| 1. |
- Cousins, Dylan S., et al.
(författare)
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Near-Infrared Spectroscopy can Predict Anatomical Abundance in Corn Stover
- 2022
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Ingår i: Frontiers in Energy Research. - : Frontiers Media S.A.. - 2296-598X. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Feedstock heterogeneity is a key challenge impacting the deconstruction and conversion of herbaceous lignocellulosic biomass to biobased fuels, chemicals, and materials. Upstream processing to homogenize biomass feedstock streams into their anatomical components via air classification allows for a more tailored approach to subsequent mechanical and chemical processing. Here, we show that differing corn stover anatomical tissues respond differently to pretreatment and enzymatic hydrolysis and therefore, a one-size-fits-all approach to chemical processing biomass is inappropriate. To inform on-line downstream processing, a robust and high-throughput analytical technique is needed to quantitatively characterize the separated biomass. Predictive correlation of near-infrared spectra to biomass chemical composition is such a technique. Here, we demonstrate the capability of models developed using an “off-the-shelf,” industrially relevant spectrometer with limited spectral range to make strong predictions of both cell wall chemical composition and the relative abundance of anatomical components of the corn stover, the latter for the first time ever. Gaussian process regression (GPR) yields stronger correlations (average R2v = 88% for chemical composition and 95% for anatomical relative abundance) than the more commonly used partial least squares (PLS) regression (average R2v = 84% for chemical composition and 92% for anatomical relative abundance). In nearly all cases, both GPR and PLS outperform models generated using neural networks. These results highlight the potential for coupling NIRS with predictive models based on GPR due to the potential to yield more robust correlations.
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| 2. |
- Cousins, Dylan S., et al.
(författare)
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Particle classification by image analysis improves understanding of corn stover degradation mechanisms during deconstruction
- 2023
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Ingår i: Industrial crops and products (Print). - : Elsevier B.V.. - 0926-6690 .- 1872-633X. ; 193
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Tidskriftsartikel (refereegranskat)abstract
- Biomass feedstock heterogeneity is a principal roadblock to implementation of the biorefinery concept. Even within an identical cultivar of corn stover, different bales contain not only varying abundance moisture, ash, glucan, and other chemical compounds, but also varying abundance of tissue anatomies (e.g., leaf, husk, cob, or stalk). These different anatomical components not only differ in their response to pretreatment and enzymatic hydrolysis to glucose, but also vary in their mechanical and conveyance properties. Although this heterogeneous nature of corn stover feedstock has been identified as a challenge, a fundamental knowledge gap of how these tissues behave during biorefining processing remains. In this work, we demonstrate the use of a commercial fiber image analyzer typically used for wood fiber characterization to monitor the particle size and shapes of non-woody feedstock during milling, pretreatment, and hydrolysis. Additionally, we present novel use of Gaussian process classification to distinguish bundle, parenchyma, and fiber particles to an accuracy of 96.4%. Quantitative probability distribution plots for characteristics such as length and roundness allow elucidation of particle morphology as pretreatment and enzymatic hydrolysis progress. In both stalk pith and stalk rind, particles peel into individual cells whose walls are subsequently fragmented during enzymatic hydrolysis.
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| 3. |
- Cousins, Dylan S., et al.
(författare)
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Predictive models enhance feedstock quality of corn stover via air classification
- 2022
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Ingår i: Biomass Conversion and Biorefinery. - : Springer Nature. - 2190-6815 .- 2190-6823.
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Tidskriftsartikel (refereegranskat)abstract
- Feedstock heterogeneity is a fundamental obstacle to cost-competitive biobased products. Agricultural products like corn stover have anatomical components that vary in their chemical composition, mechanical properties, structure, and response to chemical and biological treatments. A technique that can enrich streams in select anatomical fractions would allow a tailored deconstruction approach to increase overall process efficiency. Air classification can be leveraged for such refining; however, fundamental characterization and understanding of the particle properties that underly the physics of air classification are only modestly documented. Here, we determine fundamental particle properties including mass-to-area ratio, drag coefficient, and partition velocity that describe how anatomical tissues of corn stover behave during air classification. Mass-to-area ratios of anatomical tissues vary by nearly two orders of magnitude from 2.3 mg/mm2 for cob to 0.04 mg/mm2 for leaf. Drag coefficients of longer, fibrous materials (i.e., rind, husk, and sheath) are shown to correlate with particle area (p-value < 0.001) whereas granular tissues (i.e., cob, pith, and leaf) correlate better with mass-to-area ratio (p-values < 0.001). When compared to experimental observations, a simulated two-stage air classification and size reduction scenario predicts the overall partitioning of anatomical tissues within 15% for pith, husk, rind, and cob tissues. The model predicts an air-classified fraction preferentially enriched in cob (purity = 20%), rind (purity = 74%), and pith (purity = 4.5%) with a mass yield of 47%. Empirical relations for these properties can be used to predict the partitioning of corn stover during air classification based on anatomical type and size.
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| 4. |
- Ion, John, et al.
(författare)
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Laser surface modification of a 13.5% Cr, 0.6% C steel
- 1991
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Ingår i: Journal of Materials Science. - 0022-2461 .- 1573-4803. ; 26:1, s. 43-48
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Tidskriftsartikel (refereegranskat)abstract
- A 13.5% Cr, 0.6% C steel, with an initial microstructure of chromium carbides in a ferrite matrix, was heat-treated by scanning a high-power laser beam over the surface. The aim was to compare the physical and chemical properties produced by this type of selective surface treatment with those resulting from a conventional furnace desensitization and quench-hardening heat treatment. Surface heating homogenized the carbon originally bound in the carbides sufficiently to produce martensite, giving hardening to levels comparable with a conventional heat treatment. Chromium-rich zones, carbides and retained austenite were also detected in the heated microstructure. Surface melting produced complete homogenization of both carbon and chromium, which resulted in the retention of large amounts of austenite in the microstructure on cooling to room temperature. Subsequent refrigeration at - 196 °C transformed some of the austenite to martensite. Pitting corrosion and local reductions in hardness were observed adjacent to treated areas under certain conditions, due to precipitation of secondary carbides and elevated tempering, respectively.
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