We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
An Innovative Multiparametric Sensor Design for Detecting Microplastics and Heavy Metals
AI summary Read the abstract
Researchers designed a multiparametric optical sensor system using 22 LED light sources and 18 narrow-bandwidth photodiodes to simultaneously detect microplastics and heavy metals in environmental samples, with concentration evaluation performed by a trainable multilayer perceptron algorithm. The system offers a lower-cost alternative to conventional detection methods, eliminating the need for expert interpretation while maintaining analytical capability.
Microplastics and heavy metals are materials that harm the environment and living organisms. Rapid detection enables their control or the identification of their sources. Conventional detection methods are expensive and require expert interpretation. The proposed sensor system detects these materials and evaluates their concentrations using a trainable multilayer perceptron algorithm. The system consists of twenty-two different light spectrum LEDs and eighteen narrow bandwidth photodiodes. The absorbance of incoming light and the shifted bandwidths in the spectrum can be evaluated by assessing multiparametric optical events. The study examines water samples containing eight different heavy metals, namely arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), and zinc (Zn), along with three types of microplastics: melamine particles with a diameter of 8 µm, polystyrene particles with a diameter of 8 µm, and polystyrene particles with a diameter of 10 µm. Classification was performed in a concentration-dependent and concentration-independent manner. The system performance was improved by selecting features using the Ranker method of the InfoGain algorithm. Measurements were performed without applying any indicator chemicals. The system demonstrated high success in concentration-independent evaluation and acceptable concentration accuracy for heavy metals.
More Papers Like This
A Portable Optical Sensor for Microplastic Detection: Development and Calibration
AI summary Read the abstract
Researchers built a portable, low-cost optical sensor prototype designed to detect microplastics by shining multiple wavelengths of light through water samples. The device measures how different plastic particles absorb and scatter light, producing color spectra that can help identify microplastics. The sensor offers an affordable field-deployable option for environmental monitoring, with potential future improvements using machine learning for automated identification.
A Portable and Intelligent Dual-Modal Sensing System for Microplastics Detection
AI summary Read the abstract
Researchers built a portable dual-sensor system combining fiber optic fluorescence detection and impedance spectroscopy on a custom microfluidic chip, then trained a convolutional neural network on the combined signals to identify microplastics of varying sizes and concentrations with 100% accuracy alongside other dissolved substances.
Microplastics detection and environmentally toxicity testing by multimodal optical metrology
AI summary Read the abstract
Researchers developed a multimodal optical system combining fluorescence, Raman spectroscopy, and toxicity testing to detect and characterize microplastics in environmental samples. The system can simultaneously identify plastic types and assess their biological toxicity in a single workflow.
An Edge-Deployable Multi-Modal Nano-Sensor Array Coupled with Deep Learning for Real-Time, Multi-Pollutant Water Quality Monitoring
AI summary Read the abstract
Scientists developed a compact multi-sensor array that combines three different nanotechnology-based detection methods with deep learning to monitor water pollutants in real time, including nanoplastics at very low concentrations. The device achieved detection limits as low as 87 nanograms per liter for nanoplastics and can process data in just 31 milliseconds on low-power hardware. Field tests in municipal water systems showed the sensor maintained high accuracy even in complex real-world conditions.
Smart Portable Sensor System for Microplastic Detection in Water
AI summary Read the abstract
Researchers built a low-cost portable device that uses a light sensor to detect microplastics in water, showing results on a small screen and indicator lights. In tests with water samples containing different contamination levels, the device responded quickly and accurately classified the level of contamination.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.