Articles written on FBRC

Maximilian Voigt wrote an article on FBRC’s efforts that was published in heise online (EN, DE), we are grateful for his interview as it really helped spread the message of what we are trying to accomplish.

FBRC was also featured in Hackaday (again!).

Talks

Prof. Sanli Faez presented FBRC work in the context of the FAIR Battery project at OSHWA 2025, video below:

Daniel presented FBRC at FOSDEM 2025:

Kirk presented the project at the annual French network meeting for RFB research, slides are here and below:

Limitations of the first iteration

Daniel put the first version of the kit through many extended cycling tests, and it was found to lose sealing performance over the timescale of months. We suspect this is due to creep causing reduced sealing pressure in the middle of the cell due to bending moments over long timescales.

Another issue with this version is the tight, interference fit between the copper plate and the plastic body. This was a known trade-off, because we wanted to avoid the requirement to source and machine holes in bipolar plate material (hard to source and expensive). With this approach we were able to use grafoil (easy to get and cheap) and keep the machining required to just polypropylene (also easy to get). The fit needed to be very precise, however, which created a manufacturing constraint.

Improved performance with integrated barbs

A redesign of the cell addressed these shortcomings. Because a barb design had been successfully FDM-printed in polypropylene, for the reservoirs, we adopted this into the flow frame. This also eliminates the needs for drilling and tapping threads, source barbs, and several potentially leaky interfaces. Additionally, this design does not require any close interference fits or precise alignments, making manufacturing easier.

This approach is similar to that adopted by the Nockemann Group and coworkers, with a key difference. In that paper, they struggled to get polypropylene FDM prints to seal properly, and opted for ABS. Our initial chemistry, zinc-iodide, is chemically incompatible with ABS when charged, so we need to use polypropylene. Large FDM polypropylene prints are, however, not rigid, dimensionally accurate, or leakproof enough to seal the cell well.

In our new design we have decoupled the chemical and mechanical requirements of the flow frame-endplate assembly. The polypropylene barbed flow frames print easily and accurately. An FDM-printable endplate, in a rigid, yet chemically incompatible material like PLA or PETG, can fit over the current collector and flow frame barbs, providing rigidity while never contacting the electrolyte.

In this way, we have reliably FDM-printed leakproof cells with polypropylene as the only wetted polymer material.

The cell is undergoing development and the first release will be marked as v1.0.0. Also, we plan to write a paper on this comparing the cell to the literature to establish how it is and isn’t different from previous approaches.

Preliminary results

Daniel has been running several tests to iterate towards a stable platform chemistry/cell configuration. Here is a representative test, complete with a failure of one peristaltic pump and what that looks like. This test didn’t have the right tubing material, and so some charged electrolyte leaked through the cathode pump causing corrosion on the pump shaft and pump failure, leading to a loss of flow and rapid performance decay. We present it here, because the initial performance was motivating, and because this is what research looks like!

Here is the raw data (62 MB) of the test from the open-source MYSTAT potentiostat we use, generated with the conditions shown in Table 1. There are three columns for charge/discharge data: time, voltage, and current.

Below are some plots we can generate by processing the raw data in order to determine cell performance. The code block is expandable if you’d like to view it or run it yourself.

import pandas as pd
from tqdm import tqdm, notebook
import numpy as np
import scipy
import plotly.express as px
import plotly.graph_objects as go
import kaleido
from IPython.display import Image


tqdm_disabled = True  # for website, change to False for local work

sampling = True

MIN_POINTS0 = 500
DIFF_LIMIT = 0.1

TOTAL_VOLUME = 10  # electrolyte volume in mL

filenames = ["Dec27_Ki4_NH4Cl2_nonCondFelt_3LayerPhoto_3mmFeltplusnoncond_2.csv"]

