Evaluating lead-based vs. lead-free perovskites for environmentally sustainable indoor photovoltaics
Bibliographic record
Abstract
Indoor photovoltaics (IPVs) based on halide perovskites (HPs) and derivatives (HPDs) hold great promise for powering the vast infrastructure of Internet-of-Things (IoT) smart devices. While lead-based IPVs deliver cutting-edge performance, environmental concerns have spurred research into lead-free alternatives. However, the environmental sustainability of these IPV technologies remains underexplored, with the current lead-based versus lead-free debate confined to elemental considerations, overlooking life-cycle impacts and practical IPV requirements. This study presents the first comparative life-cycle assessment (LCA) addressing the lead-based vs. lead-free HP/HPD IPV dilemma, examining the environmental sustainability of absorbers, precursors, functional layers, and fabrication steps. A modelling framework is introduced to evaluate the net environmental gains (NEGs) of IPVs compared to the conventional battery-centric approach for powering smart devices. Our findings suggest that lead-free HP/HPD IPVs are not inherently more eco-friendly than their lead-based counterparts. We demonstrate that Pb- and Sn-based IPVs can achieve NEGs after just 3–4 weeks and 4–6 weeks, respectively, significantly outperforming mainstream IPVs. In contrast, the NEGs of Sb- and Bi-based IPVs align with mainstream IPVs, limiting their viability unless efficiencies increase to ∼40 %. Key strategies to enhance the eco-friendliness of HP/HPD IPVs and policy considerations for Pb-based IPVs in IoT applications are outlined.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".