Use of Siliceous and Calcareous Microalgae to Decarbonize Cement Production
Bibliographic record
Abstract
Production of ordinary portland cement (OPC) accounts for 7% of anthropogenic CO<sub>2</sub> emissions, mainly due to calcination of limestone and burning of fossil fuels for pyro-processing. Contrastingly, microalgae naturally uptake and sequester CO<sub>2</sub> through photosynthesis at a ratio of ~1.8 kg CO<sub>2</sub>/kg dried microalgal biomass. Researchers have already successfully demonstrated synergy between cement decarbonization efforts and microalgae cultivation through conversion of CO<sub>2</sub>-rich flue gas to microalgal biomass. Biomass is then valorized into high-value co-products, and in at least one case it is returned to the plant for use as an alternative fuel for clinkering (St. Marys Cement, Canada). Another potential synergy between cement decarbonization efforts and microalgae involves the exploitation of biomineralizers, most notably siliceous diatoms and calcareous coccolithophores. In the current work, the chemical reactivity of freshly cultured diatom frustules as a supplementary cementitious material (SCM) was explored (Chapters 4-6) for the first time. The chemical reactivity of <em>Thalassiosira pseudonana</em> frustules was relatively high (i.e., greater than a blast furnace slag, but lower than metakaolin). However, <em>Phaeodactylum tricornutum</em> frustules exhibited a lower chemical reactivity similar to a Class F fly ash. Overall, these data demonstrated not only the potential to grow highly reactive biominerals using diatoms but also the variability and potential tunability of diatom biosilica. With the goal of estimating the theoretical reduction in embodied carbon emissions of concrete mixtures incorporating microalgal biominerals as raw materials and/or SCMs, a life cycle assessment was also performed (Chapter 7). Replacement of OPC by diatom biosilica (<em>i.e.</em>, DB) at 5 wt% resulted in a 4.8% reduction in the upfront embodied carbon emissions of a 45 MPa concrete, and the upfront embodied carbon emissions were reduced significantly further when coccolithophore calcite was incorporated due to its nature as a net-CO<sub>2</sub> storing mineral. Specifically, the upfront embodied carbon emissions of a 45 MPa concrete were reduced by 62.8% when all the limestone in the cement raw meal was replaced with coccolithophore calcite. Taken together, the results of this dissertation work suggested that tremendous potential exists for biomineralizing microalgae, particularly siliceous diatoms and calcifying coccolithophores, to contribute to novel low-CO<sub>2</sub> cement biotechnologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".