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Record W7128190402 · doi:10.31224/5038

Electroslag Refining (ESR) for Sustainable Battery Recycling: Technological Innovations, Economic Viability, and Patent Landscape in Low-Cost Electricity Regions

2025· article· W7128190402 on OpenAlexaboutno aff
Sudhakar Geruganti

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsnot available
Fundersnot available
KeywordsElectricityRenewable energyBattery (electricity)SustainabilityCarbon footprintSustainable developmentSlag (welding)Electricity generation

Abstract

fetched live from OpenAlex

Electroslag refining (ESR) emerges as a complementary battery recycling solution in regions with electricity costs below $0.06/kWh. This study demonstrates that ESR achieves 92% Co/Ni recovery at $14/kg operating costs when powered by renewable energy, compared to $19/kg for hydrometallurgy. Technological innovations include Li₂O-doped slag (1-5 wt%) reducing lithium losses to <5% and IoT-controlled current modulation cutting energy use by 18%. A global patent analysis identifies white spaces in apparatus design (WIPO class C22B9/16), while techno-economic modeling reveals optimal viability in Quebec (hydro) and Rajasthan (solar) regions. Despite lower Li recovery (68% vs hydro's 80%), ESR's 35% carbon footprint reduction and slag valorization potential (92% as cement additive) position it as a sustainable alternative under EU Taxonomy criteria Detailed Description Technological Innovations Li₂O-Doped Slag Design: Composition: CaF₂-Al₂O₃-Li₂O (80-15-5 wt%) Performance: 89% Li retention vs. 62% in conventional slags at 1600°C Mechanism: Li₂O increases slag basicity, reducing Li volatility Smart ESR Systems: IoT sensors optimize current density (0.5-1.2 A/mm²) Reduces energy consumption from 12 → 9.8 kWh/kg Co Economic Viability Region Electricity Cost ($/kWh) ESR Viability Index* Key Enablers Quebec 0.04 88/100 Existing aluminum smelters Norway 0.03 95/100 Battery passport infrastructure Rajasthan 0.05 72/100 Solar park colocation *Based on 5 factors: energy cost, infrastructure, policy, demand, logistics Patent Landscape White Spaces: Direct ESR processing of pyrolyzed black mass (no prior USPTO patents) Slag compositions with 1-5% Li₂O (novelty confirmed via Espacenet search) Risk Areas: Avoid infringement on Umicore's hydrometallurgy patents (US2018367232) Design-around Tesla's direct recycling claims through high-temperature differentiation Sustainability Impact Carbon Footprint: ESR: 2.1 kg CO₂/kg metal vs. 3.8 kg for pyrometallurgy Further reducible to 1.4 kg with renewable electricity Waste Valorization: 92% of slag meets ASTM C989 for cement additives Potential offset of $50/ton disposal costs

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.769
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.273
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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