Evidence map›Paper›PMID 39373824›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024

Digital Fingerprinting of Complex Liquids Using a Reconfigurable Multi-Sensor System with Foundation Models.

Gianmarco Gabrieli, Matteo Manica, Joris Cadow-Gossweiler, Patrick W Ruch

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 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

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.

Gianmarco GabrieliIBM Research Europe, Säumerstrasse 4, Rüschlikon, 8803, Switzerland.ORCID https://orcid.org/0000-0001-7279-7707
Matteo ManicaIBM Research Europe, Säumerstrasse 4, Rüschlikon, 8803, Switzerland.ORCID https://orcid.org/0000-0002-8872-0269
Joris Cadow-GossweilerIBM Research Europe, Säumerstrasse 4, Rüschlikon, 8803, Switzerland.ORCID https://orcid.org/0000-0002-4410-2805
Patrick W RuchIBM Research Europe, Säumerstrasse 4, Rüschlikon, 8803, Switzerland.ORCID https://orcid.org/0000-0003-0969-403X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Combining chemical sensor arrays with machine learning enables designing intelligent systems to perform complex sensing tasks and unveil properties that are not directly accessible through conventional analytical chemistry. However, personalized and portable sensor systems are typically unsuitable for the generation of extensive data sets, thereby limiting the ability to train large models in the chemical sensing realm. Foundation models have demonstrated unprecedented zero-shot learning capabilities on various data structures and modalities, in particular for language and vision. Transfer learning from such models is explored by providing a framework to create effective data representations for chemical sensors and ultimately describe a novel, generalizable approach for AI-assisted chemical sensing. The translation of signals produced by remarkably simple and portable multi-sensor systems into visual fingerprints of liquid samples under test is demonstrated, and it is illustrated that how a pipeline incorporating pretrained vision models yields

Indexed as

chemical sensingmulti‐sensor systemsvision foundation models

Identifiers

PMID39373824
PMCPMC11600221

What OpenQuestion holds

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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.