Limit of Detection

This example simulates the detection of small nanoparticles (90-150 nm diameter) in a flow cytometry setup using a dual-detector configuration (side and forward scatter). The simulation includes noise models, realistic fluidics, analog signal conditioning, digitization, triggering, and peak detection.

The main goal is to evaluate whether such particles produce detectable and distinguishable scatter signals in the presence of system noise and fluidic variability.

from FlowCyPy.units import ureg
from FlowCyPy import FlowCytometer
from FlowCyPy.fluidics import (
    distributions,
    FlowCell,
    Fluidics,
    ScattererCollection,
    populations,
)
from FlowCyPy.opto_electronics import (
    Detector,
    OptoElectronics,
    Digitizer,
    Amplifier,
    source,
    circuits,
)
from FlowCyPy.digital_processing import (
    DigitalProcessing,
    peak_locator,
    discriminator,
)

Optical Source

source = source.Gaussian(
    waist_z=10 * ureg.micrometer,
    waist_y=60 * ureg.micrometer,
    wavelength=405 * ureg.nanometer,
    optical_power=100 * ureg.milliwatt,
)

Flow Cell Configuration

flow_cell = FlowCell(
    sample_volume_flow=0.02 * ureg.microliter / ureg.second,
    sheath_volume_flow=0.1 * ureg.microliter / ureg.second,
    width=20 * ureg.micrometer,
    height=10 * ureg.micrometer,
)

Define Scatterer Populations (90–150 nm spheres)

scatterer_collection = ScattererCollection()

for size in [150, 125, 100, 75, 50]:
    pop = populations.SpherePopulation(
        name=f"{size} nm",
        concentration=5e9 * ureg.particle / ureg.milliliter,
        diameter=distributions.Delta(value=size * ureg.nanometer),
        refractive_index=distributions.Delta(value=1.36),
        medium_refractive_index=1.33,
    )
    scatterer_collection.add_population(pop)

scatterer_collection.dilute(factor=100)

fluidics = Fluidics(scatterer_collection=scatterer_collection, flow_cell=flow_cell)

digitizer = Digitizer(
    bit_depth=14, use_auto_range=True, sampling_rate=60 * ureg.megahertz
)

Detectors

detector_side = Detector(
    name="side",
    phi_angle=90 * ureg.degree,
    numerical_aperture=0.2,
    responsivity=1 * ureg.ampere / ureg.watt,
    dark_current=0.1 * ureg.nanoampere,
)

detector_forward = Detector(
    name="forward",
    phi_angle=0 * ureg.degree,
    numerical_aperture=0.2,
    responsivity=1 * ureg.ampere / ureg.watt,
    dark_current=0.1 * ureg.nanoampere,
)

Opto-Electronics Processing Pipeline

amplifier = Amplifier(
    gain=10_000 * ureg.volt / ureg.ampere, bandwidth=10 * ureg.megahertz
)

analog_processing = [
    circuits.BesselLowPass(cutoff_frequency=0.4 * ureg.megahertz, order=4, gain=2),
    circuits.BaselineRestorationServo(time_constant=50 * ureg.microsecond),
]

opto_electronics = OptoElectronics(
    digitizer=digitizer,
    analog_processing=analog_processing,
    detectors=[detector_side, detector_forward],
    source=source,
    amplifier=amplifier,
)

Digital Processing Pipeline

discriminator = discriminator.FixedWindow(
    trigger_channel="side",
    threshold="4sigma",
    max_triggers=-1,
    pre_buffer=128,
    post_buffer=128,
)

digital_processing = DigitalProcessing(
    discriminator=discriminator,
    peak_algorithm=peak_locator.GlobalPeakLocator(),
)

cytometer = FlowCytometer(
    fluidics=fluidics,
    background_power=0.1 * ureg.nanowatt,
)

run_record = cytometer.run(
    run_time=10.0 * ureg.millisecond,
    digital_processing=digital_processing,
    opto_electronics=opto_electronics,
)

Plot Raw Analog Signal

run_record.plot_analog()

<Figure size 640x480 with 3 Axes>

Plot Triggered Analog Signal Segments

run_record.plot_digital()

<Figure size 640x480 with 3 Axes>