Impact of Metallophilicity on “Colossal” Positive and Negative Thermal Expansion in a Series of Isostructural Dicyanometallate Coordination Polymers
Bibliographic record
Abstract
Five isostructural dicyanometallate coordination polymers containing metallophilic interactions (In[M(CN)(2)](3) (M = Ag, Au), KCd[M(CN)(2)](3), and KNi[Au(CN)(2)](3)) were synthesized and investigated by variable-temperature powder X-ray diffraction to probe their thermal expansion properties. The compounds have a trigonal unit cell and show positive thermal expansion (PTE) in the ab plane, where Kagome sheets of M atoms reside, and negative thermal expansion (NTE) along the trigonal c axis, perpendicular to these sheets. The magnitude of thermal expansion is unusually large in all cases (40 x 10(-6) K(-1) < |alpha| < 110 x 10(-6) K(-1)). The system with the weakest metallophilic interactions, In[Ag(CN)(2)](3), shows the most "colossal" thermal expansion of the series (alpha(a) = 105(2) x 10(-6) K(-1), alpha(c) = -84(2) x 10(-6) K(-1) at 295 K), while systems containing stronger Au-Au interactions show relatively reduced thermal expansion. Thus, it appears that strong metallophilic interactions hinder colossal thermal expansion behavior. Additionally, the presence of K(+) counterions also reduces the magnitude of thermal expansion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".