{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp09_archetype_breadth150_20260804","research_id":"eoa_inverse_innovation_exp09_light_prior_art_20260804","cell_id":"invariant_mode_decomposition_design__chemistry_materials","search_lanes":{"direct_problem_and_intervention":{"queries":["silicon graphite cell formation cycling multivariate degradation swelling impedance SEI lithium loss","battery formation dynamic mode decomposition degradation eigenvalue state transition"],"source_ids":["SRC1","SRC2","SRC3"],"no_result_note":null},"synonyms_and_historical_terms":{"queries":["silicon graphite formation coupled degradation SEI lithium loss swelling impedance capacity fade","lithium ion battery degradation dynamic mode decomposition Koopman PCA multivariate process control"],"source_ids":["SRC2","SRC3","SRC4"],"no_result_note":null},"products_practices_and_standards":{"queries":["lithium ion battery formation process control temperature pressure voltage hold official","battery cell formation manufacturing process official PDF formation aging quality testing VDMA"],"source_ids":["SRC1","SRC2","SRC4"],"no_result_note":null},"component_combination":{"queries":["dynamic mode decomposition lithium ion battery degradation state of health control","PCA multivariate statistical process control battery formation data design of experiments"],"source_ids":["SRC2","SRC3","SRC4"],"no_result_note":null}},"sources":[{"source_id":"SRC1","title":"Effect of formation protocol: Cells containing Si-Graphite composite electrodes","publisher":"Journal of Power Sources (Elsevier)","url":"https://www.sciencedirect.com/science/article/pii/S0378775319304872","source_type":"PRIMARY_RESEARCH","claims_supported":["A controlled study directly compared 13.2-hour and 186-hour formation protocols in silicon–graphite/NMC pouch cells.","The protocols produced different SEI surface chemistry but similar capacity-fade rates and performance, demonstrating that silicon–graphite formation recipes and downstream outcomes are experimentally testable.","The study identifies silicon volume expansion, continuing SEI formation, lithium consumption, and poor cyclability as interacting concerns, but does not use a learned state-transition operator or modal feedback."]},{"source_id":"SRC2","title":"Predicting the impact of formation protocols on battery lifetime immediately after manufacturing","publisher":"Joule (Cell Press)","url":"https://wengandrew.github.io/files/weng2021_formation_lifetime_prediction_joule.pdf","source_type":"PRIMARY_RESEARCH","claims_supported":["In forty NMC/graphite pouch cells, formation protocol affected lithium consumption, low-state-of-charge resistance, cycle life, and later swelling behavior.","Low-SOC pulse resistance immediately after formation correlated with cycle life and improved data-driven lifetime prediction, showing that conventional formation metrics can miss an informative early diagnostic.","The authors reported substantially more end-of-life swelling at 45 degrees Celsius under fast formation despite favorable lifetime results, illustrating conflicting and coupled objectives that a single endpoint may not capture."]},{"source_id":"SRC3","title":"A Graph Theoretic Approach in Combination With Dynamic Mode Decomposition With Control (DMDc) to Analyze Battery Degradation","publisher":"arXiv","url":"https://arxiv.org/html/2605.01689v1","source_type":"PRIMARY_RESEARCH","claims_supported":["The study applies DMD with control to battery HPPC data, fitting x(k+1) approximately equal to Ax(k) plus Bu(k), eigendecomposing the reduced operator, and comparing modes across degradation stages.","It demonstrates direct prior use of transition-operator modes to characterize lithium-ion battery degradation and reports degradation-associated changes in modal structure.","Its state is delay-embedded voltage, its control variable is applied current, and its purpose is degradation characterization rather than formation-recipe intervention; it also warns that truncation, embedding, sampling, noise, and weak modal magnitude affect mode stability and interpretation."]},{"source_id":"SRC4","title":"Lithium-ion battery cell formation: status and future directions towards a knowledge-based process design","publisher":"Energy & Environmental Science, Royal Society of Chemistry","url":"https://pubs.rsc.org/ca/content/articlehtml/2024/ee/d3ee03559j","source_type":"SECONDARY_RESEARCH","claims_supported":["Formation uses defined charge/discharge cycles and may include voltage limits, temperature-controlled aging, capacity, coulombic-efficiency, resistance, impedance, leakage, weight, and optical tests.","Formation is a multi-objective process affecting capacity, lifetime, rate capability, safety, cost, and time, with interdependent material, cell-design, temperature, pressure, cycling, and degassing factors.","For silicon electrodes, large volume changes, unstable evolving SEI, pulverization, gas generation, and continuing capacity loss create a need for sophisticated formation strategies.","The review reports limited standardized evaluation and scarce systematic protocol variation, but does not describe modal damping control."]}],"problem_evidence":{"status":"PARTLY_SUPPORTED","finding":"The problem class is visible. Formation conditions affect several downstream properties; silicon–graphite cells couple volume change, evolving SEI, lithium consumption, impedance and capacity behavior; and an apparently favorable protocol can have an adverse swelling outcome not captured by a favored diagnostic. However, the retained sources do not demonstrate the proposal's narrower premise that cells remaining inside all coordinate-wise limits contain a reproducible weakly damped or growing multivariate eigenmode during formation segments.","source_ids":["SRC1","SRC2","SRC4"]},"closest_prior_art":[{"name":"DMDc characterization of lithium-ion battery degradation","source_ids":["SRC3"],"overlap":"Learns a controlled linear transition operator from sequential battery measurements, eigendecomposes it, and uses modes to characterize changes with degradation. This collides with the proposal's mathematical decomposition core.","remaining_difference":"It models delay-embedded voltage responses to applied current during HPPC testing and analyzes aging retrospectively; it does not model multivariate electrochemical, mechanical and thermal formation snapshots, rank modes by repeated-segment gain, or choose and validate formation perturbations for modal damping."