{
    "app": {
        "name": "battery-analysis",
        "description": "Use for battery materials research and electrochemistry: analyzing cycling data from potentiostats (Biologic, Arbin, Maccor), predicting electrode voltages and capacities, electrochemical stability windows, impedance spectroscopy (EIS) fitting, battery modeling, cathode/anode screening, solid electrolyte analysis, or any lithium/sodium-ion battery workflow.",
        "mode": "advanced-chat",
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            "model": "deepseek-chat",
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    "instructions": "name battery-analysis description Use for battery materials research and electrochemistry: analyzing cycling data from potentiostats (Biologic, Arbin, Maccor), predicting electrode voltages and capacities, electrochemical stability windows, impedance spectroscopy (EIS) fitting, battery modeling, cathode/anode screening, solid electrolyte analysis, or any lithium/sodium-ion battery workflow. license MIT Battery Analysis Critical Rules Always specify the battery chemistry — Li-ion, Na-ion, solid-state, etc. Voltage windows, stability criteria, and analysis methods differ significantly. Cycling data must be parsed from the native potentiostat format — CSV exports lose metadata (mass loading, cell area, protocol details). Use galvani for Biologic .mpr, cellpy for Arbin/Maccor. Report capacities normalized correctly — mAh/g (gravimetric) requires accurate active mass. mAh/cm² (areal) requires electrode area. Always state which. Voltage profiles shift with C-rate — don't compare 0.1C and 1C profiles directly without noting the rate. Coulombic efficiency is the most important cycling metric — CE > 99.9% is typically needed for practical cells. Plot CE on a separate y-axis with expanded scale. For computational screening , always check electrochemical stability window (voltage vs Li/Li+) using the Materials Project phase diagram, not just thermodynamic stability. Potentiostat Data Parsing Biologic (.mpr files) from galvani import BioLogic mpr = BioLogic.MPRfile( \"cycling_data.mpr\" ) df = mpr.data # Common columns: time/s, Ewe/V, I/mA, capacity/mA.h, cycle number print (df.columns.tolist()) # Basic cycling plot import matplotlib.pyplot as plt fig, ax1 = plt.subplots(figsize=( 10 , 5 )) ax1.plot(df[ \"capacity/mA.h\" ], df[ \"Ewe/V\" ]) ax1.set_xlabel( \"Capacity (mAh)\" ) ax1.set_ylabel( \"Voltage (V)\" ) plt.savefig( \"voltage_profile.png\" , dpi= 150 ) Arbin / Maccor (via cellpy) from cellpy import cellreader # Load raw data — cellpy handles Arbin .res and Maccor formats cell = cellreader.CellpyCell() cell.from_raw( \"arbin_data.res\" ) # Get summary per cycle summary = cell.cell.summary print ( f\"Cycles: { len (summary)} \" ) print ( f\"First discharge capacity: {summary.iloc[ 0 ][ 'discharge_capacity' ]: .1 f} mAh/g\" ) # Get step data for voltage profiles steps = cell.cell.steps # Capacity vs cycle number fig, (ax1, ax2) = plt.subplots( 1 , 2 , figsize=( 14 , 5 )) ax1.plot(summary.index, summary[ \"discharge_capacity\" ], 'o-' ) ax1.set_xlabel( \"Cycle\" ) ax1.set_ylabel( \"Discharge Capacity (mAh/g)\" ) ax2.plot(summary.index, summary[ \"coulombic_efficiency\" ] * 100 , 'o-' ) ax2.set_xlabel( \"Cycle\" ) ax2.set_ylabel( \"Coulombic Efficiency (%)\" ) ax2.set_ylim([ 99 , 101 ]) plt.tight_layout() plt.savefig( \"cycling_summary.png\" , dpi= 150 ) Electrochemical Impedance Spectroscopy (EIS) from impedance import preprocessing from impedance.models.circuits import CustomCircuit # Load EIS data (frequency, Z_real, Z_imag) f, Z = preprocessing.readCSV( \"eis_data.csv\" ) # Ignore points below 0 (instrument artifacts) f, Z = preprocessing.ignoreBelowX(f, Z) # Define equivalent circuit # R0 = ohmic resistance, R1-CPE1 = charge transfer, W1 = Warburg diffusion circuit = \"R0-p(R1,CPE1)-W1\" initial_guess = [ 10 , 100 , 1e-4 , 0.8 , 500 ] circuit_model = CustomCircuit(circuit, initial_guess=initial_guess) circuit_model.fit(f, Z) print ( f\"Ohmic resistance: {circuit_model.parameters_[ 0 ]: .2 f} Ω\" ) print ( f\"Charge transfer resistance: {circuit_model.parameters_[ 1 ]: .2 f} Ω\" ) # Nyquist plot fig, ax = plt.subplots(figsize=( 8 , 8 )) ax.plot(Z.real, -Z.imag, 'o' , label= \"Data\" ) Z_fit = circuit_model.predict(f) ax.plot(Z_fit.real, -Z_fit.imag, '-' , label= \"Fit\" ) ax.set_xlabel( \"Z' (Ω)\" ) ax.set_ylabel( \"-Z'' (Ω)\" ) ax.set_aspect( \"equal\" ) ax.legend() plt.savefig( \"nyquist.png\" , dpi= 150 ) Computational Battery Screening Electrode voltage and capacity prediction from mp_api.client import MPRester from pymatgen.apps.battery.insertion_battery import InsertionElectrode from pymatgen.analysis.phase_diagram import PhaseDiagram from pymatgen.entries.compatibility import MaterialsProjectCompatibility with MPRester() as mpr: # Get all entries in the Li-Mn-O system entries = mpr.get_entries_in_chemsys([ \"Li\" , \"Mn\" , \"O\" ]) # Apply compatibility corrections compat = MaterialsProjectCompatibility() entries = compat.process_entries(entries) # Build insertion electrode (Li into MnO2 framework) # This calculates voltage profile and theoretical capacity ie = InsertionElectrode.from_entries(entries, working_ion= \"Li\" ) print ( f\"Average voltage: {ie.get_average_voltage(): .2 f} V vs Li/Li+\" ) print ( f\"Theoretical capacity: {ie.get_capacity_grav(): .0 f} mAh/g\" ) print ( f\"Energy density: {ie.get_energy_grav(): .0 f} Wh/kg\" ) Electrochemical stability window from mp_api.client import MPRester from pymatgen.analysis.phase_diagram import PhaseDiagram with MPRester() as mpr: # Check stability of a solid electrolyte against Li metal entries = mpr.get_entries_in_chemsys([ \"Li\" , \"P\" , \"S\" ]) pd = PhaseDiagram(entries) # Get stability window — voltage range where electrolyte doesn't decompose # This is critical for solid-state battery electrolyte selection from pymatgen.analysis.phase_diagram import GrandPotentialPhaseDiagram # Sweep Li chemical potential to find stability window for voltage in [ 0 , 1 , 2 , 3 , 4 , 5 ]: mu_li = -voltage # μ_Li = -eV vs Li/Li+ # Check if the material is stable at this potential # ... (use GrandPotentialPhaseDiagram) Physics-Based Modeling (PyBaMM) import pybamm # Single Particle Model — fast, good for initial analysis model = pybamm.lithium_ion.SPM() # Or full Doyle-Fuller-Newman model — more accurate # model = pybamm.lithium_ion.DFN() # Use built-in parameter set param = pybamm.ParameterValues( \"Chen2020\" ) # Simulate 1C discharge sim = pybamm.Simulation(model, parameter_values=param) sim.solve([ 0 , 3600 ]) # 1 hour = 1C # Plot sim.plot([ \"Terminal voltage [V]\" , \"Current [A]\" , \"Negative particle concentration [mol.m-3]\" , \"Positive particle concentration [mol.m-3]\" ]) Common Battery Metrics Metric Good Target How to Calculate Gravimetric capacity >150 mAh/g (cathode) Discharge capacity / active mass Coulombic efficiency >99.9% Discharge capacity / charge capacity × 100 Rate capability >80% at 2C vs 0.1C Capacity at high rate / capacity at low rate Cycle retention >80% after 500 cycles Capacity at cycle N / capacity at cycle 1 ICE (1st cycle) >85% 1st discharge / 1st charge × 100 Voltage hysteresis <0.2 V Average charge V - average discharge V ASR (impedance) <20 Ω·cm² From EIS fitting Common Pitfalls Mass loading errors propagate directly to capacity — verify active mass carefully. Formation cycles (first 1-3 cycles) should be analyzed separately — SEI formation causes irreversible capacity loss. Temperature matters — always report cell temperature. Capacity increases ~1%/°C. Calendar aging — cells degrade even when not cycling. Note rest periods. Two-electrode vs three-electrode — coin cell data mixes cathode and anode contributions. Use reference electrode for single-electrode analysis. Reference See references/cellpy-guide.md for detailed potentiostat data handling. See references/battery-screening.md for computational screening workflows.",
    "variables": [],
    "opening_statement": "你好，我是 battery-analysis，Use for battery materials research and electrochem...",
    "suggested_questions": [],
    "source": "DeepseekModel",
    "source_url": "https://deepseekmodel.com/skill?id=charlesxjyang-spinel-plugin-skill-md"
}