Background:

Nanozymes—nanomaterials with intrinsic enzyme-like activity—have gained significant attention for diagnostics due to

Background:

Nanozymes—nanomaterials with intrinsic enzyme-like activity—have gained significant attention for diagnostics due to their stability, low-cost production, and enzyme-mimicking properties. Among these, peroxidase-mimicking nanozymes are widely used in disease diagnosis and biosensing, catalyzing peroxide decomposition to generate a detectable signal. However, many existing nanozymes suffer from low catalytic efficiency, significantly limiting their sensitivity in bioassays.

Technology Overview:

To address this, researchers at the University of Nevada, Reno have developed a carbon–platinum (CN-Pt) nanozyme by depositing ultrasmall platinum nanoparticles (1–2 nm) on nitrogen-doped carbon nanoparticles. This design dramatically enhances peroxidase-like activity, making the nanozyme a superior alternative to conventional horseradish peroxidase (HRP)-based assays.

CN-Pt nanozymes achieve 1.97 M·mL/s·g catalytic activity for H₂O₂ and 1.27 M·mL/s·g for TMB, significantly outperforming traditional nanozymes. Their record-high peroxidase-like activity enables the ultrasensitive detection of Burkholderia pseudomallei, a Tier 1 select agent, with detection limits of 0.11 ng/mL in buffer and 0.16 ng/mL in human serum.

Additionally, different from conventional Au@Pt core-shell nanozymes or similar materials, CN-Pt nanozymes naturally exhibit high colloidal stability over weeks without requiring surface modification. They can also be easily conjugated with antibodies (proteins) via electrostatic absorption, eliminating the need for complex bioconjugation procedures.

 For further information, please see ACS Applied Nano Materials Publication. 

Benefits:

• Ultra-sensitive detection: Enables sub-ng/mL detection for highly sensitive pathogen diagnostics.
• Higher catalytic activity: Demonstrates peroxidase-like activity with catalytic constants one to two orders of magnitude higher than many previously reported carbon–noble metal nanozymes.
• Thermal stability: Retains ~80% of enzymatic activity at 45°C, making it suitable for diverse operational environments.

Applications:

• Clinical and Laboratory Diagnostics: Enhances immunoassays for detecting biomarkers and pathogens in medical testing.
• Biosensor and Antimicrobial Applications: Can be incorporated into colorimetric biosensors and leveraged for antibacterial and antiviral activity, as nanozymes react with H₂O₂ to generate radicals (e.g., O*) that break down pathogens.
• Point-of-Care and Pathogen Neutralization: Potential use in rapid diagnostic tools and nanozyme-based pathogen control strategies.

Patent Pending

Technology Overview

Researchers at the University of Nevada, Reno have developed an early wildfire smoke

Technology Overview

Researchers at the University of Nevada, Reno have developed an early wildfire smoke detection software that uses AI to analyze real-time video from remote camera networks. The system, named Nemo, uses an end-to-end Transformer architecture (based on DETR) to detect small, low-density smoke plumes—often visible only as faint wisps on the horizon—within minutes of fire ignition. By capturing long-range pixel dependencies across high-resolution imagery, Nemo delivers faster, more sensitive smoke detection compared to existing deep learning techniques. In validation tests using 95 real wildfire video sequences, the software achieved a 97.9% detection rates for fires in their earliest incipient stage, with an average detection latency of just 3.6 minutes.

Figure 1: Nemo Wildfire detection

Further Details:

A. Yazdi, H. Qin, C. Jordan, L. Yang, and F. Yan, “Nemo: An Open-Source Transformer-Supercharged Benchmark for Fine-Grained Wildfire Smoke Detection,” Remote Sensing, vol. 14, no. 16, 2022. DOI: https://doi.org/10.3390/rs14163979

Benefits

• Fast Detection: Detects wildfire smoke in as little as 3.6 minutes after fire ignition.
• Early-Stage Focus: Trained to detect faint smoke plumes rather than visible flames.
• High Accuracy: 97.9% success rate detecting fires during early incipient stage.
• Fine-Grained Analysis: Classifies smoke density to insights into severity.

Applications

The wildfire smoke detection tool, including the ML model, training data, and methodology, is publicly available under Apache License 2.0 on GitHub. However, detailed hyperparameter tuning for the final model is proprietary. An improved methodology is currently under development, and we welcome for collaboration.