Evidence map›Paper›PMID 42738212›Full record

ArticleMaterials (Basel, Switzerland)2026

Quantitative Prediction of Coal-Gangue Content Using Terahertz Time-Domain Spectroscopy and Physics-Informed Machine Learning.

Zeping Liu, Lipeng Hu, Jianfei Xu, Yadong Yang, Sitong Li, Zhou Xu, Longhai Liu, Jiabao Li, Houli Liu, Dongdong Ye

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Materials (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Zeping LiuState Key Laboratory of Intelligent Mining Equipment Technology, Taiyuan 030032, China.
Lipeng HuSchool of Artificial Intelligence, Anhui Polytechnic University, Wuhu 241000, China.
Jianfei XuSchool of Intelligent Manufacturing, Wuhu University, Wuhu 241008, China.
Yadong YangState Key Laboratory of Intelligent Mining Equipment Technology, Taiyuan 030032, China.
Sitong LiState Key Laboratory of Intelligent Mining Equipment Technology, Taiyuan 030032, China.
Zhou XuSchool of Electrical and Automation, Wuhu Vocational Technical University, Wuhu 241006, China.
Longhai LiuKey Laboratory of Opto-Electronics Information Technology, Ministry of Education, School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China.
Jiabao LiSchool of Artificial Intelligence, Anhui Polytechnic University, Wuhu 241000, China.ORCID 0009-0004-6761-7010
Houli LiuSchool of Artificial Intelligence, Anhui Polytechnic University, Wuhu 241000, China.
Dongdong YeSchool of Artificial Intelligence, Anhui Polytechnic University, Wuhu 241000, China.

Funding

Excellent Young Talents Fund of Higher Education Institutions of Anhui Province 2024AH030006Key Project for Natural Science Research of Universities in Anhui Province 2024AH052008Key Project of the Educational Research and Planning Program of the Anhui Provincial Association for Vocational and Adult Education AZCJ2025089Natural Science Key Research Project of Wuhu Institute of Technology wzyzrzd202504Open Fund of the State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining SKLMRDPC22KF22Open Fund of the State Key Laboratory of Intelligent Mining Equipment Technology ZNCKKF20240108Open Research Fund of Anhui Provincial Key Laboratory of Advanced Detection and Intelligent Sensing JCKJ2025A08 and JCKJ2025A010
6 · The paper itself

Abstract

Quantitative determination of gangue content is important for efficient coal use and intelligent coal-gangue separation. We combine transmission terahertz time-domain spectroscopy (THz-TDS), multidomain feature fusion, and machine learning to predict gangue mass fraction in coal-gangue mixtures. Time- and frequency-domain signals, refractive index, absorption and extinction coefficients, and complex permittivity were extracted from samples with different gangue contents. Five-fold cross-validation was used to compare random forest, support vector regression, Gaussian process regression, an artificial neural network, and an Effective Medium Theory-constrained Physics-Informed Neural Network (EMT-PINN). EMT-PINN achieved the best performance, with a coefficient of determination (R

Indexed as

coal–gangue separationeffective medium theorymachine learningphysics-informed neural networkterahertz time-domain spectroscopy

Identifiers

PMID42738212
PMCPMC13567379

What OpenQuestion holds

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Registered trials

None linked

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.