},{"name":"Experimental comparison of fast and slow formation for silicon–graphite cells","source_ids":["SRC1"],"overlap":"Directly varies formation current protocols in silicon–graphite pouch cells and evaluates SEI chemistry, performance and capacity fade.","remaining_difference":"It compares predefined recipes through conventional outcomes rather than estimating a repeated state-transition operator, identifying growing coupled directions, or selecting controls by their projection on those directions."},{"name":"Early diagnostic prediction of formation-protocol effects","source_ids":["SRC2"],"overlap":"Uses formation and post-formation measurements to screen recipes for later lifetime, lithium consumption and swelling consequences.","remaining_difference":"Its main diagnostic is low-SOC resistance and its model predicts lifetime; it does not infer stable or unstable multivariate formation modes, conduct a modal sensitivity sweep, or enforce spectral-gap and reconstruction-residual suspension rules."},{"name":"Knowledge-based multi-objective formation design","source_ids":["SRC4"],"overlap":"Treats formation as an interdependent, multi-objective process influenced by electrical, thermal, mechanical, material and cell-design factors and evaluated with multiple measurements.","remaining_difference":"The review surveys mechanisms, diagnostics and optimization considerations but does not instantiate the proposed transition-operator, eigenmode and mode-opposing intervention loop."}],"prior_art_disposition":"ADJACENT_PRIOR_ART","contrastive_claim_remaining":"Within a predeclared silicon–graphite cell design, temperature, state-of-charge and formation window, a cross-cell-stable eigenmode of a multivariate segment-to-segment transition model will identify a coupled degradation direction that coordinate-wise checks and endpoint optimization miss; a safety-approved recipe perturbation selected for negative projection onto that mode will reduce its subsequent amplitude relative to concurrent baseline cells without exciting another concerning mode, while meeting held-out reconstruction, spectral-separation, residual and drift limits.","contrastive_claim_falsifier":"The claim fails if no weakly damped or growing mode remains stable after normalization, sensor-drift correction, resampling, spectral-gap testing and held-out reconstruction; or if the selected perturbation does not reduce the preregistered modal-amplitude trajectory versus baseline, produces improvement only through measurement scaling, fails to move the contributing raw variables consistently, or excites another concerning mode or residual behavior.","gates":{"adequate_source_search":{"status":"PASS","rationale":"The bounded search covered the direct silicon–graphite formation problem, state-space and DMD/Koopman terminology, formation practices and diagnostics, and combinations of modal analysis with battery degradation. Exactly four opened sources from three publisher groupings were retained, including three primary studies and one comprehensive research review.","source_ids":["SRC1","SRC2","SRC3","SRC4"]},"supported_problem":{"status":"PASS","rationale":"The sources support interacting silicon–graphite degradation mechanisms, formation-dependent downstream behavior, multi-objective conflicts and informative early diagnostics. The exact hidden-growing-mode premise remains a testable hypothesis, so evidence is appropriately classified as partly supported.","source_ids":["SRC1","SRC2","SRC4"]},"distinct_testable_claim":{"status":"PASS","rationale":"Although DMDc battery degradation analysis and silicon–graphite formation optimization already exist separately, the remaining claim specifies a distinct formation-segment state vector, stability and reconstruction criteria, mode-directed perturbation, concurrent comparator and falsifying outcomes.","source_ids":["SRC1","SRC2","SRC3","SRC4"]},"bounded_next_test":{"status":"PASS","rationale":"An offline fit and held-out reconstruction on one existing development dataset, followed only conditionally by one baseline-versus-single-perturbation development lot, is bounded and can falsify mode existence, model stability and causal usefulness without changing production practice.","source_ids":["SRC1","SRC2","SRC3"]},"no_obvious_safety_or_authority_stop":{"status":"PASS","rationale":"No obvious stop applies to offline analysis or a jointly approved development-cell pilot confined to existing qualified equipment and validated current, voltage, temperature, pressure and swelling envelopes. Existing interlocks and release tests must remain active; lithium plating, gas evolution, abnormal swelling, heating, unstable modes or residual-limit breaches require stopping. Production recipe changes and release of pilot cells remain outside scope.","source_ids":["SRC2","SRC4"]}},"screen_survival":true,"world_novelty_boundary":"This four-source public-web screen establishes only coarse researchability and adjacent prior art. It cannot establish world novelty, patentability, an exhaustive absence of academic, patent or proprietary industrial implementations, market size, expert acceptance, production feasibility or realized safety and durability value."}