DOI: 10.5281/zenodo.18414743
title: Supplementary data to "Optimizing feather hydrolysate via machine learning for microbial recycling of waste concrete fines" paper

description: Supplementary data to „Optimizing feather hydrolysate via machine learning for microbial recycling of waste concrete fines”

Last updated: 2025-01-29

DOI: 10.5281/zenodo.18414744

______Contact______

* Hana Stiborová

* hana.stiborova@vscht.cz

* +420 220 44 5204

* ORCID: 0000-0003-3424-4225

* Dept. of Biochemistry and Microbiology. Faculty of Food and Biochemistry Technology, University of chemistry and Technology, Prague

* Technická 5, 166 28, Prague 6, Czech Republic

 

* Henrietta Ottová

* ottovah@vscht.cz

* +420 220 44 5204

* ORCID: 0009-0008-8851-3180

* Dept. of Biochemistry and Microbiology. Faculty of Food and Biochemistry Technology, University of chemistry and Technology, Prague

* Technická 5, 166 28, Prague 6, Czech Republic

______Licence______

*Dataset for Optimizing feather hydrolysate via machine learning for microbial recycling of waste concrete fines 2025 is licensed under CC BY-NC 4.0



*Licence information: https://creativecommons.org/licenses/by/4.0/


This dataset will be public after publication.

______About the dataset______

The concrete industry faces significant challenges from CO2 emissions and the disposal of waste concrete fines (WCF). Microbially induced calcite precipitation (MICP) can bind WCF into bioconcrete, but the high cost of commercial culture media hinders its application. We aimed to develop a low-cost, sustainable medium from waste chicken feather hydrolysate, optimized via machine learning for two MICP bacteria, Sporosarcina pasteurii DSM 33 and Sutcliffiella cohnii DSM 6307.

The model successfully optimized the feather hydrolysis process based on bacterial growth by exploring parameters such as hydrolysis duration, temperature, and the concentration of feathers and hydroxide. The resulting bioconcrete samples were analysed by X-ray diffraction (XRD), porosimetry, thermogravimetry analysis (TGA), indentation and scanning electron microscope (SEM).

______Methods of data collection______

*Hydrolysate preparation

To navigate the complex relationships between the hydrolysis parameters and bacterial growth, a machine learning approach was adopted. The following parameters were optimized: 1) temperature (°C), 2) hydrolysis duration (h), 3) feather content (g L⁻¹), 4) KOH concentration (g L⁻¹), and 5) yeast extract (YE) concentration (g L⁻¹). Within these ranges, the StatEase software generated 46 experimental conditions for hydrolysate preparation (see hydrolysates_table). Accordingly, 46 distinct feather hydrolysates were prepared under the specified conditions and modified – urea, respectively sodium sesquicarbonate, and YE were added, followed by pH adjustment. Then the hydrolysates were centrifuged at 17,000g for 12 min and filtered through a 0.45 µm membrane filter. These modified hydrolysates served as the culture medium for two bacterial strains, Sporosarcina pasteurii DSM 33 and Sporosarcina cohnii DSM 6307.

*OD600

microEON plate reader (BioTek Instruments)

* Sample preparation

WCF from highway (WCF-H) and WCF from column (WCF-C) were used for this experiment. WCF suspension was prepared by mixing 10 g of WCF, 1 mL of HCl (2 mol L-1), and 10 mL of bacterial suspension (OD = 5). The mixture was transferred into containers (diameter 2.1 cm). After 24 h, once the suspension had settled, 10 mL of biocementation solution (BS) was added every 48 h for 28 days, and the samples were stored at 28 °C (S. pasteurii) or at 30 °C (S. cohnii). More details in the manuscript.

* XRD

HW: Aeris XRD analyser (Malvern Panalytical) equipped with a cobalt anode and Fe Kb filter over the 2θ range of 5–85°

* SEM 

     HW: scanning electron microscope (SEM) Phenom Element Identification Version 3.8.6.0

*TGA

STA 449 F5 Jupiter instrument (NETZSCH-Gerätebau GmbH), argon atmosphere with a gas flow rate of 40 mL min-1

* Porosimetry

Pascal 140 and Pascal 440 porosimeters (Thermo Fisher Scientific Inc.)

* Indentation

MTS Criterion Series 40 testing machine, loading rate of 0.5 mm min-1, a conical steel indenter, tip diameter of 0.15 mm, a base diameter of 12.2 mm, and an apex angle of 60°.

______Methods of data processing______

The OD600 data were corrected by subtracting the negative control.

______File name structure_____

* [ProjectShortName]_[DataType]_[ShortDescription]_[Date]_[Version].[FileExtension] 

* e.g. MICP-WCF_XRD_bioconcreteSamples_20251208.xlsx

______File formats______

* SEM – tiff pictures

* TGA, XRD, Porosimetry, OD600, Indentation – csv

*Read me file – txt

______Date formats______

* YYYY-MM-DD

* HH:MM:SS 24hr format

______Units and abbreviations______

* XRD X-ray diffraction (XRD)

* TGA thermogravimetry analysis 

* SEM scanning electron microscope

* Bioconcrete samples: the type of cultivation medium used (Med = commercial medium, Feath = feather hydrolysate, Ster = sterile, saline solution), type of WCF (H = highway WCF, C = column WCF), and used bacterial strain (SC = Sutcliffiella cohnii, 30 °C, SP = Sporosarcina pasteurii, 28 °C).