Dataset on moisture-based hydrothermal carbonization control, adsorption behaviour and surface characteristics of marine-based hydrochars for pollution removal

This dataset accompanies a study on pollutant adsorption by hydrochars produced via hydrothermal carbonization (HTC) of marine biowaste (fish and shrimp) and lignocellulosic biomass (corn), benchmarked against commercial activated carbon. It assembles material properties and adsorption performance under controlled laboratory conditions, enabling reproduction, cross‑comparison, and modeling.

The first component reports moisture content and additional water determination for raw biomasses and derived hydrochars. Gravimetric measurements provide mean values and standard deviations, allowing transparent HTC mass balance and yield calculations and clarifying the role of inherent/retained water on carbonization behavior and subsequent material stability.

The second component contains BET Rouquerol data from nitrogen adsorption–desorption isotherms. Both raw isotherm points and processed outputs are included: specific surface area, cumulative pore volume, average pore diameter, and pore‑size contributions (micro/meso/macro). Results are provided for activated carbon, corn hydrochar, fish hydrochar, and shrimp hydrochar. These data allow verification of BET calculations, assessment of textural differences among feedstocks (protein‑ and chitin‑rich marine residues vs lignocellulosic corn), and evaluation of how HTC processing translates into accessible surface and porosity.

The third component provides adsorption kinetics for seven representative contaminants—tetracycline, doxycycline hyclate, acetaminophen, methylene blue, methyl orange, 4‑nitrophenol, and potassium dichromate—measured on all four adsorbents. Time‑course uptake data are supplied alongside fitted parameters for pseudo‑first‑order, pseudo‑second‑order, and Two‑PFO models, with goodness‑of‑fit metrics. Outcomes are reported both as qe (mg g⁻¹) and as surface‑area‑normalized qe,mol (nmol m⁻²), facilitating comparison across materials with disparate surface areas and enabling mechanistic interpretation beyond mass‑based capacities.

Collectively, the dataset links feedstock composition, moisture behavior, and textural properties to adsorption kinetics across multiple pollutant classes. It is suitable for (i) reproducing and validating kinetic fits; (ii) benchmarking hydrochars against activated carbon; (iii) developing predictive relationships between material descriptors and adsorption efficiency; and (iv) use in meta‑analysis or machine‑learning workflows that require harmonized physicochemical inputs and adsorption outputs.