Abstract
Currently, there are a lot of discussions on the production of sustainable cement for construction purposes, unlike the conventional ordinary Portland cement (OPC), as its production, transportation, and application contribute to the generation of greenhouse gases, hence, climate change. Consequently, limestone, the primary material used to produce OPC, is non-renewable. Therefore, there is a need to use sustainable materials to make cementitious materials to achieve sustainable construction. This has led to a lot of research focussing on the valorisation of agricultural wastes and less economical, no-food lignocellulosic plants in producing sustainable and environmentally friendly cementitious materials commonly known as Supplementary Cementitious Materials (SCMs). The agrowastes ashes include rice husk ash (RHA), sugarcane bagasse ash (SCBA), and corn cob ash (CCA), among others. In contrast, the lignocellulosic plants’ ashes include common water reed ash (CWRA) and cyperus papyrus ash (CPA). There has been the belief that these pozzolanic materials are homogenous. However, these ashes are highly heterogeneous when they undergo microscopic analysis. Therefore, the current data paper provides Laser Diffraction Spectroscopy (LD) for Particle Size Distribution (PSD), Fourier-transform infrared spectroscopy (FT-IR), X-Ray Fluorescence (XRF), and Scanning Electron Microscope (SEM) data for unprocessed CWRA and CPA in the form of tables, micrographs, and figures for microscopic analysis. This data helps characterise and evaluate CWRA and CPA's potential as pozzolanic materials, especially as road construction materials, and will be beneficial for other scientists to better understand unprocessed CWRA and CPA mineral information development biologically inspired materials for biologically inspired materials sustainable development across many disciplines.
Keywords: Cement, Common water reed ash (CWRA), Cyperus papyrus ash (CPA), Scanning electron microscope (SEM), Sustainable development
Specifications Table
| Subject | Engineering |
| Specific subject area | Materials Characterisation |
| Type of data | Tables, Figures, and Images |
| How data were acquired | Particle size distribution, spectroscopic, and microscopic data used to classify lignocellulosic bio-pozzolans for engineering applications are explored. |
| Data format | Raw, Analysed |
| Parameters for data collection | Particle size distribution (PSD) data were obtained using laser diffraction (LS) mastersizer 2000.FT-IR spectra were obtained using BRUKER TENSOR 27 in 4500–500 cm−1.The chemical compositions of CWRA and CPA were characterised by the X-ray fluorescent (XRF) BRUKER model S8 TIGER XRF spectrometer.The Scanning Electron Microscope (SEM) data, the Hitachi FlexSEM 1000, was used after gold plating. |
| Data source location | Common Water Reed (Phragmites australis) were obtained on the banks of River Kafue in Zambia, while Cyperus papyrus (CP) was obtained from the banks of the River Nile in Uganda. Further details of the locations are provided in Table 3. |
| Description of data collection | The CWR and CP were sun-dried and burned on a hard surface to avoid contamination by foreign materials. After cooling, the CWRA and CPA were sampled in airtight polythene bags for microscopic analysis using Laser Diffraction Spectroscopy (LD) for Particle Size Distribution (PSD), FT-IR, XRF spectroscopy, and SEM. |
| Data accessibility | The data is available in the article (https://data.mendeley.com/datasets/n3vzfpkt9p/1). |
Value of the Data
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Microscopic analysis requires expensive equipment and is time-consuming. These data fully show the ultra-structures of the unprocessed CWRA and CPA as green pozzolanic materials based on Laser Diffraction, FT-IR, Raman spectra, and SEM micrographs be useful for researchers who do not have access to these types of equipment.
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The data presented here are valuable to researchers investigating the partial replacement of cement in all types of concrete with CWRA and CPA.
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Other researchers may use these data to better understand CWRA and CPA mineral information to develop biologically inspired materials and extract green nanoparticles/nanomaterials.
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The data are relevant for government agencies seeking a classification system to characterise ashes from CWRA and CPA as pozzolanic materials.
1. Data Description
The data obtained show particle size distribution (PSD) by Laser Diffraction (LS) method, FT-IR spectra, chemical composition XRF data, and SEM micrographs of CWRA and CPA specimen ashes. The collected data includes three (3) tables, three (3) figures, and raw data to be found on the following link: https://data.mendeley.com/datasets/n3vzfpkt9p/1. The three tables and figures are described within where they appear in the article. The supplementary data No. 1 include the raw data for particle size distribution as obtained from the Mastersizer 2000. The supplementary data No. 2 contains the raw data from the Fourier transform infrared analyses using the BRUKER Tensor 47. The supplementary data No. 3 contains the raw SEM micrographs as obtained from the Hitachi FlexSEM 1000.
