Evidence map›Paper›PMID 34963899›Full record

ArticleACS central science2021

Closed-Loop Electrolyte Design for Lithium-Mediated Ammonia Synthesis.

Dilip Krishnamurthy, Nikifar Lazouski, Michal L Gala, Karthish Manthiram, Venkatasubramanian Viswanathan

Open access · diamondAbstract read
In one paragraph

Article in ACS central science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.3field-weighted citation impact, top 10% of its field
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

13 citing papers in PubMed, 64 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Regulating Interfacial Microenvironment in Aqueous Electrolyte via a NAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    Article
  9. Article
  10. Article
  11. Article
  12. Electrochemical Nitrogen Fixation for Green Ammonia: Recent Progress and Challenges.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023
    Review
  13. Near ambient NNature reviews. Chemistry · 2023
    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

5 authors at 2 institutions in 1 country.

Dilip KrishnamurthyDepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.ORCID https://orcid.org/0000-0001-8231-5492
Nikifar LazouskiDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.ORCID https://orcid.org/0000-0002-4655-2041
Michal L GalaDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Karthish ManthiramDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.ORCID https://orcid.org/0000-0001-9260-3391
Venkatasubramanian ViswanathanDepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.ORCID https://orcid.org/0000-0003-1060-5495
Massachusetts Institute of Technology · USCarnegie Mellon University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Novel methods for producing ammonia, a large-scale industrial chemical, are necessary for reducing the environmental impact of its production. Lithium-mediated electrochemical nitrogen reduction is one attractive alternative method for producing ammonia. In this work, we experimentally tested several classes of proton donors for activity in the lithium-mediated approach. From these data, an interpretable data-driven classification model is constructed to distinguish between active and inactive proton donors; solvatochromic Kamlet-Taft parameters emerged to be the key descriptors for predicting nitrogen reduction activity. A deep learning model is trained to predict these parameters using experimental data from the literature. The combination of the classification and deep learning models provides a predictive mapping from proton donor structure to activity for nitrogen reduction. We demonstrate that the two-model approach is superior to a purely mechanistic or a data-driven approach in accuracy and experimental data efficiency.

Identifiers

PMID34963899
PMCPMC8704027
OpenAlexW3217002494

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.