Evidence map›Paper›PMID 35871554›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2022

High-Throughput Screening of Bicationic Redox Materials for Chemical Looping Ammonia Synthesis.

Jiaxin Fan, Wenxian Li, Sean Li, Jack Yang

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Integrating Vision-Language Models for Accelerated High-Throughput Nutrition Screening.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    Article
  2. 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.

Jiaxin FanMaterials and Manufacturing Futures Institute, School of Material Science and Engineering, University of New South Wales, Sydney, New South Wales, 2052, Australia.ORCID 0000-0003-3926-1438
Wenxian LiMaterials and Manufacturing Futures Institute, School of Material Science and Engineering, University of New South Wales, Sydney, New South Wales, 2052, Australia.
Sean LiMaterials and Manufacturing Futures Institute, School of Material Science and Engineering, University of New South Wales, Sydney, New South Wales, 2052, Australia.
Jack YangMaterials and Manufacturing Futures Institute, School of Material Science and Engineering, University of New South Wales, Sydney, New South Wales, 2052, Australia.ORCID 0000-0003-3589-6807

Funding

Australian Research Council DP190103661Australian Research Council DP220103229
6 · The paper itself

Abstract

Ammonia recently has gained increasing attention as a carrier for the efficient and safe usage of hydrogen to further advance the hydrogen economy. However, there is a pressing need to develop new ammonia synthesis techniques to overcome the problem of intense energy consumption associated with the widely used Haber-Bosch process. Chemical looping ammonia synthesis (CLAS) is a promising approach to tackle this problem, but the ideal redox materials to drive these chemical looping processes are yet to be discovered. Here, by mining the well-established MP database, the reaction free energies for CLAS involving 1699 bicationic inorganic redox pairs are screened to comprehensively investigate their potentials as efficient redox materials in four different CLAS schemes. A state-of-the-art machine learning strategy is further deployed to significantly widen the chemical space for discovering the promising redox materials from more than half a million candidates. Most importantly, using the three-step H

Indexed as

MetalloidsTransition ElementsAlkaliesAmmoniaHigh-Throughput Screening AssaysHydrogenMetals, Alkaline EarthOxidation-ReductionAlkaliesAmmoniaHydrogenMetalloidsMetals, Alkaline EarthTransition Elementsbicationic redox pairschemical looping ammonia synthesiscooperative effecthigh-throughput materials screeningmachine learningreaction engineering

Identifiers

PMID35871554
PMCPMC9507380

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.