Image created by Dr. Michael J. Miller
A team from National Taiwan University has created a robotic sensing platform capable of identifying bacteria through touch.
The team explained that rapid identification of bacteria is critical in the healthcare, food safety, environmental monitoring and infection control fields, with the most common first steps being gram classification, wherein bacteria is separated into gram-positive and gram-negative groups. While this data can reportedly help guide early treatment decisions and safety responses, gram staining involves many chemical steps, trained personnel and manual interpretation.
As such, the team developed its robotic sensing platform, which features a flexible sensor mounted on a robotic gripper, noting that when the robot gently encounters a bacterial sample, the surface of the bacteria creates a small electrical signal. Thanks to their different cell wall structures, gram-positive and gram-negative bacteria generate different signal patterns.
In the lab, the team tested representative bacteria — Escherichia coli, Staphylococcus aureus, Staphylococcus epidermidis and Pseudomonas aeruginosa. When they combined signals from two sensing materials and analyzed the patterns with a computer model, the system achieved roughly 90.93% accuracy in terms of distinguishing gram-positive and gram-negative bacteria with a response time of 620 milliseconds.
This approach, according to its developers, does not require staining reagents or additional labels. Further, the robotic platform lessens the need for direct human handling of bacterial samples.
The team suggests that this new, nondestructive touch-based sensing strategy could one day contribute to expedited point-of-care diagnostics, automated microbiology workflows and safer bacterial monitoring in healthcare and environmental settings.
Additional development could potentially enhance the platform to include broader pathogen panels, such as 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," the team concluded.
An article detailing the system, “Triboelectric nanosensor-based robotic platform for rapid label-free discrimination of Gram-positive and Gram-negative bacteria,” appears in the journal Nano Energy.
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.