The study effectively highlights the significance of biochar properties like quinone functional groups and elemental composition (e.g., nitrogen, nickel, and zinc) in facilitating reductive debromination. It’s intriguing to see such strong correlations emerge from the analysis. However, I’m curious; did the authors explore or consider potential mechanistic studies, such as real-time electron transfer measurements or spectroscopic techniques, to directly observe the role of these functional groups or elements during debromination? Additionally, given the environmental emphasis, how do you envision scaling this approach while managing potential challenges like metal leaching or biochar longevity in diverse remediation scenarios?
You derive significant positive correlations for N, Ni, P, Zn, and quinones (Table 3) and build a 3‑predictor multiple linear model (Eq. 4, R² = 0.98) using only the 6 most reactive biochars (n=6). With three predictors, the degrees of freedom are just 2—this model is almost certainly overfitted and has no predictive power. Why were the other 24 low‑reactivity biochars excluded from this regression? Including them would provide the necessary variance and statistical power; excluding them introduces severe selection bias and renders your p‑values meaningless.
You fit a pseudo‑first‑order model (Eq. 2) that asymptotically forces complete (100%) conversion of DBA to ethylene. However, your maximum observed debromination extent (ε₇d) is only 27%, and for SOYM6 adsorption accounts for just ~3% of removal. Fitting a model that assumes 100% conversion severely distorts the rate constant *k* (explaining why BARST6 with low ε₇d shows a high *k*). More critically, what happens to the remaining ~70% of DBA in the SOYM systems? The reaction stalls, yet you do not discuss GR passivation, product inhibition, or irreversible surface binding—so your derived *k* values are not true reaction rates but artifacts of a flawed asymptotic assumption.