.\n“By optimizing the gas injection setup, the sensor can be directly interfaced with the\nambient environment, highlighting its potential for applications in open-air\nammonia detection (Supplementary Fig. 23). Continuous monitoring over 192 hours\nwas conducted to investigate the influence of varying temperature and humidity in\nopen environments on the sensor performance (Supplementary Fig. 24).”\n“Supplementary Movie 7 visualizes the wireless sensing capability of the platform,\nwith the intelligent display device providing real-time responses without latency.”\nWe provide a point-by-point response to comprehensively address the reviewer’s\ncomments.\n(1) Photographs and Circuit Diagrams of the Device\nIn the revised manuscript, Figs. 6b and 6c present photographs of the device, which\nintegrates the liquid–solid TENG and the Bluetooth circuit module, along with a\nschematic illustrating the electronic logic architecture. Furthermore, Figs. 2a-c of the\nmanuscript show the structural diagram and circuit schematic of TENG in more detail.\nschematic of the wireless sensor and electronic architecture. c Schematic of the\nwireless connection between the sensor and the intelligent display device.\nFig. 2 | a Schematic diagram of the sensor structure. b The effect of gas-liquid flow\nrate ratio on the shape and size of A-droplet. c Schematic diagram of the sensor's\nequivalent circuit.\n“A coaxial injector, a triboelectric droplet sensor, and a wireless sensing module are\nintegrated into a single ammonia sensing platform, with the sensor directly connected\nto the wireless module (Fig. 6b). When the A-droplet is generated by the top coaxial\ninjector and released downward, the electrical signal produced by the triboelectric\n21\ndroplet sensor enters a signal modulation circuit composed of transistors and\nresistors. The circuit adjusts the signal amplitude before it is transmitted to the\nanalog-to-digital converter (ADC) of a microcontroller, converting multi-channel\nanalog signals into digital form for processing by the central processing unit. Finally,\nthe Bluetooth module transmits the processed digital signals in real time to intelligent\ndisplay devices at high transmission rates (Fig. 6c).”\n(2) Convolutional Neural Network\nIn the revised manuscript, the Methods section has been supplemented with a detailed\ndiscussion of the deep learning framework to enhance methodological clarity.\nSpecifically, a convolutional neural network (CNN) was employed to identify and\nclassify subtle deviations and complex features in the signal data. The model utilizes a\nhierarchical feature extraction architecture, comprising an input layer, multiple\nconvolutional and pooling modules, fully connected layers, and a a-Softmax classifier,\nenabling progressive extraction of image features for ammonia concentration\nclassification. The dataset consists of 6,000 images, partitioned into training, validation,\nand test sets in a 6:3:1 ratio. It should be noted that the AI component is intended to\ndemonstrate the sensor’s potential for interdisciplinary applications rather than to\nintroduce methodological innovation. The primary novelty of this work lies in\nachieving ammonia detection through contact electrification of droplets, rather than in\nthe AI implementation. This approach reflects the core contribution of the study, as\ninnovation in other areas is not the focus, which is common across most research efforts.\n22\n“Deep Learning”\n“Signal classification was implemented using the YOLO11 algorithm within the\nUltralytics framework, employing a convolutional neural network (CNN)\narchitecture to analyze the time–frequency images derived from triboelectric signals.\nThe model architecture comprises an initial convolutional layer for feature\nextraction across three stages, with C3k2 and C2PSA modules enabling multi-scale\nfeature fusion, and a final Classify layer outputting five ammonia concentration\nlevels. The dataset consists of 6,000 images, partitioned into training, validation,\nand test sets in a 6:3:1 ratio. The YOLO11 classification model was configured for\ntransfer learning based on the pre-trained YOLO11x-cls weights, successfully\ntransferring 234 out of 236 weight parameters. Training was conducted on an\nNVIDIA RTX 4070 GPU for 100 epochs, employing an adaptive learning rate\nschedule to optimize convergence, with eight data-loading threads for parallel\nprocessing and a batch size of 16. The output layer utilizes a Softmax activation\nfunction to classify five ammonia concentration levels (0, 50, 100, 150, 200 ppm), and\nclassification accuracy was optimized using the cross-entropy loss function.”\n(3) Experimental Validation under Real-World Conditions\nAs demonstrated in Supplementary Movie S7, the sensor was tested under real-world\nconditions, with Fig. R3 showing representative operational snapshots. These\nexperiments confirm that the sensor possesses fundamental applicability in practical\nenvironments. In response to the reviewer’s suggestions, we additionally conducted\n23\nlong-term stability tests (Supplementary Fig. 24). The sensor’s output was monitored\nover 192 hours under outdoor conditions at 12-hour intervals, encompassing varying\ndiurnal cycles. During the testing period, humidity fluctuations reached approximately\n28%RH, and the temperature difference between day and night was around 2.2 ℃. The\nresults indicate that environmental variations under different diurnal conditions induced\na sensor deviation of less than 8.4 %. Notably, calibration strategies for humidity and\ntemperature have been proposed in previous reports (Nat. Commun. 