Stabilizing Ni<sup>2+</sup> in Hollow Nano MOF/Polymetallic Phosphides Composites for Enhanced Electrochemical Performance in 3D‐Printed Micro‐Supercapacitors
Bibliographic record
Abstract
Abstract Polymetallic phosphides exhibit favorable conductivities. A reasonable design of nano‐metal–organic frame (MOF) composite morphologies and in situ introduction of polymetallic phosphides into the framework can effectively improve electrolyte penetration and rapid electron transfer. To address existing challenges, Ni, with a strong coordination ability with N, is introduced to partially replace Co in nano‐Co–MOF composite. The hollow nanostructure is stabilized through CoNi bimetallic coordination and low‐temperature controllable polymetallic phosphide generation rate. The Ni, Co, and P atoms, generated during reduction, effectively enhance electron transfer rate within the framework. X‐ray absorption fine structure (XAFS) characterization results further confirm the existence of Ni–N, Ni–Ni, and Co–Co structures in the nanocomposite. The changes in each component during the charge–discharge process of the electrochemical reactions are investigated using in situ X‐ray diffraction (XRD). Theoretical calculations further confirm that P can effectively improve conductivity. VZNPGC//MXene MSCs, constructed with active materials derived from the hollow nano MOF composites synthesized through the Ni 2+ stabilization strategy, demonstrate a specific capacitance of 1184 mF cm −2 , along with an energy density of 236.75 µWh cm −2 (power density of 0.14 mW cm −2 ). This approach introduces a new direction for the synthesis of highly conductive nano‐MOF composites.
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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".