all_data = []
for f in filenames:
    if len(all_data) == 0:
        if "Potentiostat_project" in f:
            all_data.append(pd.read_csv(f))
        else:
            all_data.append(pd.read_csv(f, delimiter="\t"))
    else:
        df0 = pd.read_csv(f, delimiter="\t")
        df0["Elapsed time(s)"] += all_data[-1]["Elapsed time(s)"].iat[-1]
        all_data.append(df0)

df = pd.concat(all_data, ignore_index=True)

df["mean_current"] = df["Current(A)"].rolling(1).mean()
df["prev_current"] = df["mean_current"].shift(1)
df["VChange"] = df["Potential(V)"].diff().abs()
df["is_change"] = (
    ((df["mean_current"] > 0) & (df["prev_current"] < 0))
    | ((df["mean_current"] < 0) & (df["prev_current"] > 0))
).astype(int)

idx_changes = list(df[df["is_change"] == 1].index)
idx_changes.append(len(df) - 1)

all_curves = []
idx_start = 0
for idx in tqdm(idx_changes, disable=tqdm_disabled):
    if len(df.iloc[idx_start:idx, :]) > 50:
        all_curves.append(df.iloc[idx_start:idx, :])
    idx_start = idx

results = []
n_curves = np.max([1, int(np.floor(len(all_curves) / 2))])

for CN in notebook.tnrange(n_curves, disable=tqdm_disabled):
    CURVE_N1 = CN * 2
    CURVE_N2 = CN * 2 + 1

    # Process charge data

    if sampling:
        N_TERM_POINTS = int(np.min([MIN_POINTS0, len(all_curves[CURVE_N1]) / 2.0]))
        MIN_POINTS = int(
            np.min([MIN_POINTS0, len(all_curves[CURVE_N1]) - N_TERM_POINTS * 2])
        )

        df0 = pd.concat(
            [
                all_curves[CURVE_N1].iloc[:N_TERM_POINTS],
                all_curves[CURVE_N1]
                .iloc[N_TERM_POINTS:-N_TERM_POINTS]
                .sample(n=MIN_POINTS),
                all_curves[CURVE_N1].iloc[-N_TERM_POINTS:],
            ]
        ).sort_values("Elapsed time(s)", ascending=True)
        df0 = df0[df0["VChange"] < DIFF_LIMIT]
    else:
        df0 = all_curves[CURVE_N1].copy()
    df0["mAh"] = np.abs(
        scipy.integrate.cumulative_trapezoid(
            df0["Current(A)"], df0["Elapsed time(s)"], initial=0
        )
        * 1000.0
        / 3600.0
    )
    total_energy0 = scipy.integrate.cumulative_trapezoid(
        df0["Current(A)"].abs() * df0["Potential(V)"],
        df0["Elapsed time(s)"],
        initial=0.0,
    )[-1]

    # Process discharge data
    if sampling:
        N_TERM_POINTS = int(np.min([MIN_POINTS0, len(all_curves[CURVE_N2]) / 2.0]))
        MIN_POINTS = int(
            np.min([MIN_POINTS0, len(all_curves[CURVE_N2]) - N_TERM_POINTS * 2])
        )
        df1 = pd.concat(
            [
                all_curves[CURVE_N2].iloc[:N_TERM_POINTS],
                all_curves[CURVE_N2]
                .iloc[N_TERM_POINTS:-N_TERM_POINTS]
                .sample(n=MIN_POINTS),
                all_curves[CURVE_N2].iloc[-N_TERM_POINTS:],
            ]
        ).sort_values("Elapsed time(s)", ascending=True)
        df1 = df1[df1["VChange"] < DIFF_LIMIT]
    else:
        df1 = all_curves[CURVE_N2].copy()

    df1["mAh"] = np.abs(
        scipy.integrate.cumulative_trapezoid(
            df1["Current(A)"], df1["Elapsed time(s)"], initial=0.0
        )
        * 1000.0
        / 3600.0
    )
    total_energy1 = scipy.integrate.cumulative_trapezoid(
        df1["Current(A)"].abs() * df1["Potential(V)"],
        df1["Elapsed time(s)"],
        initial=0.0,
    )[-1]

