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Record W4285398537 · doi:10.1149/ma2022-017661mtgabs

3D Heteroatom-Doped Carbon for Lithium/Zinc Metal Anodes

2022· article· en· W4285398537 on OpenAlexaff
Zhixiao Xu, Xiaolei Wang

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFaraday efficiencyAnodeMaterials scienceElectrolyteHeteroatomSeparator (oil production)FOIL methodLithium (medication)Chemical engineeringNanotechnologyElectrochemistryPlating (geology)Capacity lossComposite materialChemistryElectrode

Abstract

fetched live from OpenAlex

With the advance of portable electronics and electric vehicles, graphite-based lithium-ion batteries with moderate energy densities can not meet the ever-increasing energy demand which calls for high-energy anode materials.[1] Among candidates, lithium metal has been regarded as the ultimate anode for rechargeable batteries because of its highest theoretical specific capacity (3860 mA h g−1) and lowest electrochemical potential (−3.040 V vs SHE).[1] Unfortunately, only primary lithium metal batteries (LMBs) but no rechargeable ones have been commercialized due to the low Coulombic efficiency (CE) and dendritic growth during Li plating/stripping, resulting in short cycle life and safety concerns. Recently, it has been clarified that LMBs failure majorly occur because of low CE, active Li consumption or electrolytes depletion and the low CE is largely caused by “dead” Li formation, which is associated with Li deposition morphology.[2] As such, it is of great significance to develop methods to regulate the Li plating/stripping behavior and minimize the generation of inactive Li deposits for practical application of LMBs. Many strategies have been proposed to improve CE, inhibit Li dendrites, and extend cycle life, including Li host design,[3] electrolyte engineering,[4] separator modification,[5] and artificial solid electrolyte interphase (SEI) construction.[6] In this work, we report the large-area coating of polymer-derived N,O-codoped vertically aligned carbon sheet arrays on commercial Cu foil current collector (NOCA@Cu) as the efficient 3D host toward safe and dendrite-free LMBs through polymer interfacial self-assembly and morphology-preserved carbonization.[7] Interestingly, it is found that the different orientation mode, vertical or horizontal, of polymer layers on the Cu surface will have a huge influence on heteroatom dopants and topological defects of derived carbon. The optimized NOCA@Cu delivers excellent performance with a high CE of 91–93% and long life up to 600 cycles in the carbonate electrolyte as well as 98.5% CE and stable cycling up to 1300 h in ether electrolyte, much better than horizontal carbon film-coated Cu and pristine Cu. On the other hand, aqueous zinc metal-based energy devices show great promise for large-scale energy storage due to the advantage of Zn metals including high theoretical capacity (gravimetric: 820 mAh g−1), suitable redox potential (−0.764 V vs. SHE), high safety, cost effectiveness, and eco-friendliness.[8] However, similar to Li metal, Zn metal also shows poor reversibility of deposition/dissolution.[9] This poor reversibility is mainly caused by the low CE and dendritic growth of Zn metal accompanied by side reactions from metal corrosion and hydrogen evolution due to water decomposition. [10] Hence, it is significant to develop dendrite-free Zn metal anodes with high CE for rechargeable devices, which unfortunately is still challenging as with Li metal anodes. Similar strategies have been proposed to tackle the issues, such as host construction,[11] interfacial protection,[12] and electrolyte engineering.[13] Despite the progress, most of reported Zn anode only experienced low depth of discharge (DOD, <1%),[14] which will considerably lower cell-level energy densities. As such, construction of Zn hosts, e.g., carbon-rich nanomaterials, to regulate the plating/stripping of Zn metal anode under high DOD is important to achieve high cell-level energy densities. As potential carbon hosts, 3D carbons with highly exposed surface area and hierarchically oriented building blocks could homogenize ionic flux, decrease local current densities, and guide Zn deposition in a unique way.[15] In this work, we firstly regulate heteroatom-doped 3D carbon host for high-DOD Zn metal chemistry, including Coulombic efficiency, deposition morphology, and full cell applications. DFT calculations reveal that among oxygen/nitrogen dopants the ether (C-O), carboxylic (-O-C=O-) and pyrrolic N groups show strong binding with Zn, making them favorable heterogeneous nucleation sites for zinc growth. Accordingly, monomers enriched with those O/N groups were rationally selected and corresponding polymers were prepared via controlled self-assembly and converted to carbon with different morphologies and sizes. Among carbon hosts, carbon flowers (Cflower) enable best performance with high CE values of 97~99% at current densities of 0.5~10 mA cm-2, surpassing other structured hosts. Reference [1] Chem. Rev. 2017, 117, 10403. [2] Nature 2019, 572, 511. [3] Carbon Energy 2021, 1. [4] Adv. Energy Mater. 2017, 7, 1602011. [5] ACS Nano 2017, 11, 6114. [6] Nat. Nanotechnol. 2014, 9, 618. [7] Adv. Funct. Mater. 2021, 31, 2102354. [8] Chem. Rev. 2020, 120, 7795. [9] Angew. Chem. Int. Ed. 2020, 59, 13180. [10] ACS Energy Lett. 2020, 5, 3569. [11] Adv. Mater. 2020, 32, 1906803. [12] Adv. Energy Mater. 2018, 8, 1801090. [13] Nat. Mater. 2018, 17, 543. [14] Adv. Mater. 2019, 0, 1900668. [15] Electrochem. Energ. Rev. 2021, 4, 269.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.226
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2022
Admission routes1
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