Evidence map›Paper›PMID 40184458›Full record

ArticleScience advances2025

A high-throughput experimentation platform for data-driven discovery in electrochemistry.

Dian-Zhao Lin, Kai-Jui Pan, Yuyin Li, Charles B Musgrave Iii, Lingyu Zhang, Krish N Jayarapu, Tianchen Li, Jasmine Vy Tran, William A Goddard, Zhengtang Luo and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Data-Driven Materials Science for Energy-Sustainable Applications.Advanced materials (Deerfield Beach, Fla.) · 2026
    Review
  2. Article
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  6. Article
  7. Article
  8. Article
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  10. Review
  11. Advancing Organic Chemistry Using High-Throughput Experimentation.Angewandte Chemie (International ed. in English) · 2025
    Review
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

11 authors.

Dian-Zhao LinDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0003-1177-2308
Kai-Jui PanDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0009-0001-9894-9905
Yuyin LiDepartment of Chemical and Biological Engineering, William Mong Institute of Nano Science and Technology and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong 999077, P. R. China.ORCID 0000-0002-3709-7606
Charles B Musgrave IiiMaterials and Process Simulation Center, Department of Chemistry, California Institute of Technology, Pasadena, CA 91125, USA.ORCID 0000-0002-3432-0817
Lingyu ZhangDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Krish N JayarapuDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0002-5822-4133
Tianchen LiDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0001-7571-9632
Jasmine Vy TranDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
William A GoddardMaterials and Process Simulation Center, Department of Chemistry, California Institute of Technology, Pasadena, CA 91125, USA.ORCID 0000-0003-0097-5716
Zhengtang LuoDepartment of Chemical and Biological Engineering, William Mong Institute of Nano Science and Technology and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong 999077, P. R. China.ORCID 0000-0002-5134-9240
Yayuan LiuDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0002-0718-4784

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Automating electrochemical analyses combined with artificial intelligence is poised to accelerate discoveries in renewable energy sciences and technologies. This study presents an automated high-throughput electrochemical characterization (AHTech) platform as a cost-effective and versatile tool for rapidly assessing liquid analytes. The Python-controlled platform combines a liquid handling robot, potentiostat, and customizable microelectrode bundles for diverse, reproducible electrochemical measurements in microtiter plates, minimizing chemical consumption and manual effort. To showcase the capability of AHTech, we screened a library of 180 small molecules as electrolyte additives for aqueous zinc metal batteries, generating data for training machine learning models to predict Coulombic efficiencies. Key molecular features governing additive performance were elucidated using Shapley Additive exPlanations and Spearman's correlation, pinpointing high-performance candidates like

Identifiers

PMID40184458
PMCPMC13109943

What OpenQuestion holds

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LicenceCC BY-NC
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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.