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Record W3095252446 · doi:10.1039/d0cp05172a

Towards understanding the catalytic properties of lead-based ballistic modifiers in double base propellants

2020· article· en· W3095252446 on OpenAlexfundno aff
Lisette R. Warren, Colin R. Pulham, Carole A. Morrison

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2020
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsnot available
FundersU.S. Army Combat Capabilities Development Command Aviation and Missile CenterArmy Research OfficeU.S. Army Combat Capabilities Development CommandEngineering and Physical Sciences Research CouncilMcMaster University
KeywordsPropellantLead (geology)CatalysisBase (topology)Materials scienceAmorphous solidCombustionCarbon fibersBurn rate (chemistry)Chemical engineeringAerospace engineeringNanotechnologyChemistryComposite materialPhysical chemistryOrganic chemistryComposite number

Abstract

fetched live from OpenAlex

Lead-based ballistic modifiers are additives in double base propellants (DBPs) which render the burn rate insensitive to changes in pressure within a defined pressure range, thus imparting greater control of combustion conditions. In-coming European legislation will soon ban the use of lead in propellant formulations, however, and few suitable candidate replacement materials are currently available. In an effort to understand better the unique properties offered by lead-based modifiers, we present a first-principles computational study on Pb, PbO, PbO2, SnO2 and Bi2O3, all of which have been investigated experimentally as ballistic modifier materials. Our study demonstrates that various quantifiable properties exist for the lead-based materials. Overall, they have narrower electronic band gaps, lower surface energies and lower surface work functions than the lead-free systems, indicating a greater propensity to form stable chemical surfaces with higher catalytic activity. We also show that of the set, only Pb and α-PbO can support the formation of a weakly bound layer of amorphous carbon, a key experimental observable in the burning of DBPs.

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 score0.201
Threshold uncertainty score0.553

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 head0.055
GPT teacher head0.217
Teacher spread0.162 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

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