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Record W4409699504 · doi:10.22215/cujs.v3i2.5101

Co-Crystallization of Photoresponsive Molecules with Metal Perchlorates

2025· article· en· W4409699504 on OpenAlexaff
M. J. ATHERTON, Katherine M. Marczenko

Bibliographic record

VenueCarleton undergraduate journal of science. · 2025
Typearticle
Languageen
FieldChemistry
TopicCrystallography and molecular interactions
Canadian institutionsCarleton University
Fundersnot available
KeywordsCrystallizationMoleculeMetalMaterials scienceChemistryPhotochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The interaction between photoresponsive molecules and potent oxidizers, such as metal perchlorates, is of interest to the Marczenko lab for their potential to control energetic material reactivity, leading to increased energy release and improved detonation. Metal perchlorates, such as manganese (Mn(ClO4)2), nickel (Ni(ClO4)2), and aluminum (Al(ClO4)3), are known for their oxidizing strength, releasing oxygen during decomposition to enhance the combustion capabilities of energetic materials. Additionally, they are thermally stable, making them suitable for higher temperature applications. Photoresponsive molecules can transfer energy from light to matter. The photoresponsive molecules of interest for this project were azobenzene and styrylpyridine derivatives. Co-crystallization of photoresponsive molecules with Mg, Ni, and Al perchlorate salts using slow cooling and evaporation methods yielded positive preliminary results. Characterization techniques, including nuclear magnetic resonance (NMR) spectroscopy and powder X-ray diffraction (PXRD), were utilized to assess molecular identity and phase formations. Diethyl benzylphosphonate was successfully synthesized, which was required for the successful synthesis of (E)-4-styrlpyrdine. Future work will continue to characterize the products from these reactions, generate co-crystals of (E)-4-styrlpyrdine with metal perchlorate salts, and assess the photoreactivity of these new materials.

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 score0.030
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.007
GPT teacher head0.269
Teacher spread0.262 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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