Highly Efficient and Durable Anode Catalyst Layer Constructed with Deformable Hollow IrO<sub>x</sub> Nanospheres in Low‐Iridium PEM Water Electrolyzer
Bibliographic record
Abstract
Abstract Reducing iridium packing density (g Ir cm −3 electrode) represents a critical pathway to lower geometric Ir loading in proton exchange membrane water electrolyzers (PEMWEs), yet conventional approaches often cause performance issues of anode catalyst layer due to decreased structural stability and limited electron/mass transport efficiency. Here, we present deformable hollow IrO x nanospheres ( dh ‐IrO x ) as a structural‐engineered catalyst architecture that achieves an ultralow Ir packing density (20% of conventional IrO 2 nanoparticle‐based electrodes) while maintaining high catalytic activity and durability at reduced Ir loadings. Scalable synthesis of dh ‐IrO x via a hard‐template method—featuring precise SiO 2 nanosphere templating and conformal Ir(OH) 3 coating—enables batch production of tens of grams. Through cavity dimension and shell thickness optimization, dh ‐IrO x demonstrates excellent mechanical resilience to necessary electrode fabrication stresses, including high‐shear agitation, ultrasonic processing and hot‐pressing. In the anode catalyst layer, the quasi‐ordered close packing of dh ‐IrO x nanospheres simultaneously maximizes electrochemically active surface area, suppresses particle migration and agglomeration, and establishes percolated electron highways and rapid mass transport channels. The architected anode delivers high PEMWE performance (e.g., 1 A cm −2 @1.60 V and 2 A cm −2 @1.75 V@80 °C), while demonstrating excellent operational durability with <1.5% voltage loss over 3000 h.
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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".