Image created by Dr. Michael J. Miller
Researchers have developed a robotic platform that identifies Gram-positive and Gram-negative bacteria through touch-based electrical signals, without staining or chemical labels. The study achieved rapid classification within 0.62 seconds and 90.93% accuracy.
Fast identification of bacteria is important in healthcare, food safety, environmental monitoring, and infection control. One of the most common first steps is Gram classification, which separates bacteria into Gram-positive and Gram-negative groups. This information can help guide early treatment decisions and safety responses. However, conventional Gram staining requires several chemical steps, trained personnel, and manual interpretation.
This study was recently selected as a cover article in Nano Energy, highlighting its potential impact in rapid bacterial sensing and automated biomedical analysis.
A research team led by Prof. Zong-Hong Lin at National Taiwan University has developed a robotic sensing platform that identifies bacteria through touch. The system uses a flexible sensor mounted on a robotic gripper. When the robot gently contacts a bacterial sample, the surface of the bacteria produces a small electrical signal. Because Gram-positive and Gram-negative bacteria have different cell wall structures, they generate different signal patterns.
The team tested representative bacteria including Escherichia coli, Staphylococcus aureus, Staphylococcus epidermidis, and Pseudomonas aeruginosa. By combining signals from two sensing materials and analyzing the patterns with a computer model, the system achieved 90.93% accuracy in distinguishing Gram-positive and Gram-negative bacteria. The response time was only 620 milliseconds.
This approach offers several practical advantages. It does not require staining reagents or additional labels, and the robotic platform reduces the need for direct human handling of bacterial samples. The method is also non-destructive, meaning it may be useful for future systems that need repeated or automated monitoring.
The researchers envision that this touch-based sensing strategy could contribute to faster point-of-care diagnostics, automated microbiology workflows, and safer bacterial monitoring in healthcare and environmental settings. Further development could expand the platform to broader pathogen panels, including antibiotic-resistant bacteria and other clinically important microorganisms.
“By turning a simple touch into an electrical fingerprint, our system offers a faster and safer way to identify bacteria without chemical labels,” says co-corresponding author Zong-Hong Lin, professor and vice chair in the Department of Biomedical Engineering at National Taiwan University.
Source: National Taiwan University
Reference
Fu-Cheng Kao, Wei-Zan Hsu, Arshad Khan, Sheng-Chun Hung, Tupan Das, Ravindra Joshi, Parag Parashar, Ming-Kai Hsieh, Arnab Pal, Zong-Hong Lin, Triboelectric nanosensor-based robotic platform for rapid label-free discrimination of Gram-positive and Gram-negative bacteria, Nano Energy, Volume 152, 2026, 111879, ISSN 2211-2855, https://doi.org/10.1016/j.nanoen.2026.111879.
Abstract
Rapid and reliable identification of bacterial contaminants is essential for safeguarding public health, particularly in the face of rising antimicrobial resistance and emerging infectious disease outbreaks. Conventional Gram staining is time-consuming, operator-dependent, and relies on hazardous chemical reagents. Here, we present a triboelectric nanosensor (TENS)-based robotic platform capable of rapid, label-free, and non-destructive discrimination between Gram-positive (G+) and Gram-negative (G-) bacteria through contact electrification. The system integrates multiple triboelectric materials onto a robotic gripper to enable automated sensing while minimizing operator exposure risk. Fundamental differences in bacterial cell wall architecture generate distinct surface charging behaviors, which are captured as unique triboelectric signatures. Comprehensive characterization using X-ray photoelectron spectroscopy (XPS) and Fourier-transform infrared spectroscopy (FTIR) confirms the underlying chemical distinctions between G+ and G- bacteria, while micro-modified Kelvin probe force microscopy (KPFM) validates the material-dependent surface potential responses. Coupled with machine learning analysis, the platform achieves 90.93% classification accuracy, with a rapid response time of 620 ms. The robotic integration demonstrates strong potential for clinical application, offering reagent-free, automated, and operator-safe bacterial identification capability. This work establishes contact electrification as a new physical sensing modality for bacterial classification and opens new directions for point-of-care diagnostics and automated microbiological analysis.