1.1. Particle Size Distribution
Particle size distribution is a valuable characteristic of materials, especially pozzolanic materials. It defines the reactivity of the material. Coarse materials and fine materials react differently. The most common approach for expressing laser diffraction results is to report the D10, D50, and D90 values based on a volume distribution. Table 1 details the D10, D50, and D90 of CWRA and CPA under different LS conditions, including obscuration. The raw data is provided as Supplementary material No. 1-particle size distribution.
Table 1.
Details the particle size distribution of CWRA and CPA under different laser diffraction conditions.
| No. | Sample Name | d (0.1) | d (0.5) | d (0.9) |
|---|---|---|---|---|
| 1. | CWRA-Unprocessed_1.6bar_60% | 4.803 | 26.759 | 165.841 |
| 2. | CWRA-Unprocessed_1.6bar_60% | 6.131 | 36.793 | 214.794 |
| 3. | CWRA-Unprocessed_1.6bar_60% | 6.382 | 51.440 | 92.721 |
| 4. | CWRA-Unprocessed_1.6bar_60% - Average | 5.815 | 40.707 | 134.518 |
| 5. | Averaged Result_2measurements | 5.369 | 31.449 | 188.865 |
| 6. | CWRA-Unprocessed_1.6bar_50% | 4.618 | 25.575 | 165.264 |
| 7. | CWRA-Unprocessed_1.6bar_50% | 4.997 | 28.424 | 164.278 |
| 8. | CWRA-Unprocessed_1.6bar_50% | 10.731 | 68.923 | 325.313 |
| 9. | CWRA-Unprocessed_1.6bar_50% - Average | 5.983 | 39.417 | 246.063 |
| 10. | Averaged Result_CWRA_unp_1_4_5 | 4.800 | 26.900 | 165.114 |
| 11. | CWRA-Unprocessed_1.6bar_40% | 4.602 | 25.115 | 151.737 |
| 12. | CWRA-Unprocessed_1.6bar_40% | 5.030 | 28.261 | 155.859 |
| 13. | CWRA-Unprocessed_1.6bar_40% | 4.860 | 27.572 | 157.230 |
| 14. | CWRA-Unprocessed_1.6bar_40% - Average | 4.822 | 26.949 | 155.030 |
| 15. | CPA-Unprocessed_1.6BAR_40% | 5.333 | 32.124 | 166.574 |
| 16. | CPA-Unprocessed_1.6BAR_40% | 6.607 | 41.848 | 226.162 |
| 17. | CPA-Unprocessed_1.6BAR_40% | 6.585 | 40.597 | 195.952 |
| 18. | CPA-Unprocessed_1.6BAR_40% - Average | 6.093 | 38.015 | 196.081 |
| 19. | CPA-Unprocessed_1.6BAR_50% | 5.754 | 35.013 | 175.473 |
| 20. | CPA-Unprocessed_1.6BAR_50% | 6.697 | 40.614 | 189.629 |
| 21. | CPA-Unprocessed_1.6BAR_50% | 6.656 | 41.108 | 194.824 |
| 22. | CPA-Unprocessed_1.6BAR_50% - Average | 6.330 | 38.854 | 186.661 |
1.2. FT-IR Spectra
FTIR spectral identified the critical chemical compound existing in the unprocessed lignocellulosic bio-pozzolan (CWRA and CPA), as revealed in Fig. 1(https://data.mendeley.com/datasets/n3vzfpkt9p/1) [1,2]. The FT-IR raw data is provided as Supplementary material No. 2-FTIR Raw Data.
Fig. 1.
FT-IR Spectra for CWRA and CPA specimen.
1.3. X-Ray Fluorescence
The chemical compositions of CWRA and CPA were characterised by the X-ray fluorescent (XRF) BRUKER model S8 TIGER XRF spectrometer. Table 2 shows the results of the experiment which are uploaded on https://data.mendeley.com/datasets/n3vzfpkt9p/1.
Table 2.
The oxides composition of CWRA and CPA.
| Oxide | CPA Concentration | Oxide | CWRA Concentration |
|---|---|---|---|
| SiO2 | 33.70% | SiO2 | 63.40% |
| K2O | 28.20% | K2O | 7.60% |
| Cl | 4.10% | CaO | 5.90% |
| CaO | 2.70% | P2O5 | 2.60% |
| P2O5 | 2.10% | MgO | 2.20% |
| Na2O | 1.40% | Cl | 0.60% |
| MgO | 1.20% | Al2O3 | 0.60% |
| Al2O3 | 0.50% | Fe2O3 | 0.40% |
| MnO | 0.30% | SO3 | 0.30% |
| Fe2O3 | 0.30% | MnO | 0.10% |
| SO3 | 0.30% | Na2O | 0.05% |
| Br | 0.06% | TiO2 | 0.04% |
| BaO | 0.04% | SrO | 0.04% |
| TiO2 | 0.03% | BaO | 0.04% |
| ZnO | 0.02% | ZnO | 0.01% |
| SrO | 0.02% | ZrO2 | 0.01% |
| Rb2O | 0.01% | LOI | 16.50% |
| LOI | 24.30% |
1.4. Scanning Electron Microscopy
Fig. 2 shows the sampled SEM micrographs for CWRA and CPA specimens. The ImageJ software can analyse the micrographs to determine the diameters, areas and length [3,4]. The raw SEM micrographs are provided as Supplementary material No. 3-SEM Raw Data uploaded at https://data.mendeley.com/datasets/n3vzfpkt9p/1.
