Evidence map›Paper›PMID 42359030›Full record

ReviewPrecision chemistry2026

Field-Programmable Dynamic Catalytic Technology to Make Catalyst Intelligent.

Haoran Zhang, Yuen Wu

Abstract readReview
In one paragraph

Review in Precision chemistry, 2026. 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

2 authors.

Haoran ZhangState Key Laboratory of Precision and Intelligent Chemistry/School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China.
Yuen WuState Key Laboratory of Precision and Intelligent Chemistry/School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China.ORCID https://orcid.org/0000-0001-9524-2843

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Conventional heterogeneous catalysts are constrained by static atomic structures, which are ill-suited for the diverse requirements of multistep reactions. This article introduces Field-Programmable Dynamic Catalysis (PDC), a paradigm enabling real-time, reversible programming of atomic structures via external fields and intelligent feedback. Using copper-based liquid metal catalysts, we demonstrate adaptive restructuring that optimizes methane oxidation performance. With magnetically tunable iron-based catalysts, we achieve reversible switching between atomically dispersed and clustered states, thus validating on-demand control over reaction pathways. This paradigm holds great promise for inert small molecule activation, such as nitrogen conversion, yet key challenges remain in boosting liquid metal surface area and designing scalable reactors. To address this multidimensional complexity, artificial intelligence is indispensable for accelerating the discovery and development of advanced PDC catalysts. Ultimately, PDC transforms catalysts from passive materials into active information-processing systems, enabling intelligent and precise chemical control at the atomic scale.

Indexed as

active site reconfigurationartificial intelligencefield-programmable dynamic catalysisinert molecule activationliquid metalmagnetic field regulationprecision-intelligent catalysis

Identifiers

PMID42359030
PMCPMC13292089

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.