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Record W1497567432 · doi:10.1021/es0018668

Proton Binding by Hydrous Ferric Oxide and Aluminum Oxide Surfaces Interpreted Using Fully Optimized Continuous p<i>K</i><sub>a</sub> Spectra

2001· article· en· W1497567432 on OpenAlexafffund
D. Scott Smith, F. G. Ferris

Bibliographic record

VenueEnvironmental Science & Technology · 2001
Typearticle
Languageen
FieldEnergy
TopicIron oxide chemistry and applications
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsFerricOxideChemistryHydrous ferric oxidesLepidocrociteTitrationProtonInorganic chemistrySmoothnessAnalytical Chemistry (journal)GoethitePhysical chemistryAdsorptionMathematicsChromatographyPhysicsMathematical analysisOrganic chemistry

Abstract

fetched live from OpenAlex

A modified regularized least squares pKa spectrum approach is proposed to determine proton stability constants and concentrations for binding sites on hydrous ferric oxide (HFO) and aluminum oxide surfaces. Acid-base titration data are fit to a continuous binding site model for the system represented as a pKa spectrum. The modified parameter fitting method optimizes simultaneously for both smoothness of the pKa spectrum and goodness-of-fit, whereas other methods optimize for goodness-of-fit given a fixed smoothness factor. The modified method is tested with aluminum oxide and recovers values consistent with theoretical values. The regularized pKa spectrum method optimized for smoothness is applied to prepared samples of two types of HFO. The prepared HFO samples differ only in the total iron concentration of the parent solution. The resultant pKa distributions are compared to proton binding constants from MUSIC model results for crystalline iron oxides. The types of binding sites in the HFO sample are consistent with theoretical binding site stability constants for crystalline iron oxides. Overall, the prepared HFO samples have binding constants most consistent with values for lepidocrocite and goethite.

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 categoriesMeta-epidemiology (narrow)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.006
GPT teacher head0.211
Teacher spread0.205 · 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

Citations41
Published2001
Admission routes2
Has abstractyes

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