Autonomous lab optimization of amorphous silicon metasurfaces for grid-integrated electrolysis will reduce solar-to-fuel conversion carbon footprint by ≥30% compared to static designs, by dynamically tuning photochemical activity to match real-time grid volatility (Bridges: Metaphotonic Catalysis × Multi-Scale Optimization Framework × Architecture Carbon Tool).

An autonomous (closed-loop, ML-driven) optimization system controlling amorphous silicon (a-Si:H) metasurface geometry and/or tunable optical response, integrated with a grid-responsive electrolysis control policy, will achieve a ≥30% reduction in lifecycle carbon footprint (gCO2e/kg H2, cradle-to-gate, including embodied fabrication emissions amortized over device lifetime) for solar-to-hydrogen production, relative to a static (fixed-geometry, non-grid-responsive) metasurface-electrolyzer baseline, when evaluated over a minimum 12-month simulated or measured grid-carbon-intensity time series for a specified regional grid mix. The claim is falsifiable: if the measured/simulated carbon reduction is <30% (or if the dynamic system's higher embodied/control-overhead emissions erase the operational savings), the hypothesis is disproven for that grid context.

Phase 1 (Simulation, months 1–3): Build coupled optical-electrochemical-grid digital twin. Validate metasurface optical response model (FDTD/RCWA) against published a-Si:H metasurface absorption spectra. Couple to electrolyzer performance model (current-voltage-efficiency curves) and grid carbon-intensity time series (real historical data, 3 regions: high-volatility e.g. CAISO/Germany, medium e.g. Texas ERCOT, low-volatility e.g. France). Run static-vs-dynamic control policy comparison via Bayesian optimization / multi-scale optimization framework (per hypothesis's cited framework) over 12-month simulated operation. Phase 2 (Bench validation, months 4–8): Fabricate 3 static a-Si:H metasurface samples (baseline designs from literature) + 1 tunable prototype (electrochromic or thermally-tunable actuation layer) at ≤10cm² scale. Measure real photochemical/photocatalytic activity (H2 evolution rate via gas chromatography) under (a) fixed AM1.5G illumination and (b) simulated grid-signal-driven modulation using programmable LED solar simulator array. Phase 3 (Integrated carbon accounting, months 8–10): Apply Architecture Carbon Tool (or equivalent LCA framework) to compute full cradle-to-gate carbon footprint for both static and dynamic systems using measured efficiencies, actual fabrication BOM/energy data, and real grid carbon-intensity traces. Statistical comparison (paired bootstrap, n≥1000 resamples) of carbon footprint distributions.

# Digital twin pipeline
for region in [high_volatility, medium_volatility, low_volatility]:
    grid_signal = load_carbon_intensity_series(region, resolution='1h', years=1)
    irradiance = load_nsrdb_tmy(region)
    for seed in range(5):
        weather = perturb(irradiance, seed)
        # Static baseline
        static_design = optimize_metasurface(objective='max_avg_efficiency', grid=None)
        static_h2, static_carbon = simulate_operation(static_design, weather, grid_signal, mode='static')
        # Dynamic system
        agent = MultiScaleOptimizer(state=[grid_signal_t, irradiance_forecast_t, device_state_t])
        for t in timesteps:
            action = agent.select_tuning_state(state_t)  # e.g. Bayesian opt / RL policy
            metasurface.apply(action)
            h2_rate_t = photoelectrochemical_model(metasurface.state, irradiance_t)
            reward_t = -carbon_cost(h2_rate_t, grid_signal_t)
            agent.update(reward_t)
        dynamic_h2, dynamic_carbon = simulate_operation(agent, weather, grid_signal, mode='dynamic')
        # LCA
        total_static_co2 = embodied_carbon(static_design) + operational_carbon(static_carbon)
        total_dynamic_co2 = embodied_carbon(dynamic_design, incl_actuators, control_hw) + operational_carbon(dynamic_carbon)
        record(reduction = 1 - total_dynamic_co2/total_static_co2)

bootstrap_ci(reduction_distribution, n=1000)
sensitivity_sweep(control_hw_embodied_carbon, range=[-50%, +50%])