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Record W4231542013 · doi:10.1149/ma2019-01/31/1593

H Sorption Based Materials for the Hydrogenation of CO2 to Added Value Fuel Products

2019· article· en· W4231542013 on OpenAlexaboutno aff
Maxime Leclerc, Ane Etxebarria, Dong‐Jin Yun, Yifan Ye, Ethan J. Crumlin, Gessie Brisard

Bibliographic record

VenueECS Meeting Abstracts · 2019
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsSorptionElectrocatalystContext (archaeology)AdsorptionBimetallic stripElectrochemical energy conversionCatalysisElectrochemistryProcess engineeringEnergy storageCyclic voltammetryMaterials scienceEnergy transformationChemical engineeringElectrolysisNanotechnologyChemistryElectrodeOrganic chemistryPhysicsEngineeringThermodynamics

Abstract

fetched live from OpenAlex

Energy transformation through technological systems has always been a critical issue regarding how our society works. With the environmental consideration taking over these last years, the establishment of efficient energy conversion technology allowing storage of renewably produced electricity in simple non-environmentally harmful chemicals is one of the great challenges of our times. The interest in CO2 in this context is simple; it represents a largely distributed low-energy chemical available for green energy storage. The electrochemical reduction of CO2 to small fuel molecule has been largely investigated, but the reduction process inefficiency and the cost of the highly specialized catalysts act as drawback to the implementation of this technology to industrial applications. In this work, we explore bimetallic catalyst that moves away from the simple optimization of reaction rate by adjustment of adsorption energies of species (i.e. volcano plot). We propose to combine a material active for H adsorption to a conventional CO2 electrocatalyst to explore the effect of potential enhanced availability and transfer of H during the carbon species conversion. The Cu/Pd system has been selected as a model material for the studies. Although many structures are possible to combine the metals, we chose to plate the Cu on the Pd substrate by UPD technique [1] to reduce the large hydrogen evolution occurring at Pd. The system has been studied by conventional electrochemistry, including a diversity of potentiostatic measurements and cyclic voltammetry (CV). The isotherm of H sorption has been assessed using Lasia methodology [2] to insure that the H sorption on Pd and the CO2 reduction on Cu occur in the same potential region. The UPD has been optimized and combined to dilution method to allow CO2 reduction at Cu-free solution after the deposition of a designed monolayer (ML) content of Cu on Pd. CVs has been conducted to access the general behavior of the Cu/Pd system. Activity of the underlying substrate for H has been observed to occur at the 1ML Cu on Pd. Measurements point toward the preservation of some H sorption activity by the Pd through the Cu ML although activity of bare Pd in contact with the solution through ML defect is not to exclude. We propose that the preservation of the Pd structure and electronic effects at the 1 ML Cu explains the possibility of enhanced H adsorption after the reduction by Cu at the interface. Differential electrochemical mass spectrometry (DEMS) measurements are conducted to study the effect of different content of Cu ML at the surface of the catalyst on the product distribution during the electrolysis. The specific adsorption of the anion from the electrolyte being known to affect the reaction pathway by partial and scattered surface coverage [3], different electrolytes were used to gain insight on pathways that are privileged by the different Cu/Pd surfaces. To further rationalize the links between surface properties and reaction products, X-ray photoelectron spectroscopy at near-ambient pressures (APXPS) measurements has been collected. This technique uses X-rays that generate photoelectrons that enter a differentially pumped XPS analyzer with electronoptics that facilitate the characterization of a solid material that is exposed to gases to enable the observation of their interaction during the spectrum collection. This method has previously been used to highlight different modes of CO2 adsorption on Cu surface [4]. Several ML content of Cu over Pd has been exposed to CO2 and CO under H-present and H-absent regimes. The affinity of the CO for Pd dominant surfaces has been observed. The coverage of Cu on the Pd substrate also influences the charge transfer between the adsorbed CO and the surface. We believe that the close containment of domains active for H sorption and CO2 reduction can be used as a tool to control selectivity over CO2 reduction and to favor the production of small added value molecules. Acknowledgement The financial support of the Fonds de recherche du Québec – Nature et technologies (FRQNT), of the Natural Sciences and Engineering Research Council of Canada (NSERC) and of Rio Tinto Alcan are gratefully acknowledged. This research used resources of the Advanced Light Source, which is a DOE Office of Science User Facility under contract no. DE-AC02-05CH11231. References A. Kumar and D. A. Buttry, The Journal of Physical Chemistry C, 2015, 119(29), 16927-16933 H. Duncan and A. Lasia, J. Electroanal. Chem., 2008, 621(1), 62−68. P. Dubé, G. Brisard, J. Electroanal. Chem., 2008, 582(1-2), 230-240 M. Favaro, H. Xiao, T. Cheng, W. A. Goddard, J. Yano et E. J. Crumlin, PNAS, 2017, 201701405

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 score0.003
Threshold uncertainty score0.009

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.215
Teacher spread0.203 · 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
Published2019
Admission routes1
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