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Review
. 2025 Jul 2;11(7):1052-1061.
doi: 10.1021/acscentsci.5c00589. eCollection 2025 Jul 23.

Chemistries Moonshot: An Entirely Recyclable Car

Affiliations
Review

Chemistries Moonshot: An Entirely Recyclable Car

Robin Schoemaker et al. ACS Cent Sci. .

Abstract

Automobiles depend on fossil resources - both to create the device and to power it. The automotive industry has decreased this dependency on fossil fuels by developing more fuel-efficient combustion engines, lightweight designs, and biofuels. The rise of battery electric vehicles (BEVs) offers the chance to reduce the fossil footprint by avoiding fuel combustion and exhaust emission. Disruptive approaches toward a truly sustainable car are far from being market-ready. To reach a completely sustainable car, the automotive industry must address the carbon footprint of material production, which is based in the chemical sector. The automotive and chemical industries have to adopt closed-loop thinking, utilize renewable resources for biodegradables, as well as develop novel materials and designs for efficient recycling. Disruptive approaches can arise from predictive models that can accelerate chemical research and enable the discovery of sustainable materials with desirable recycling properties. Integrating generative artificial intelligence (AI) with high-throughput experimental validation will shorten material development cycles and advance the transition to more sustainable products. Moving toward a fully recyclable car is aligning research and development efforts from the chemical sector to the automotive industry and beyond, presenting a giant leap toward a circular economy.

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Figures

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Share of total lifecycle emissions of internal combustion engine vehicles (left) and expected change in emissions of material production (right).
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An automotive’s life adhering to the cradle-to-cradle principle.
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Biobased chemicals and materials pave the way toward a fully recyclable car.
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Current challenges in data-driven chemistry and ways to address them. (SHAP: SHapley Additive exPlanations; LIME: Local Interpretable Model-agnostic Explanations; PINN: Physics-Informed Neural Networks).

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