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Record W2057587597 · doi:10.1524/zpch.2012.0238

On Ligand Binding Energies in Porphyrinic Systems

2012· article· en· W2057587597 on OpenAlexaff
Oliver Schalk, Yujun Liang, Andreas‐Neil Unterreiner

Bibliographic record

VenueZeitschrift für Physikalische Chemie · 2012
Typearticle
Languageen
FieldMaterials Science
TopicPorphyrin and Phthalocyanine Chemistry
Canadian institutionsNational Research Council Canada
FundersDeutsche ForschungsgemeinschaftKarlsruhe Institute of TechnologyAlexander von Humboldt-Stiftung
KeywordsChemistryLigand (biochemistry)MoleculeMagnesiumAtom (system on chip)Binding energyZincNitrogenBond strengthBond lengthMetalSulfurOxygenCrystallographyBond energyInorganic chemistryComputational chemistryOrganic chemistryAtomic physicsAdhesive

Abstract

fetched live from OpenAlex

Abstract Energy decomposition analysis is a powerful tool to study the bond properties of molecules. Here, we used a recent implementation by P. Su and H. Li (J. Chem. Phys. 131 (2009) 014102) to test the method for investigations of ligand binding to medium sized molecules (30–40 atoms). We studied the properties of the bond between porphyrins or phthalocyanines with central metals without (Mg) or with a closed d shell (Zn and Cd) to ligands binding via various row II and III atoms (nitrogen, oxygen, phosphorus and sulphur) using various methods and basis sets. Several interesting results could be deduced: In the porphyrins, ligands binding via a nitrogen atom were preferred to those binding via oxygen and sixfold coordination was readily available for magnesium as central metal atom as opposed to zinc and cadmium derivatives which also showed a weaker bond strength. In phthalocyanines, a tendency to form fivefold coordination was also seen for magnesium compounds and bonds to nitrogen and oxygen had the same strength. Ligation to row III elements created only weak bonds that were prone to fluctuations.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.069
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.019
GPT teacher head0.274
Teacher spread0.255 · 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.

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

Citations7
Published2012
Admission routes1
Has abstractyes

Explore more

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