Messai × Helmholtz-UFZ
Live collaboration briefing
The data layer for mechanistic bioelectrochemistry.
Corpus-scale parameters, priors, and model setups — mapped to Dr. Benjamin Korth’s work on the thermodynamics, kinetics, and energy efficiency of electroactive microorganisms.
01 · Model setups you’d recognise
Open any configuration in the lab
Each carries its governing physics — the coupled PDEs and kinetics are the model. Click a card to open its detail in the live catalog.
Microbial Electrosynthesis
Carbon FixerMFC run in reverse: electrons + CO₂ → acetate/butyrate. Sporomusa ovata on a cathode at −0.4…−1.2 V vs SHE.
Butler–Volmer + HER · CO₂/HCO₃⁻/CO₃²⁻ equilibria + mass transfer · Wood–Ljungdahl flux balance · multi-product Monod
Dual-Chamber MEC
Hydrogen EngineAnode exoelectrogens oxidise acetate; ~0.4–0.8 V bias drives H₂ evolution at the cathode. Energy yield 150–400% over input.
Butler–Volmer w/ biofilm conduction (Marcus–Hush) · Monod growth · Volmer–Heyrovsky–Tafel HER · Nernst–Planck across CEM
Resource-Recovery / MDC
Circular MinerThree-chamber bioelectrodialysis: cathodic reduction + ion recovery + desalination. Net energy −0.5…+0.3 kWh·m⁻³.
Nernst–Planck multi-ion transport · Donnan equilibria at membranes · Butler–Volmer at electrodes · stack charge balance
Continuous Microfluidic BES
Laminar ShieldMembraneless laminar co-flow at low Re; acetate feed at short residence time — a clean testbed for continuous acetate-uptake kinetics.
Navier–Stokes (low-Re) · convection–diffusion–reaction · Butler–Volmer boundaries · Monod biofilm · δ≈√(D·L/U)
02 · Meta-analysis, sliced by system class
Pooled Bayesian priors as forest plots
Pooled + per-system-class posteriors with 95% credible intervals — the per-class split (MES / MEC / MFC) is the story. Amber ⚠ marks a stratum whose MCMC fit hasn’t converged: flagged, never hidden. Open any card in the live explorer.
Coulombic efficiency
Open in explorer →Forest plot compares MES vs MEC vs MFC — the per-class split is the story.
Areal current density
Open in explorer →Log-scale prior; heavy-tailed spread reflects real cross-corpus variance.
Applied potential
Open in explorer →Energy input distribution for MEC — the energetics lever.
COD removal
Open in explorer →Remediation performance across cathodic and mixed systems.
03 · Priors you can drop into your models
Download the posteriors as sampling specs
The export ships each fit as a ready-to-sample distribution (natural-log lognormal for heavy-tailed quantities, normal for linear) with n, units, provenance, and caveats — straight into PyMC / Stan / scipy.
| Parameter | Class | n | Central | 95% CI | Flags |
|---|---|---|---|---|---|
| coulombic_efficiency | pooled | 146 | 39.1%mean | 33.8% – 44.2% | failed diagnosticspathological Student-t tail (ν=0.71 < 1, no finite mean) |
| MEC | 11 | 61%mean | 49.6% – 72.3% | usablemoderate support (n_papers=11) | |
| MES | 17 | 68.4%mean | 54.1% – 81.3% | failed diagnosticsLOO unreliable (high Pareto-k) — influential outliers dominate the fit | |
| MFC | 72 | 22%mean | 19.3% – 24.6% | unusablewithin-study variance not identified (σ_within≈2.9e-11); partial pooling would let any user data crush the prior | |
| current_density_areal | pooledlog | 200 | 0.32 A/m^2median | 0.285 A/m^2 – 0.652 A/m^2 | trustworthy |
| MEClog | 11 | 3.3 A/m^2median | 1.26 A/m^2 – 5.86 A/m^2 | usablemoderate support (n_papers=11) | |
| MESlog | 17 | 1.27 A/m^2median | 0.999 A/m^2 – 21.3 A/m^2 | usablemoderate support (n_papers=17) | |
| MFClog | 108 | 0.249 A/m^2median | 0.149 A/m^2 – 0.441 A/m^2 | usableconvergence not confirmed | |