    CE = 100.0 * (df1["mAh"].iloc[-1] / df0["mAh"].iloc[-1])
    EE = 100.0 * (total_energy1 / total_energy0)
    VE = 100.0 * EE / CE
    results.append(
        {
            "Number": CN + 1,
            "CE": CE,
            "VE": VE,
            "EE": EE,
            "Charge_potential": df0["Potential(V)"].mean(),
            "Discharge_potential": df1["Potential(V)"].mean(),
            "Charge_stored": df1["mAh"].iloc[-1] / TOTAL_VOLUME,
            "Energy_density_discharge": total_energy1 / TOTAL_VOLUME / 3600.0 * 1000,
        }
    )

    # Save the modified DataFrames back to the all_curves list
    all_curves[CURVE_N1] = df0
    all_curves[CURVE_N2] = df1

results_df = pd.DataFrame(results)

if not tqdm_disabled:
    print(results_df)
    print("")
    print(results_df.mean())

# Plot charge/discharge curves
fig1 = go.Figure()
for CN in range(n_curves):
    CURVE_N1 = CN * 2
    CURVE_N2 = CN * 2 + 1
    fig1.add_trace(
        go.Scatter(
            x=all_curves[CURVE_N1]["mAh"] / TOTAL_VOLUME,
            y=all_curves[CURVE_N1]["Potential(V)"],
            mode="lines",
            name=f"Charge {CN+1}",
            line=dict(color="blue", dash="solid"),
        )
    )
    fig1.add_trace(
        go.Scatter(
            x=all_curves[CURVE_N2]["mAh"] / TOTAL_VOLUME,
            y=all_curves[CURVE_N2]["Potential(V)"],
            mode="lines",
            name=f"Discharge {CN+1}",
            line=dict(color="grey", dash="solid"),
        )
    )
fig1.update_layout(
    title="Charge and Discharge Curves",
    xaxis_title="Capacity (Ah/L)",
    yaxis_title="Potential (V)",
)

fig1.show()

# fig1.write_image('fig1.pdf')

Here in Figure 1 you can see representative charge/discharge curves for the first 7 cycles. In cycle 8, the cathode pump fails during discharge, and negatively impacts all subsequent cycling.

# Plot efficiency
fig2 = px.scatter(
    results_df,
    x="Number",
    y=["CE", "VE", "EE"],
    labels={"value": "Efficiency (%)", "variable": "Metric"},
    title="Efficiency by Cycle",
)
fig2.update_traces(mode="lines+markers")
fig2.show()

# Plot potential
# fig3 = px.scatter(results_df, x="Number", y=["Charge_potential", "Discharge_potential"],
#                labels={"value": "Potential (V)", "variable": "Metric", "Number": "Cycle Number"},
#                title="Mean Potential by Cycle")
# fig3.show()

Now in Figure 2 we determine coulombic, energy, and voltaic efficiencies for the cycles.

Energy efficiency () is how much energy we got out of the cell on discharge divided by how much went into charging it.

Coulombic efficiency () is how much electrical charge (mAh, coulombs, electrons…) we got out of the cell on discharge divided by how much went into charging it. In this system, we have less than 100% coulombic efficiency because of self-discharge. Because we are using a porous, nonselective separator (paper) charged triiodide can pass through and recombine with zinc on the anode. This hurts our coulombic and energy efficiencies on each cycle but does not permanently degrade the battery.

Voltaic efficiency () is akin to energy efficiency if we assume 100% coulombic efficiency; another way of looking at it is the average level of energy of electrons that entered the cell on charge vs. the average energy of electrons leaving on discharge, ignoring the total amount of electrons.