15, 6936 (2024)).\nIn the revised manuscript, we have updated the data and discussion accordingly and\nprovided potential solutions. We sincerely thank the reviewer for their professional\ninsights and hope that our responses will meet with their approval. Detailed revisions\nare as follows:\nFig. R3 | Photograph of the sensor.\n“Continuous monitoring over 192 hours was conducted to investigate the influence\nof varying temperature and humidity in open environments on the sensor\nperformance (Supplementary Fig. 24).”\n“Notably, these environmentally induced signal deviations can be further corrected\nthrough algorithmic calibration5.”\n“5 Lv, W. et al. Wide-range and high-accuracy wireless sensor with self-humidity\ncompensation for real-time ammonia monitoring. Nat. Commun. 15, 6936 (2024).”\n6. Why does the sensor output remain constant despite changes in ambient temperature,\ngiven that the solubility of ammonia in water is temperature-dependent?\nResponse: We sincerely thank the reviewer for their careful evaluation and apologize\nfor the previously unclear statements. As noted by the reviewer, we had not fully\nconsidered the temperature-dependent solubility of ammonia, which is critical to\nunderstanding the observed decrease in output. In Fig. S25 of the manuscript, we\nmeasured the sensor’s response at various temperatures, revealing a deviation of 12.3%\nunder different thermal conditions. To address this oversight, we have revised the\ncorresponding discussion in the manuscript and further clarified this phenomenon. Fig.\neven at 50 ℃, the solubility reaches 14.0 g NH / 100 g H O. For an A-droplet with a\n3 2\nmass of approximately 35 mg, this corresponds to an absorption capacity of roughly\n4.9 mg of ammonia. In other words, an A-droplet can absorb ammonia at concentrations\nup to approximately 13,000 ppm, far exceeding the levels tested experimentally. This\ndata further confirms the feasibility of ammonia sensing under the experimental\nconditions of this study. Nonetheless, it does not entirely exclude the potential influence\nof temperature on ammonia absorption. Therefore, in the revised manuscript, we\ncontinue to list ammonia solubility as a contributing factor. Detailed revisions are as\nfollows:\nSupplementary Fig. 25 | Effect of temperature on sensor performance.\n“The sensor shows an output deviation of less than 10% within the 0-50 °C range\nand less than 15% under 50-90% RH (Supplementary Figs. 25 and 26), with signal\nvariations likely arising from changes in ammonia solubility and humidity-induced\nsuppression of contact electrification.”\n7. The video that has already been shown does not show a physical image of the sensor,\nnor does it display the electrical signal output.\nResponse: We thank the reviewer for their careful scrutiny and apologise for the\noversight. Substantive measures have been implemented to rectify the issue. In the\nrevised manuscript, we provide a high-speed camera recording of the liquid–solid\nTENG in operation (Supplementary Movie 2); the video also displays the TENG’s real-\ntime output signal waveform. Representative frames are shown in Fig. R1. Furthermore,\nwe re-captured macroscopic images of the sensor assembly (Supplementary Movie 7),\nencompassing the injection system, the TENG device, and the display devices.\nCorresponding video frames are presented in Fig. R3. We once again extend our sincere\n27\nthanks to the reviewer for the thorough evaluation and sincerely hope that these\nrevisions will earn their approval. The detailed modifications are as follows.\nFig. R1 | Photograph of the sensor in operation.\nFig. R3 | Photograph of the sensor.\n“Supplementary Movie 2 visually presents the real-time process of output generation\ndriven by the A-droplet and demonstrates the sensor’s continuous operation at a\nfrequency of 2.5 Hz.”\n“Supplementary Movie 7 visualizes the wireless sensing capability of the platform,\nwith the intelligent display device providing real-time responses without latency.”\nThe authors have responded to all the comments in the manuscript's revised version;\nhowever, some points still need to be provided with more details. The following are\nsome suggestions before finalizing this manuscript:\nResponse: We sincerely extend our gratitude to the reviewers for their meticulous\nevaluation of our work in both the previous and current rounds. We are also deeply\nappreciative of the reviewers’ acknowledgment that all comments have been thoroughly\naddressed in the revised manuscript. Your affirmation has greatly strengthened our\nconfidence in advancing this research. In response to the three specific suggestions\nraised by the reviewers—including the enlarged view of data without A-droplet, the\nphotographic evidence of water contact angle measurements, and the discussion on the\nfeasibility of ammonia detection under real open-environment conditions—we have\nconducted additional targeted experiments. The corresponding data have been fully\nincorporated into the revised manuscript and Supporting Information, with detailed\nresponses provided in the respective sections.\n1. Regarding the comment No. 1 in the first revision, the triboelectric current data\nprovided in Fig. R9 and R10 for without A-droplet, also needed to be include a zooming\nview which show the clarity of the data, such as it contains any peak with the blow of\nair or just a kind of noise. Moreover, these comparison data should be included in the\nsupporting information for a detailed explanation, which is why it is essential to\nintroduce an A-droplet.