Facile Low-voltage SEM Imaging of Lignocellulosic Biomass using a Low-cost Methanesulfonate Ionic Liquid
Bibliographic record
Abstract
Waste lignocellulosic biomass, often found in agricultural and municipal wastes, is a promising source of carbon due to its low cost and sustainability [1]. For this type of material, developing a high-throughput and surface-sensitive morphological characterization protocol can facilitate the understanding of the effects of their associated environment and processing conditions. Scanning electron microscopy (SEM) excels in surface imaging, but since biomass samples are organic and have poor electrical conductivity, they require specialized techniques to avoid to electron beam damage and charging. Sputter coating techniques require additional equipment and training while variable-pressure imaging degrades spatial resolution and signal-to-noise ratio. Fortunately, room-temperature ionic liquids (RTILs) with low vapor pressure and electrical conductivity have been found to dissipate excess charges and remain stable under high vacuum [2]. However, although a few RTIL solutions have been used to treat wood and other biological samples, the susceptibility of hydrolysis of some anions [3], the high cost due to customized synthesis [4], and the need for chemical purification [5] limit their usefulness for biomass analyses. Moreover, waste biomass can have significantly deformed biological features, lose their water-soluble contents, and undergo significant swelling during the immersion process. Monitoring the morphological changes of the same sample is needed to ensure the reliability of the IL treatment. In this work, an affordable and neutral RTIL, 1-ethyl-3-methylimidazolium methanesulfonate ([EMI][MeSO3]), was selected to treat dried spent black tea and mechanically ground pinecone scales. A Hitachi SU-7000 Schottky Field Emission SEM was used to capture secondary electron (SE) images of the samples at an acceleration voltage of 1 kV and probe current of 7 pA. These parameters were used to partially suppress charging and improve contrast such that low-quality SE images of the samples before IL treatment would serve as usable baselines for morphological comparisons. For IL treatment, samples were immersed in 10 vol% IL solutions for 1 to 2 hours, followed by drying on Kimwipes paper. Figure 1 shows the effect of IL treatment on spent black tea with different solvents: ethanol and deionized water. After ethanolic IL solution treatment, the overall charging was effectively reduced and topographical contrast improved, but the uneven distribution of the IL introduced non-uniform contrast as an artifact due to the rapid evaporation of the solvent. In contrast, after aqueous IL solution treatment, non-uniform contrast artifacts were avoided, but the topography contrast remained poor, possibly due to less surface IL thickness. The optimal IL solution was found at a concentration of 10 vol% in a mixture of ethanol and water at a volume ratio of 3:1. The optimal treatment time was found to be 2 h for spent black tea and 1 h for pinecone scales to ensure IL infiltration and desorption of water-soluble contents. The difference in treatment time can be attributed to the different plant structures and compositions. Figure 2 shows the comparison of spent black tea and ground pinecone before and after optimized IL treatment. For both samples, some morphological features were slightly displaced, but the overall structure remained unchanged, confirming the viability and stability of the IL. Samples before treatment had poor contrast due to residual charging, but in both cases after the optimized IL treatment, a uniformly thin IL coating on the surface of the sample was able to conform to the surface features, suppress charging, and greatly enhance the perceived three-dimensionality. This work provides a simple and low-cost IL treatment of biomass specimen preparation for SEM imaging based on [EMI][MeSO3] with solvent optimization. The combination of IL treatment and LVSEM demonstrates the benefit of enhanced surface sensitivity and topography contrast. The similar methodology can be applied to other liquid-absorbing specimens and IL formulations. The comparison method based on low-dose can be applied to many electrically non-conductive but vacuum-stable materials to expand the application of IL treatment and verify the dimensional stability of the samples [6]. (a) Stomata on spent black tea after 10 vol% ethanolic IL treatment. (b) Stomata on spent black tea after 10 vol% aqueous IL treatment. Both were cropped from the 1280*960 pixel original images captured at an acceleration voltage of 1 kV and a probe current of 7 pA, with signals collected in line integration mode using the upper secondary electron detector. (a) Stomata on spent black tea before IL (b) the same stomata after optimized IL treatment (c)Pinecone scale before IL treatment (d) the same pit on the pinecone scale after IL treatment. All images were cropped from the 1280*960-pixel original images captured at an acceleration voltage of 1 kV and a probe current of 7 pA, with signals collected in line integration mode using the upper secondary electron detector.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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".