Fast Cyclic Voltammetry

              flowchart TD
              pypalmsens.FastCyclicVoltammetry[FastCyclicVoltammetry]
              pypalmsens._methods.base.BaseTechnique[BaseTechnique]
              pypalmsens._methods.mixins.PretreatmentMixin[PretreatmentMixin]
              pypalmsens._methods.mixins.VersusOCPMixin[VersusOCPMixin]
              pypalmsens._methods.mixins.PostMeasurementMixin[PostMeasurementMixin]
              pypalmsens._methods.mixins.IrDropCompensationMixin[IrDropCompensationMixin]
              pypalmsens._methods.mixins.DataProcessingMixin[DataProcessingMixin]
              pypalmsens._methods.mixins.GeneralMixin[GeneralMixin]
              pypalmsens._methods.base_model.BaseModel[BaseModel]

                              pypalmsens._methods.base.BaseTechnique --> pypalmsens.FastCyclicVoltammetry
                                pypalmsens._methods.base_model.BaseModel --> pypalmsens._methods.base.BaseTechnique
                

                pypalmsens._methods.mixins.PretreatmentMixin --> pypalmsens.FastCyclicVoltammetry
                                pypalmsens._methods.base_model.BaseModel --> pypalmsens._methods.mixins.PretreatmentMixin
                

                pypalmsens._methods.mixins.VersusOCPMixin --> pypalmsens.FastCyclicVoltammetry
                                pypalmsens._methods.base_model.BaseModel --> pypalmsens._methods.mixins.VersusOCPMixin
                

                pypalmsens._methods.mixins.PostMeasurementMixin --> pypalmsens.FastCyclicVoltammetry
                                pypalmsens._methods.base_model.BaseModel --> pypalmsens._methods.mixins.PostMeasurementMixin
                

                pypalmsens._methods.mixins.IrDropCompensationMixin --> pypalmsens.FastCyclicVoltammetry
                                pypalmsens._methods.base_model.BaseModel --> pypalmsens._methods.mixins.IrDropCompensationMixin
                

                pypalmsens._methods.mixins.DataProcessingMixin --> pypalmsens.FastCyclicVoltammetry
                                pypalmsens._methods.base_model.BaseModel --> pypalmsens._methods.mixins.DataProcessingMixin
                

                pypalmsens._methods.mixins.GeneralMixin --> pypalmsens.FastCyclicVoltammetry
                                pypalmsens._methods.base_model.BaseModel --> pypalmsens._methods.mixins.GeneralMixin
                



              click pypalmsens.FastCyclicVoltammetry href "" "pypalmsens.FastCyclicVoltammetry"
              click pypalmsens._methods.base.BaseTechnique href "" "pypalmsens._methods.base.BaseTechnique"
              click pypalmsens._methods.mixins.PretreatmentMixin href "" "pypalmsens._methods.mixins.PretreatmentMixin"
              click pypalmsens._methods.mixins.VersusOCPMixin href "" "pypalmsens._methods.mixins.VersusOCPMixin"
              click pypalmsens._methods.mixins.PostMeasurementMixin href "" "pypalmsens._methods.mixins.PostMeasurementMixin"
              click pypalmsens._methods.mixins.IrDropCompensationMixin href "" "pypalmsens._methods.mixins.IrDropCompensationMixin"
              click pypalmsens._methods.mixins.DataProcessingMixin href "" "pypalmsens._methods.mixins.DataProcessingMixin"
              click pypalmsens._methods.mixins.GeneralMixin href "" "pypalmsens._methods.mixins.GeneralMixin"
              click pypalmsens._methods.base_model.BaseModel href "" "pypalmsens._methods.base_model.BaseModel"
            

Create fast cyclic voltammetry method parameters.

In Cyclic Voltammetry a cyclic potential scan is performed between two vertex potentials vertex1_potential and vertex2_potential. The scan can start (begin_potential) at one of these vertex potentials or anywhere in between.

A CV becomes a Fast CV if the scan rate in combination with step_potential results in a rate of over 2500 points / second (scan_rate / step_potential > 2500).

Methods:

• from_dict –

  Structure technique instance from dict.

• from_method_id –

  Create new instance of appropriate technique from method ID.

• to_dict –

  Return the technique instance as a new key/value dictionary mapping.

Attributes:

• begin_potential (float) –

  Potential where the scan starts and stops at in V.

• current_range (AllowedCurrentRanges) –

  Fixed current range.

• data_processing (DataProcessing) –

  Set the data processing settings.

• equilibration_time (float) –

  Equilibration time in s.

• general (General) –

  Sets general/other settings.

• ir_drop_compensation (IrDropCompensation) –

  Set the iR drop compensation settings.

• n_avg_scans (int) –

  The number of scan repetitions for averaging.

• n_equil_scans (int) –

  Number of equilibration scans.

• n_scans (int) –

  Number of repetitions for this scan.

• post_measurement (PostMeasurement) –

  Set the post measurement settings.

• pretreatment (Pretreatment) –

  Set the pretreatment settings.

• scanrate (float) –

  Scan rate in V/s.

• step_potential (float) –

  Potential step size in V.

• versus_ocp (VersusOCP) –

  Set the versus OCP settings.

• vertex1_potential (float) –

  First potential where direction reverses in V.

• vertex2_potential (float) –

  Second potential where direction reverses. V.

begin_potential: float = -0.5

current_range: AllowedCurrentRanges = '1uA'

data_processing: DataProcessing = Field(default_factory=DataProcessing)

equilibration_time: float = 0.0

general: General = Field(default_factory=General)

ir_drop_compensation: IrDropCompensation = Field(default_factory=IrDropCompensation)

n_avg_scans: int = 1

n_equil_scans: int = 1

n_scans: int = 1

post_measurement: PostMeasurement = Field(default_factory=PostMeasurement)

pretreatment: Pretreatment = Field(default_factory=Pretreatment)

scanrate: float = 1.0

step_potential: float = 0.1

versus_ocp: VersusOCP = Field(default_factory=VersusOCP)

vertex1_potential: float = 0.5

vertex2_potential: float = -0.5

from_dict(obj: dict[str, Any]) -> BaseTechnique

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@classmethod
def from_dict(cls, obj: dict[str, Any]) -> BaseTechnique:
    """Structure technique instance from dict.

    Opposite of `.to_dict()`"""
    return cls.model_validate(obj)

from_method_id(id: str) -> BaseTechnique

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@classmethod
def from_method_id(cls, id: str) -> BaseTechnique:
    """Create new instance of appropriate technique from method ID."""
    new = cls._registry[id]
    return new()

to_dict() -> dict[str, Any]

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def to_dict(self) -> dict[str, Any]:
    """Return the technique instance as a new key/value dictionary mapping."""
    return self.model_dump()