Faster chemical mapping assisted by computer vision: insights from glass and ice core samples

Recent advances in high-repetition-rate lasers and fast aerosol transfer facilitate laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) mapping rates of up to megapixels per hour, however, practical limits in time and resources still hamper mapping the chemistry of square centimetre or larger areas of target samples at high resolutions. This is especially relevant for the analysis of deep sections of polar ice cores, motivating exploration of approaches to improve the efficiency of LA-ICP-MS data collection for large-area mapping. Assisted by computer vision, and demonstrated on glass and ice samples, we show how an informed experimental design coupled with computational post processing can contribute to large reductions in measurement times and lead to associated increases in measurement areas. Using various inpainting techniques, we demonstrate how the collection of data can be reduced by up to two thirds while still capturing spatial variability. Although motivated by ice core analysis, these approaches are generalisable to other target matrices and represent a new approach to large-area LA-ICP-MS mapping.

Authors

Institutions

Publication Details

Journal
The Analyst
Published
2025-01-01
DOI
https://doi.org/10.1039/d5an00325c
Citations
2
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
4.96

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Faster chemical mapping assisted by computer vision: insights from glass and ice core samples

Nicolas Stoll, Sebastiano Vascon, Carlo Barbante, Martin Šala et al.
2 citations
The Analyst
Geochemistry and Geologic Mapping
4.96
article

Faster chemical mapping assisted by computer vision: insights from glass and ice core samples

Nicolas Stoll, Sebastiano Vascon, Carlo Barbante, Martin Šala, Pascal Bohleber, Piers Larkman
article en
2 citations

Abstract

Recent advances in high-repetition-rate lasers and fast aerosol transfer facilitate laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) mapping rates of up to megapixels per hour, however, practical limits in time and resources still hamper mapping the chemistry of square centimetre or larger areas of target samples at high resolutions. This is especially relevant for the analysis of deep sections of polar ice cores, motivating exploration of approaches to improve the efficiency of LA-ICP-MS data collection for large-area mapping. Assisted by computer vision, and demonstrated on glass and ice samples, we show how an informed experimental design coupled with computational post processing can contribute to large reductions in measurement times and lead to associated increases in measurement areas. Using various inpainting techniques, we demonstrate how the collection of data can be reduced by up to two thirds while still capturing spatial variability. Although motivated by ice core analysis, these approaches are generalisable to other target matrices and represent a new approach to large-area LA-ICP-MS mapping.

The AnalystVol. 150(15)
Goethe University Frankfurt (DE), Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung (DE), Ca' Foscari University of Venice (IT), University of Washington (US), National Institute of Chemistry (SI), National Research Council (IT), Earth and Space Research (US), Seattle University (US)
Javna Agencija za Raziskovalno Dejavnost RS, H2020 European Research Council, HORIZON EUROPE European Research Council
Affordable and clean energy
Openalex Percentile: Top 5%
Geochemistry and Geologic Mapping
4.96
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.