Metal Ion Release Assembly: A Versatile Strategy for Scalable and Tunable 2D-Material Coatings
Bibliographic record
Abstract
Two-dimensional materials (2DMs) exhibit distinctive electronic, electrochemical, and barrier properties, yet scalable production methods for conformal, thick, and uniform coatings across diverse and complex substrates remain limited. We introduce a Metal Ion Release Assembly (MIRA) strategy that uses a gelatin hydrogel preloaded with metal ions (M n + ) as a controlled ion-release platform. Upon immersion in a 2DM dispersion, M n + is released from the hydrogel, screening the surface charges of nanosheets and inducing electrostatic assembly at the gelatin hydrogel surface. This MIRA process enables the formation of M n + –2DM multilayer coatings without the need for additives. The applicability of MIRA is demonstrated using graphene oxide, Ti 3 C 2 T x MXene, and montmorillonite nanosheets via spin coating, dip coating, and doctor blading. Coating thicknesses from ∼1 μm to >20 μm are systematically tuned by adjusting immersion time, M n + concentration, and 2DM dispersion concentration. Interference reflection microscopy confirms rapid nanosheet attachment and assembly driven by burst M n + release. A diffusion-limited analytical model based on Fick’s second law with time-dependent diffusion coefficients accurately predicts coating thickness evolution. M n + can be removed through mild acid rinsing. Scalability and substrate adaptability in MIRA are demonstrated by fabricating large-area (∼400 cm 2 ) and conformal coatings on curved and cylindrical surfaces. Electrochemical tests show the MXene electrodes fabricated using MIRA and acid rinsing processes perform comparably to pristine MXene electrodes, with similar resistances, specific capacitance, and cycling stability. MIRA provides a tunable and scalable platform for thick 2DM coatings, with applications in sensing, electromagnetic shielding, and corrosion protection.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".