Waste Material Classification Based on a Wavelength-Sensitive Ge-on-Si Photodetector.

Anju Manakkakudy Kumaran, Andrea De Iacovo, Andrea Ballabio, Jacopo Frigerio, Giovanni Isella, Lorenzo Colace

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

ZnO Doping-Induced Performance Boost in CoACS omega · 2026
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

6 authors.

Anju Manakkakudy KumaranDepartment of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy.ORCID 0009-0009-7802-1462

Andrea De IacovoDepartment of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy.ORCID 0000-0001-5006-5505

Andrea BallabioEye4NIR S.r.l., Via Bartolomeo Colleoni, 14, 23801 Calolziocorte, Italy.

Jacopo FrigerioLNESS Dipartimento di Fisica, Politecnico di Milano, Via Anzani 42, 22100 Como, Italy.

Giovanni IsellaLNESS Dipartimento di Fisica, Politecnico di Milano, Via Anzani 42, 22100 Como, Italy.

Lorenzo ColaceDepartment of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy.ORCID 0000-0002-7111-3905

Funding

Ministero dell'Università e della Ricerca F83C21000170001

6 · The paper itself

Abstract

Waste material classification is critical for efficient recycling and waste management. This study proposes a novel, low-cost material classification system based on a single, voltage-tunable Ge-on-Si photodetector operating across the visible and short-wave infrared (SWIR) spectral regions. Thanks to its tunability, the sensor is able to extract spectral information, and the system effectively distinguishes between seven different materials, including plastics, aluminum, glass, and paper. The system operates with a broadband illuminator, and material identification is obtained through the processing of the photocurrent signal at different bias voltages with classification algorithms. Here, we demonstrate the basic system functionality and near real-time classification of different waste materials.

Indexed as

dual-band photodetectormachine learning algorithmmaterial classificationoptical sensor

Identifiers

PMID39517867

DOI10.3390/s24216970

PMCPMC11548383

What OpenQuestion holds

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.