Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Automated HER2 Scoring with Uncertainty Quantification Using Lensfree Holography and Deep Learning.

BME frontiers·2026

Same author

Snapshot 3D image projection using a diffractive decoder.

Light, science & applications·2026

Same author

Autonomous Uncertainty Quantification for Computational Point-of-Care Sensors.

ACS nano·2026

Same author

Universal and transferable attacks on pathology foundation models using microscopic perturbations.

Light, science & applications·2026

Same author

Super-resolution image projection over an extended depth of field using a diffractive decoder.

Light, science & applications·2026

Same author

A virtual cohort framework with applications to adoptive cell therapy in bladder cancer.

bioRxiv : the preprint server for biology·2026

Same journal

Dual-Function Halide Exchange Strategy for Simultaneous Sn<sup>4+</sup> Elimination and Stability Enhancement in Pb-Sn Mixed Perovskite Solar Cells.

ACS nano·2026

Same journal

Vertically Stacked Indium Gallium Zinc Oxide-Based Three-Dimensional Integrated Circuits.

ACS nano·2026

Same journal

Tunable Nanoparticle Thin-Film Reveals Distance Dependence of Auger-Mediated Radiation Enhancement in Diffuse Midline Glioma.

ACS nano·2026

Same journal

G-Quadruplex Network Engineering in Ionogels: Realizing Robust Biosensing Interfaces for Plant Electrophysiology.

ACS nano·2026

Same journal

Announcing the 2026 <i>ACS Nano</i> Lectureship and <i>ACS Nano</i> Impact Award Laureates.

ACS nano·2026

Same journal

Ultrafast Self-Assembly of Zeolitic Imidazolate Framework-8 Enables Antibody Orientation for Ultrasensitive Lateral Flow Immunoassays.

ACS nano·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 8, 2026

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Computational Sensing Using Low-Cost and Mobile Plasmonic Readers Designed by Machine Learning.

Zachary S Ballard¹, Daniel Shir¹, Aashish Bhardwaj¹

¹Electrical Engineering Department, ‡Bioengineering Department, and §California NanoSystems Institute (CNSI), University of California , Los Angeles, California 90095, United States.

|January 28, 2017

Summary

This summary is machine-generated.

We developed a machine learning framework to design low-cost, mobile plasmonic readers. This approach optimizes light-emitting diodes for accurate refractive index prediction, enhancing plasmonic sensor applications.

Keywords:

computational sensing localized surface plasmon resonance machine learning mobile sensing plasmonic sensing plasmonics

More Related Videos

Using Extraordinary Optical Transmission to Quantify Cardiac Biomarkers in Human Serum

Using Extraordinary Optical Transmission to Quantify Cardiac Biomarkers in Human Serum

Published on: December 13, 2017

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Related Experiment Videos

Last Updated: Mar 8, 2026

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Using Extraordinary Optical Transmission to Quantify Cardiac Biomarkers in Human Serum

Using Extraordinary Optical Transmission to Quantify Cardiac Biomarkers in Human Serum

Published on: December 13, 2017

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Area of Science:

Nanotechnology
Sensor Technology
Machine Learning

Background:

Plasmonic sensors are crucial for biological and chemical detection.
Advances in nanofabrication enable cost-effective mass production of plasmonic sensors.
Improved sensor readout devices are needed to maximize the impact of plasmonic nanosensors.

Purpose of the Study:

To propose a machine learning framework for designing low-cost, mobile multispectral plasmonic readers.
To overcome limitations of bulky, expensive traditional readout components.
To enable wider adoption of plasmonic nanosensing technologies.

Main Methods:

A feature selection model was trained on fabricated plasmonic nanosensors.
Optimal light-emitting diodes (LEDs) were selected for a minimum-error refractive index prediction model.
The framework accounts for spectral responses and fabrication variability.
Experimental validation was performed using a modular mobile plasmonic reader with hexagonal and square nanohole array sensors.

Main Results:

The machine learning framework successfully designed a low-cost, mobile multispectral plasmonic reader.
Optimal illumination bands identified by the model differed from intuitive selections based on spectral features.
The computational sensing approach was experimentally validated.
The framework demonstrated effectiveness across different plasmonic sensor designs.

Conclusions:

The proposed framework offers a universal tool for designing cost-effective, mobile multispectral readers for plasmonics.
This facilitates the translation of nanosensing technologies into practical applications like wearable devices and diagnostics.
The approach is broadly applicable to other spectral-change-based sensors, including nanoparticle assays and graphene sensors.