Evidence map›Paper›PMID 39860906›Full record

ArticleSensors (Basel, Switzerland)2025

GravelSens: A Smart Gravel Sensor for High-Resolution, Non-Destructive Monitoring of Clogging Dynamics.

Kaan Koca, Eckhard Schleicher, André Bieberle, Stefan Haun, Silke Wieprecht, Markus Noack

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

6 authors.

Kaan KocaInstitute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, 70569 Stuttgart, Germany.ORCID 0000-0003-1200-7499
Eckhard SchleicherHelmholtz-Zentrum Dresden-Rossendorf, 01328 Dresden, Germany.ORCID 0000-0002-6676-5263
André BieberleHelmholtz-Zentrum Dresden-Rossendorf, 01328 Dresden, Germany.ORCID 0000-0003-3428-5019
Stefan HaunInstitute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, 70569 Stuttgart, Germany.ORCID 0000-0002-8202-4633
Silke WieprechtInstitute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, 70569 Stuttgart, Germany.ORCID 0000-0001-6776-2446
Markus NoackFaculty of Architecture and Civil Engineering, Karlsruhe University of Applied Sciences, 76133 Karlsruhe, Germany.ORCID 0000-0002-3625-0357

Funding

Deutsche Forschungsgemeinschaft Project number 445027764
6 · The paper itself

Abstract

Engineers, geomorphologists, and ecologists acknowledge the need for temporally and spatially resolved measurements of sediment clogging (also known as colmation) in permeable gravel-bed rivers due to its adverse impacts on water and habitat quality. In this paper, we present a novel method for non-destructive, real-time measurements of pore-scale sediment deposition and monitoring of clogging by using wire-mesh sensors (WMSs) embedded in spheres, forming a smart gravel bed (GravelSens). The measuring principle is based on one-by-one voltage excitation of transmitter electrodes, followed by simultaneous measurements of the resulting current by receiver electrodes at each crossing measuring pores. The currents are then linked to the conductive component of fluid impedance. The measurement performance of the developed sensor is validated by applying the Maxwell Garnett and parallel models to sensor data and comparing the results to data obtained by gamma ray computed tomography (CT). GravelSens is tested and validated under varying filling conditions of different particle sizes ranging from sand to fine gravel. The close agreement between GravelSens and CT measurements indicates the technology's applicability in sediment-water research while also suggesting its potential for other solid-liquid two-phase flows. This pore-scale measurement and visualization system offers the capability to monitor clogging and de-clogging dynamics within pore spaces up to 10,000 Hz, making it the first laboratory equipment capable of performing such in situ measurements without radiation. Thus, GravelSens is a major improvement over existing methods and holds promise for advancing the understanding of flow-sediment-ecology interactions.

Indexed as

cloggingconductance sensorfine sedimentgravel bedpore-filling measurementporous mediasediment depositionsediment transporttwo-phase flowwire-mesh sensor

Identifiers

PMID39860906
PMCPMC11768524

What OpenQuestion holds

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Registered trials

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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.