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Record W2762348911 · doi:10.1039/c7sc03466k

From cellulose to kerogen: molecular simulation of a geological process

2017· article· en· W2762348911 on OpenAlexaff
Léa Atmani, Christophe Bichara, Roland J.‐M. Pellenq, Adri C. T. van Duin, Zamaan Raza, Lionel A. Truflandier, Amaël Obliger, Paul G. Kralert, Franz‐Josef Ulm, Jean‐Marc Leyssale

Bibliographic record

VenueChemical Science · 2017
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsCanadian Nautical Research Society
FundersAir Force Office of Scientific ResearchAix-Marseille UniversitéAgence Nationale de la RechercheNational Science Foundation
KeywordsKerogenMolecular dynamicsCelluloseOrganic matterProcess (computing)Materials scienceChemical engineeringChemistryGeologyOrganic chemistrySource rockComputational chemistryPaleontologyComputer scienceEngineering

Abstract

fetched live from OpenAlex

The process by which organic matter decomposes deep underground to form petroleum and its underlying kerogen matrix has so far remained a no man's land to theoreticians, largely because of the geological (Myears) timescale associated with the process. Using reactive molecular dynamics and an accelerated simulation framework, the replica exchange molecular dynamics method, we simulate the full transformation of cellulose into kerogen and its associated fluid phase under prevailing geological conditions. We observe in sequence the fragmentation of the cellulose crystal and production of water, the development of an unsaturated aliphatic macromolecular phase and its aromatization. The composition of the solid residue along the maturation pathway strictly follows what is observed for natural type III kerogen and for artificially matured samples under confined conditions. After expulsion of the fluid phase, the obtained microporous kerogen possesses the structure, texture, density, porosity and stiffness observed for mature type III kerogen and a microporous carbon obtained by saccharose pyrolysis at low temperature. As expected for this variety of precursor, the main resulting hydrocarbon is methane. The present work thus demonstrates that molecular simulations can now be used to assess, almost quantitatively, such complex chemical processes as petrogenesis in fossil reservoirs and, more generally, the possible conversion of any natural product into bio-sourced materials and/or fuel.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.226
Threshold uncertainty score0.178

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.016
GPT teacher head0.282
Teacher spread0.266 · 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 teacher head, 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".

Quick stats

Citations49
Published2017
Admission routes1
Has abstractyes

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