| cod_removal | pooled | 273 | 72.4%mean | 69.6% – 75.1% | trustworthy |
| MEC | 19 | 71.1%mean | 59.4% – 80.5% | failed diagnosticsLOO unreliable (high Pareto-k) — influential outliers dominate the fit | |
| MES | 17 | 82.9%mean | 80.9% – 84.8% | unusablewithin-study variance not identified (σ_within≈3.0e-14); partial pooling would let any user data crush the prior | |
| MFC | 160 | 73%mean | 69.9% – 76.3% | usableconvergence not confirmed | |
| applied_potential | pooled | 64 | 0.358 Vmean | 0.184 V – 0.536 V | failed diagnosticsLOO unreliable (high Pareto-k) — influential outliers dominate the fit |
| MEC | 16 | 0.838 Vmean | 0.586 V – 1.08 V | failed diagnosticsLOO unreliable (high Pareto-k) — influential outliers dominate the fit | |
| MFC | 11 | -0.127 Vmean | -0.157 V – -0.0554 V | unusablewithin-study variance not identified (σ_within≈5.1e-8); partial pooling would let any user data crush the prior |
Central = geometric median (log fits) or posterior mean (linear). sparse = n<10; approx = MCMC not fully converged.
04 · Continuous-BES acetate kinetics
The acetate-uptake picture your models formalise
Continuous Geobacter biofilms take up acetate with Monod kinetics, q = q_max·S/(K_S+S). The corpus fits the observable ingredients below — but not K_S or q_max themselves. That gap is exactly where your continuous-BES data closes the loop.
Plateau anchored on the corpus areal-current prior (median 0.32 A/m^2). K_S is left unknown — the corpus does not fit it.
| Kinetic quantity | n | Central | 95% CI | Flags |
|---|---|---|---|---|
| Substrate concentrationlog | 81 | 500 mg/L | 248 mg/L – 781 mg/L | |
| Acetate concentrationlog | 38 | 550 mg/L | 440 mg/L – 796 mg/L | approx |
| Areal current densitylog | 200 | 0.32 A/m^2 | 0.285 A/m^2 – 0.652 A/m^2 | |
| Exchange current density (j₀)log | 6 | 0.0827 A/m^2 | 0.00827 A/m^2 – 0.993 A/m^2 | sparse |
| Specific growth rate (µ) | 5 | 0.125 1/h | -0.0364 1/h – 0.302 1/h | sparseapprox |
| Biofilm thicknesslog | 17 | 0.0000200 m | 0.00000427 m – 0.0000714 m | |
| Flow ratelog | 84 | 0.6 L/h | 0.359 L/h – 0.645 L/h | approx |
What the corpus doesn’t yet fit — your continuous-BES data would seed these
- K_S — Half-saturation constant: the acetate-affinity term in Monod uptake q = q_max·S/(K_S+S) — not extracted corpus-wide
- q_max — Max specific uptake rate: the saturation plateau of acetate uptake — not extracted corpus-wide
05 · Fine-tune with your own data
Turn the corpus prior into your posterior
Paste your own measurements and pick a base stratum. MESSAI runs a closed-form partial-pooling update — corpus prior ⊕ your data → your posterior — in the same sampling-spec format you download above. It reports the shrinkage weight w (how much is borrowed from the corpus) and hard-refuses to pool onto a fit that didn’t converge. No data leaves your browser except the numbers you paste.
Where the data is soft — stated up front
- Extraction is noisy — cross-corpus scatter is large (power density CoV ≈ 10³%); every value ships with n, units, and range.
- Coverage is partial — a substantial share of indexed papers are not yet joined to extracted values, so distributions reflect the extracted subset.
- Sparse or non-converged fits are flagged, never hidden.
Priors served from the per-class hierarchical v2 artifact (PyMC NUTS, Student-t). Model setups and meta-analysis link to the live lab and meta-analysis surfaces. Prepared as a collaboration briefing for Dr. Benjamin Korth, Helmholtz Centre for Environmental Research — UFZ.