\nduring the first round of review, which has materially aided the refinement of our work.\nWe have expanded the current traces presented in Figs. R9 and R10 and included\nenlarged inset views to highlight finer details. These enlarged traces reveal only minor,\nstochastic fluctuations in the current signal, with no discernible, blowing-induced\ncharacteristic peaks; moreover, the magnitude of these fluctuations remains within the\ninstrument’s background noise level. The experimental evidence therefore substantiates\nthe necessity of introducing an A-droplet for reliable ammonia sensing, in agreement\nwith the reviewer’s recommendation. In the revised submission, Figs. R9 and R10 have\nbeen relocated to the Supporting Information as Fig. 8, and we have added a\ncorresponding discussion in the main text to justify the inclusion of the A-droplet. We\nsincerely thank the reviewers once again for their meticulous evaluation and hope to\nreceive the reviewers’ approval. The detailed revisions are as follows.\nSupplementary Fig. 8 | Ammonia response performance of the A-droplet.\n“Moreover, direct blowing of ammonia does not elicit any sensor response,\nunderscoring the necessity of introducing the A-droplet (Supplementary Fig. 8).”\n30\n2. Regarding comment No. 2 in the first revision, the authors are suggested to include\nthe pictures of A-droplets on FEP used for measurement of water contact angle\n(Supplementary Fig. 19) to enrich the visualization of the behaviour of A-droplets on\nFEP film with variation in the amount of ammonia.\nResponse: We gratefully acknowledge the reviewer’s careful scrutiny, which has\nmaterially contributed to the improvement of this work. In the revised manuscript’s\nSupporting Information we have appended photographic evidence from the water\ncontact-angle measurements (Supplementary Fig. S20). It should be noted that the A-\ndroplets do not adopt a canonical elliptical profile, which can introduce recognition\nerrors in automatic contact-angle analysers. To eliminate this artefact, we measured the\nwater contact angle using conventional droplets under identical ammonia-adsorption\nconditions. These measurements were intended solely to verify whether ammonia\nuptake by the solution alters the wettability of the dielectric surface; no substantive\ndifference was observed between measurements made with ordinary droplets and those\nusing hollow droplets. This approach and its rationale were explained in our first-round\nresponse and have now been explicitly stated in the main text. We thank the reviewer\nagain for the thorough review. Detailed revisions are as follows.\n“After ammonia absorption by the solution, the water contact angle on the dielectric\nsurface exhibits no significant deviation (Supplementary Fig. 20), thereby excluding\nthe influence of wettability on liquid–solid contact electrification.”\n3. Regarding comment No.3 in the first revision, it seems the authors misunderstood\nthe suggestion. The intent of the comment No. 3 was obvious: if there is a leak of\nammonia gas in the real environment, how will the authors realize its sensing via A-\ndroplet and TENG-P? It is worth detailing the possible process of making an A-droplet\nfrom the ammonia in a real and open environment, to demonstrate the potential of the\nproposed technique in the real world.\nResponse: We thank the reviewer for the careful evaluation and apologise for our\nearlier inaccurate reply. Prompted by the reviewer’s detailed exposition, we concur that\nit is essential to clarify the sensor’s feasibility for detecting and responding to ammonia\nleakages in open environments. Revised Fig. S23 presents the experimental setup for\natmospheric measurements. In this study, A-droplets were fabricated using a coaxial\ninjection system, in which the ammonia/air stream was supplied through an\nindependent pumping unit. In the most recent experiments we employed a dispensing\nperistaltic pump (BT100-J) as the air-delivery component of the coaxial injector; the\npump outlet was positioned directly in the ambient environment, demonstrating the\npracticability of sensing ammonia concentrations in an open setting. The inset of Fig.\nS23 includes an optical image of an A-droplet produced by the peristaltic pump and the\n32\ncorresponding sensor response trace. Using the peristaltic pump we still obtain intact\nA-droplets and reproduce the same characteristic output signal. These results further\ncorroborate the sensor’s capability for direct monitoring of atmospheric ammonia. All\nrelevant experimental data have been incorporated into the revised Supporting\nInformation and are discussed in the main text (Fig. 6 and the Conclusions) to\nsubstantiate the sensor’s potential for ambient applications. We appreciate the\nreviewer’s professional advice and respectfully seek their endorsement of these\nrevisions. Detailed revisions are as follows.\nSupplementary Fig. 23 | Response capability of the sensing device in open\nenvironments.\n“By optimizing the gas injection setup, the sensor can be directly interfaced with the\nambient environment, highlighting its potential for applications in open-air\nammonia detection (Supplementary Fig. 23).”\n“In the future, rational design of the coaxial injection system is expected to\nsignificantly enhance the platform’s ammonia responsiveness and adaptability in\nopen environments.”\n33"}]

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