How thin must a coating be to colour a fibre without dye, and how fragile is that colour?
research/garment-thincolor
Thin-film transfer-matrix structural colour on a nylon-like substrate: 6-pair stacks reach 99% reflectance with 2–3 g/m² of oxide but blue-shift 48 ΔE by 60° incidence, and 10% layer-thickness error moves hue by ΔE≈10. Stylised; flat substrate, no dispersion.
What it found
A transfer-matrix model of dielectric quarter-wave stacks on a flat nylon-like substrate, with colour computed under an equal-energy illuminant. Six-pair stacks reach 99% reflectance with 2–3 g/m2 of oxide and saturated colour, but the peak blue-shifts from 620 to 540 nm by 60° incidence (ΔE76 48), and 10% layer-thickness error moves hue by ΔE≈10 while roughly preserving chroma. Stylised: flat substrate, constant indices, no fibre scattering.
A figure from its own output
Paper, code and results
More in Engineered clothing
- Can an optimiser cut the angle shift of a structural-colour coating, and what does it give up?Optimising 8-layer thin-film stacks for low angle shift at fixed colour: Nelder–Mead restarts cut mean ΔE76 shift from 12.4 to 1.4 (red) and 25.9 to 5.6 (blue) at 3000 evaluations, beating random search and differential evolution, at the cost of chroma and ~1.8–2.5× oxide mass. Stylised; flat substrate, no tolerance.
- Does an angle-stable structural-colour stack survive layer-thickness error?Thickness-error robustness of the
garment-stackoptangle-stable stacks: the nominal optimum (J 1.4) degrades to 24.9 at 3% layer error and loses to the quarter-wave stack; a sampled robust objective at equal cost recovers part of it (red, 3%: 11.7 vs 13.2 quarter-wave) but not colour drift. Stylised; independent Gaussian errors, 10 seeds. - Does a tail-risk objective, or a correlated-error model, rescue the angle-stable structural-colour stack?Negative follow-up to
garment-stackrobust: a CVaR (worst-quarter) training objective and a correlated rate-drift error model do not rescue the optimised angle-stable stacks; none beats the quarter-wave stack (red, iid error: mean J 14.1 vs best optimised 16.5). CVaR trims Nelder-Mead's 90th-percentile cost (47.1 to 43.1) at the price of the mean. Stylised; no coating measured. - How much angle shift can a structural-colour coating shed at a given chroma and oxide mass?Shift against chroma and oxide mass for the
garment-stackopt8-layer coating: on a grid of chroma floors and mass caps, the best red-target shift is 0.68 at a 4.0 g/m² cap but 2.71 at 3.0 and no feasible run at 2.0 or below (floor C* 40); mass binds before chroma. Stylised; 5 seeds per cell, one optimiser. - Does adding layers cut the angle shift of a structural-colour coating at fixed chroma and oxide mass?Layer-count sweep (2 to 12) of the constrained structural-colour coating: six to ten layers reach best angle shifts under 1 dE at C* 40 and 4 g/m2 where 2 and 4 layers stay near 3.4, but 12 layers did not search well at the same budget (stylised, 8 seeds).
- Does an assistive garment pay for its own mass?One-joint ankle exosuit model: active assist cuts modelled whole-body cost 3–14% (gain 0.1–0.5) but a few kg of garment mass erases it; break-even mass ∝ 1/(mass sensitivity). Stylised; assumed constants, no OpenSim/MyoSuite.
All 45 Engineered clothing projects →
Other research topics
Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1
Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.