Fig. 2.
SEM micrographs for CWRA and CPA specimens at different magnifications.
2. Experimental Design, Materials and Methods
2.1. Synthesis of CWRA and CPA
The common water reeds were obtained on river Kafue in Zambia, while the cyperus papyrus were obtained from Adjumani on River Nile in Uganda. The details of the locations of collecting theses samples are provided in Table 3. The Fig. 3 shows the process chart for obtaining the open-air processed Common Water Reed (Phragmites australis) Ash and Cyperus Papyrus (Cyperus Papyrus) Ash. The general process involve cutting the samples from there natural habitats, drying them on hard surface and burning them in open-air under uncontrolled conditions. Therefore, representative samples are collected and packaged in airtight polythene bags for advanced materials caharacterisation.
Table 3.
Sample collection locations in Uganda and Zambia.
| Coordinates |
||||
|---|---|---|---|---|
| Name | Location/Country | Latitude | Longitude | Altitude |
| Common Water Reeds | Copperbelt, Zambia | 12°47′24.36"S | 28°15′26.48"E | ∼1176m |
| Cyperus Papyrus | Adjumani, Uganda | 3°26′3.61"N | 31°39′28.30"E | ∼621m |
Fig. 3.
CWRA and CPA specimen preparation (all the photos were taken by author).
2.2. Characterisation of the Unprocessed CWRA and CPA
2.2.1. Laser Diffraction Spectroscopy
Particle size distribution (PSD) data were obtained using laser diffraction (LS) Malvern Mastersizer 2000 as adopted by different authors [5], [6], [7], [8], [9]. The data were obtained at different obscuration between 60% to 40% under constant 1.6 bars.
2.2.2. Fourier-Transform Infrared Spectroscopy
Fourier-transform infrared spectroscopy (FT-IR) spectra were recorded using BRUKER TENSOR 27 in the range of 4500–500 cm−1 as reported by different researchers [10], [11], [12]. Several runs were made to get the most uniform spectra.
2.2.3. X-Ray Fluorescence (XRF) Spectroscopy
The chemical compositions of CWRA and CPA were characterised by a high performance X-Ray Flourescent (XRF) spectrometer (Model: S8 TIGER, Bruker, Germany), equipped with an Rh anode X-ray tube (4 kW, 60 kV and 170 mA). A detailed description of this spectrometer is reported by some researchers [13,14].
2.2.4. Scanning Electron Microscope (SEM) Investigation
The Scanning Electron Microscope (SEM) data, the Hitachi FlexSEM 1000, was used after gold plating as used by different studies [15,16].
Ethical Approval
It is not required for this study as no humans or animals were studied by any of the authors.
CRediT Author Statement
Martin Aluga: Performed conceptualization; Laboratory work and writing – original draft; Chewe Kambole: Performed writing – review & editing.
Declaration of Competing Interest
No competing interests.
Acknowledgements
The authors acknowledge the financial support of the Africa Centre of Excellence for Sustainable Mining, coordinated by the Copperbelt University and funded by the World Bank.
The ERASMUS+ KA107 (ICM) Academic Year 2021-2022, coordinated by the University of Valladolid and managed by SEPIE (Erasmus Plus Office in Spain), grant code: 13INUVA8579 promoted by the European Commission is acknowledged for the doctorate mobility program that made this work a reality.
We thank Prof. Maria Corcero Alonso for the general supervision to undertake the studies, Dr Ana Aquilez for her support in Particle Size Distribution, FT-IR spectra, Raman spectra, and Flex SEM data acquisition, Pablo Obregon for SEM data analysis, and Soraya Soraya Rodríguez Rojo for support in the PSD data analysis using laser diffraction technique.
Data Availability
References
- 1.Imoisili P.E., Ukoba K.O., Jen T.C. Green technology extraction and characterisation of silica nanoparticles from palm kernel shell ash via sol-gel. J. Mater. Res. Technol. 2020;9:307–313. doi: 10.1016/j.jmrt.2019.10.059. [DOI] [Google Scholar]
- 2.UCLA, Infrared Spectroscopy Table, (2001). https://www.chem.ucla.edu/∼bacher/General/30BL/IR/ir.html.
