Biomimetic Conductive Scaffolds Integrating Metal-Organic Frameworks and Piezoelectric Polymers for Wound Healing by Epithelial Electric Field Modulation
Bibliographic record
Abstract
Human skin generates endogenous electric fields after injury, typically ranging from 10-100 mV/mm, which serve as navigational cues for keratinocytes, fibroblasts, and other repair-oriented cells.These fields drive galvanotaxis, the directed migration of cells in response to electrical gradients, and work in parallel with biochemical cues such as cytokines and growth factors during wound closure (Zhao, 2009).Despite this, in large or surgical wounds, these electric fields dissipate rapidly due to moisture loss, clotting, or disrupted ion transport.Current electroactive regeneration methods rely on external stimulation devices or inert conductive polymers that lack intrinsic charge-storage capabilities, which limits long-term stability.In this work, I propose a biomimetic, electroactive scaffold composed of a gelatin methacryloyl (GelMA) hydrogel reinforced with UiO-66-NH₂ metal-organic framework (MOF) nanoparticles (0.25-0.5 wt%) and trace PEDOT:PSS (0.1 wt%).The MOF acts as a weak redox reservoir, temporarily storing charge through reversible proton exchange with surrounding ions; a process sometimes referred to in materials chemistry as proton-coupled electron transfer.The PEDOT:PSS polymer enables lateral ion conduction across the hydrogel.A PVDF-TrFE piezoelectric underlayer may convert normal body motion such as bending or rotation into microcurrents, refreshing interplanar potentials without wired stimulation.I propose a framework involving open-circuit potential mapping, ion-leach safety testing via plasma mass spectrometry (ICP-MS), and migration assays in keratinocytes and fibroblasts.If successful, this scaffold would operate as a battery-free electroceutical material, enhancing vvrather than replacing the body's own electric field signaling.Possible influence on melanocyte
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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".