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Zhao et al. Soft Sci. 2026, 6, 4 Page 9 of 15

60 min, which represents approximately 420% enhancement (2.05 ± 0.14 V) compared to the untreated
sensor (0.48 ± 0.02 V). This dramatic increase highlights the critical role of nanoscale surface engineering in
amplifying triboelectric electricity generation. Representative output voltage waveforms for sensors with
different plasma treatment times confirm consistent improvement in signal amplitude [Figure 3C].
Simulation results further validate the impact of surface roughness on triboelectric performance. Finite
element simulations of the surface potential distribution during the contact-separation process for both
smooth-surfaced and rough-surfaced devices indicate that the increased surface roughness leads to enhanced
open-circuit voltage [Figure 3D and Supplementary Figure 12]. Long-term cyclic stability under repeated
deformation demonstrates the suitability of the textile sensor for continuous, real-world respiratory
monitoring.

Sensor sensitivity is evaluated by measuring output voltage under varying pressures (from 0.5 to 1.5kPa) at a
constant 2 Hz frequency [Figure 3E] and varying frequencies (from 2 to 0.25Hz) at a constant 1 kPa pressure
[Figure 3F]. The results demonstrate a linear response to pressure changes with a sensitivity of 2.02 V·kPa -1
and stable performance across frequencies, indicating reliability for respiratory monitoring. The response
and recovery times of the triboelectric sensor are measured to be 96 and 126.1 ms [Figure 3G], which enables
the detection of transient respiratory events and abrupt changes in breathing patterns that may indicate
physiological distress. Moreover, the textile sensor demonstrates outstanding durability, maintaining stable
output performance over 5,000 continuous operating cycles [Figure 3H]. Collectively, the experimental
measurements and simulations show that controlled plasma-induced nanoscale roughness is a practical and
effective strategy to enhance triboelectric output for reliable respiratory monitoring.

On-mask triboelectric sensors for respiratory monitoring
The system implementation involves continuous monitoring and analysis of respiratory parameters from
testers wearing the sensor-integrated mask in real-world scenarios [Figure 4A and Supplementary Figure 13]
and simulated strong electromagnetic field environments. The triboelectric sensor produces distinctive
voltage signals corresponding to various respiratory patterns in real-world scenarios, including normal,
rapid, deep, shallow, speaking, and dyspneic breathing [Figure 4B]. Each pattern generates a unique
waveform with characteristic features in terms of amplitude, frequency, and regularity, which forms the basis
for subsequent signal analysis and machine learning-driven classification. Even in strong electromagnetic
field environments, the high-frequency noise in raw sensor signals can be effectively filtered by our custom
hardware low-pass circuit [Supplementary Figure 14], confirming the system’s robustness against
electromagnetic interference [Supplementary Figure 15]. Figure 4C shows the correspondence between the
signal curve and the stages of exhalation and inhalation during normal breathing. This mapping verifies that
temporal features of the voltage trace accurately reflect physiological respiratory phases. In comparison,
Figure 4D shows the signal curve output by abnormal breathing caused by interference signals, and breathing
delays can be sensitively detected. Interference signals introduced by mechanical actions during actual use
are identified by a custom algorithm, which analyzes the temporal continuity and amplitude variation
characteristics of respiratory signals. Breathing delays are distinguished by the characteristics of the
inspiratory phase following exhalation: normal breathing shows a rapid downward transition, while
breathing delay presents a gradual decrease. The extraction of these features provides a guarantee for the
accurate classification of subsequent dynamic breathing patterns.

To ensure reliable performance under extreme conditions, we assess the electrical durability of the
triboelectric sensor during prolonged use [Figure 4E]. Long-term testing reveals that the sensor retains over
95% of its initial output voltage after 90 days of regular use, indicating excellent durability for practical
applications. Besides, stable output under humid conditions is critical for long-lasting scenarios. We
conducted a 12-hour continuous wear test with sensor-integrated masks [Figure 4F], where the sensor was in