- 3.Armstrong M.R., Senthilnathan S., Balzer C.J., Shan B., Chen L., Mu B. Particle size studies to reveal crystallization mechanisms of the metal organic framework HKUST-1 during sonochemical synthesis. Ultrason. Sonochem. 2017;34:365–370. doi: 10.1016/J.ULTSONCH.2016.06.011. [DOI] [PubMed] [Google Scholar]
- 4.Govindhan R., Anbalagan S., Ravishankar M. HR-SEM and FT-IR dataset for green corrosion inhibition activity of 4-{[4-(pyridin-2-yl)piperazin-1-yl]methyl}aniline at CO2 atmosphere. Data Brief. 2021;39 doi: 10.1016/J.DIB.2021.107492. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.van Beers M.M.C., Slooten C., Meulenaar J., Sediq A.S., Verrijk R., Jiskoot W. Micro-Flow Imaging as a quantitative tool to assess size and agglomeration of PLGA microparticles. Eur. J. Pharm. Biopharm. 2017;117:91–104. doi: 10.1016/J.EJPB.2017.04.002. [DOI] [PubMed] [Google Scholar]
- 6.Guimarães A.A., Klein T.S., Medronho R.de A. Fish-hook effect in granulometric efficiency curves of hydrocyclones: a misuse of laser diffraction particle size analysers. Powder Technol. 2020;374:185–189. doi: 10.1016/J.POWTEC.2020.06.091. [DOI] [Google Scholar]
- 7.Kowalenko C.G., Babuin D. Inherent factors limiting the use of laser diffraction for determining particle size distributions of soil and related samples. Geoderma. 2013;193–194:22–28. doi: 10.1016/J.GEODERMA.2012.09.006. [DOI] [Google Scholar]
- 8.Darder M.L., Paz-González A., García-Tomillo A., Lado M., Wilson M.G. Comparing multifractal characteristics of soil particle size distributions calculated by Mie and Fraunhofer models from laser diffraction measurements. Appl. Math. Modell. 2021;94:36–48. doi: 10.1016/J.APM.2020.12.044. [DOI] [Google Scholar]
- 9.Polakowski C., Sochan A., Ryżak M., Beczek M., Mazur R., Majewska K., Turski M., Bieganowski A. Measurement of soil dry aggregate size distribution using the laser diffraction method, Soil Tillage Res. 2021;211 doi: 10.1016/J.STILL.2021.105023. [DOI] [Google Scholar]
- 10.Girija A.M., Arasu M.M.A., Devi D. Spectroscopic investigations (FTIR, UV-VIS, NMR) and DFT calculations on the molecular structure of Nω-Nitro-L-arginine. J. Indian Chem. Soc. 2022;99 doi: 10.1016/J.JICS.2022.100437. [DOI] [Google Scholar]
- 11.Tarhan İ., Derin Z., Akif Erdem M. The study of Middle Bronze Age pottery from Yassıtepe Höyük site in İzmir, Turkey, by FTIR and XRD with chemometrics. J. Archaeol. Sci. 2022;42 doi: 10.1016/J.JASREP.2022.103401. [DOI] [Google Scholar]
- 12.Kremmyda A., MacNaughtan W., Arapoglou D., Eliopoulos C., Metafa M., Harding S.E., Israilides C. Mushroom and cereal β-D-glucan solid state NMR and FTIR datasets. Data Brief. 2022;40 doi: 10.1016/J.DIB.2021.107765. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.H.S. Kainth, A. Upmanyu, H. Sharma, T. Singh, S. Kumar, Chemical state analysis of Cl Kα and Kβ1,3 X-ray emission lines using polychromatic WDXRF spectrometer, Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms. 416 (2018) 62–67. https://doi.org/10.1016/J.NIMB.2017.12.011.
- 14.Kainth H.S. Effect of chemical environment on K shell emission lines of transition and post transition compounds. J. Electron Spectrosc. Relat. Phenom. 2018;223:53–61. doi: 10.1016/J.ELSPEC.2017.12.010. [DOI] [Google Scholar]
- 15.Crippa G., Griesshaber E., Checa A.G., Harper E.M., Simonet Roda M., Schmahl W.W. SEM, EBSD, laser confocal microscopy and FE-SEM data from modern Glycymeris shell layers. Data Brief. 2020;33 doi: 10.1016/J.DIB.2020.106547. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Mazumder M., Ahmed R., Wajahat Ali A., Lee S.J. SEM and ESEM techniques used for analysis of asphalt binder and mixture: a state of the art review. Constr. Build. Mater. 2018;186:313–329. doi: 10.1016/J.CONBUILDMAT.2018.07.126. [DOI] [Google Scholar]
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