Evidence map›Paper›PMID 39996995›Full record

ArticleBiosensors2025

In Situ Biofilm Monitoring Using a Heat Transfer Sensor: The Impact of Flow Velocity in a Pipe and Planar System.

Andreas Netsch, Shaswata Sen, Harald Horn, Michael Wagner

Abstract read
In one paragraph

Article in Biosensors, 2025. 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

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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Andreas NetschDVGW Research Center at the Engler-Bunte-Institut, Engler-Bunte-Ring 9a, 76131 Karlsruhe, Germany.ORCID 0009-0002-4539-6734
Shaswata SenEngler-Bunte-Institut, Water Chemistry and Water Technology, Karlsruhe Institute of Technology (KIT), Engler-Bunte-Ring 9a, 76131 Karlsruhe, Germany.
Harald HornDVGW Research Center at the Engler-Bunte-Institut, Engler-Bunte-Ring 9a, 76131 Karlsruhe, Germany.ORCID 0000-0002-9385-3883
Michael WagnerEngler-Bunte-Institut, Water Chemistry and Water Technology, Karlsruhe Institute of Technology (KIT), Engler-Bunte-Ring 9a, 76131 Karlsruhe, Germany.ORCID 0000-0002-7796-1905

Funding

Federal Ministry of Education and Research 02WER1531
6 · The paper itself

Abstract

Industrially applied bioelectrochemical systems require long-term stable operation, and hence the control of biofilm accumulation on the electrodes. An optimized application of biofilm control mechanisms presupposes on-line, in-situ monitoring of the accumulated biofilm. Heat transfer sensors have successfully been integrated into industrial systems for on-line, non-invasive monitoring of biofilms. In this study, a mathematical model for the description of the sensitivity of a heat transfer biofilm sensor was developed, incorporating the hydrodynamic conditions of the fluid and the geometrical properties of the substratum. This model was experimentally validated at different flow velocities by integrating biofilm sensors into cylindrical pipes and planar mesofluidic flow cells with a carbonaceous substratum. Dimensionless sensor readings were correlated with the mean biovolume measured gravimetrically, and optical coherence tomography was used to determine the sensors' sensitivity. The biofilm sensors applied in the planar flow cells revealed an increase in sensitivity by a factor of 6 compared to standard stainless steel pipes, as well as improved sensitivity at higher flow velocities.

Indexed as

BiofilmsBiosensing TechniquesHot TemperatureHydrodynamicsModels, Theoreticalbioelectrochemical systembiofilm sensorheat transferhydrodynamicsmathematical model

Identifiers

PMID39996995
PMCPMC11853227

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.