The relationship between the three efficiencies is straightforward: energy efficiency is the product of coulombic and voltaic efficiencies:

# Plot discharge charge capacity
# fig4 = px.scatter(results_df, x="Number", y="Charge_stored",
#                labels={"Charge_stored": "Discharge Capacity (Ah/L)", "Number": "Cycle Number"},
#                title="Discharge Capacity by Cycle")
# fig4.show()

# Plot discharge energy capacity
fig5 = px.scatter(
    results_df,
    x="Number",
    y="Energy_density_discharge",
    labels={
        "Energy_density_discharge": "Energy Density on Discharge (Wh/L)",
        "Number": "Cycle Number",
    },
    title="Energy Density by Cycle",
)
fig5.show()

Then, we can calculate the energy density accessible on discharge for each cycle.

table_df = (
    results_df[:7]
    .describe()
    .loc[["mean", "std"]]
    .round(decimals=1)
    .drop(columns=["Number", "Charge_potential", "Discharge_potential"])
    .rename(
        columns={
            "CE": "Coulombic Efficiency (%)",
            "EE": "Energy Efficiency (%)",
            "VE": "Voltaic Efficiency (%)",
            "Charge_stored": "Discharge Capacity (Ah/L)",
            "Energy_density_discharge": "Energy Density (Wh/L)",
        }
    )
    .transpose()
)
table_df = (
    table_df["mean"].astype("str") + " ± " + table_df["std"].astype("str")
)
if not tqdm_disabled:
    print(table_df.to_markdown())

The means and standard deviations of these metrics for the first 7 cycles are in Table 2.

We are still working on selecting the correct tubing and internal cell components for this system. An important component is the polypropylene felt spacer included on the anode side where zinc plates/strips. You can see a cross-section of this felt in the charged state, showing zinc plating into the felt from left to right.

Presentation

For our first-ever public presentation of the project, we felt well-received. At least two members of the audience were co-authors on papers we cited as examples of other academic cell designs.

Hands-on assembly

Right after the presentation we got into assembling the flow cells. People then brought the assembled cell to a station where we would pump pure water through the cell and check for leaks.

We got a lot of useful feedback during this workshop, both directly and indirectly. A major one being that most people don’t know how to use a torque wrench! I will add that the only team that built a leak-proof cell on the first try was the team of mechanical engineers… who how to use a torque wrench. We will add this to the documentation and workshop instructions in the future now.

Good things

• “easy to assemble”

• “easy to align”

• “sturdy”

• “CHEAP ! ! !”

• “fun activity”

• “nice/understandable demonstration” (x2)

Things to improve

• “fittings hard to fit” (presumably barbed fittings)

• graphite foil is fragile/flimsy (x2)

• need to include o-rings on drawing

• indicate in instructions that some parts are not symmetrical

• square copper plate with can be placed two ways, one which results in a working cell, the other way which results in a leaky cell

• could add alignment options for quicker assembly

• numbering of gaskets

• flow field design can be improved / is very simple currently

• overtightening of barbs and/or cell bolts

• add washers for better pressure distribution

• a symmetrical flow frame could avoid assembly in the wrong orientation

• threaded hole for barb should be further from bolt hole

• barbs are vulnerable to overtightening (at least one group overtightened and sheared them off completely)

• closed and tightened cell appears slighly bigger in middle

• (silicone) rubber gasket can loosen over time, more rigid plastic gasket might avoid this

• possible to close cell with a clamp instead of bolts?

Roadmap for the test cell

We will complete the entire documentation for the version of the cell we produced for the workshop, and mark this version as the “first release” of the cell. We’ll then incorporate any breaking design changes from the feedback just mentioned which will go into the second release.

One design change that could address multiple feedback points is adopting some poka-yoke design strategies, such as making both the copper current collector and corresponding recess in the cell body rectangular, so that it can only be inserted one way.

Another suggestion was to address the ordering of gaskets and flow frames by placing a notch or other visual indicator along the external perimeter of each part, so that when assembled there should be a large visual indicator showing that the notches of all parts are aligned and therefore assembled in the correct order.