Methods for characterizing mechanical properties of wood cell walls via nanoindentation

Nanoindentation is a method of contacting a material whose mechanical properties are unknown with another material whose properties are known. Nanoindentation has the advantage of being able to probe a material’s microstructure while being sensitive enough to detect variations in mechanical properties. However, nanoindentation has some limitations as a testing technique due to the specific formation and structure of some biomaterials. The main objective of this research is to identify any factors that influence the nanoindentation measurement of wood cell walls (a typical biomaterial). The function of the embedding media in describing the properties of wood cells is poorly understood. This research demonstrated that Spurr’s resin, when diffused into wood cell wall during the embedding process, enhanced both the Young’s modulus and hardness of the cell walls. A substitute sample preparation method was developed to avoid this resin penetration into cell wall and was determined to be both effective and easy to perform. The nanoindentation procedure involves the application of a monitor and an analysis of the load-displacement behavior and the response in the material. It can be anticipated that various ways of loading, including the maximum force, the loading time, and others, will cause a variety of mechanical properties. Thus, our second aim was to study the effect of load function on nanoindentation measurement in wood. It was discovered that a fast loading rate contributed to greater contact depth and lower hardness. Increasing the holding time decreased measured values for both Young’s modulus and hardness. However, no significant difference of Young’s modulus and hardness among three loading functions with different unloading rates. The final part of the research was to study the effect of moisture content on the micromechanical properties of wood material. Several nanoindentations were performed on the wood cell wall while varying the moisture content of wood. Results indicated that both the Young’s modulus and hardness decreased significantly with an increase of moisture content. A rheology model was developed to describe the nanoindentation behaviors of wood cell walls at different moisture contents. Five parameters were extracted from Burger’s model, and the relationships among those five parameters were quantified

NANOSCALED CELLULOSE AND ITS CARBONACEOUS MATERIAL: APPLICATION AND LOCAL STRUCTURE INVESTIGATION

In this dissertation, cellulose nanocrystals three-dimensional morphology, size distribution, and the crystal structure were statistically and quantitatively investigated. Lognormal distribution was identified as the most likely for cellulose nanocrystals’ size distribution. Height and width dimensions were shown to decrease toward the ends from the midpoint of individual CNCs, implying a spindle-like shape. XRD analysis of crystallite size accompanied with TEM and AFM measurements revealed that the cross-sectional dimensions of individual switchgrass CNC were either rectangular or elliptical shape, with an approximately 3~5 nm [nanometer] lateral element length range. A sponge-like carbon aerogel from microfibril cellulose with high porosity, ultra-low density, hydrophobic properties, and reusability was synthesized. Carbon aerogels heat-treated at 700 and 900 oC [Celsius] were examined and compared. Sample C-700 (521 m2 /g [square meter/gram]) exhibits significantly higher BET surface area than Sample C-950 (149 m2 /g [square meter/gram]). It also achieved highest normalized sorption capacity (86 g/g [gram/gram]) for paraffin oil. The removal of hydrophilic function groups of carbon aerogel proved by FTIR results to its highly hydrophobic properties. The Oil absorption ability is favored by its highly porous 3D network structure with interconnected cellulose nanofibrils. Simultaneous effects of processing parameters (peak temperature, heating rate) for carbon aerogel processing were investigated using response surface methodology (RSM). Results indicated that the optimum conditions were: 300 °C [Celsius] of the peak temperature and 8.0 °C/min [Celsius/minute] of the heating rate with approximately 90.1 g/g [gram/gram] of the normalized oil absorption capacity. An electron microscopy investigation was performed to link the micro-structure and properties of carbonized cellulose and lignin with the structure of original biomass components. Structure details at micro and molecular levels have been investigated by scanning transmission electron microscopy (STEM). Atomic resolution images revealed the presence of random, fractured graphene fragments in carbonized cellulose (C-CNC) and of large domains of parallel stacked graphene in carbonized lignin (C-Lignin). The randomly arranged small graphene fragments in C-CNC create a network of interconnected micropores and mesopores and contribute to the increased BET surface area. Formation of parallel stacks of graphene structures is favored by the pre-existence of aromatic components in lignin

On global ACC for foliated threefolds

In this paper, we prove the rational coefficient case of the global ACC for foliated threefolds. Specifically, we consider any lc foliated log Calabi-Yau triple $(X,\mathcal{F},B)$ of dimension $3$ whose coefficients belong to a set $\Gamma$ of rational numbers satisfying the descending chain condition, and prove that the coefficients of $B$ belong to a finite set depending only on $\Gamma$. To prove our main result, we introduce the concept of generalized foliated quadruples, which is a mixture of foliated triples and Birkar-Zhang's generalized pairs. With this concept, we establish a canonical bundle formula for foliations in any dimension. As for applications, we extend Shokurov's global index conjecture in the classical MMP to foliated triples and prove this conjecture for threefolds with nonzero boundaries and for surfaces. Additionally, we introduce the theory of rational polytopes for functional divisors on foliations and prove some miscellaneous results.Comment: 22 pages. Add a paragraph on pages 3-4. Proposition 6.4 and Lemma 7.2 strengthened. Small modification of the proof of 8.1. Reference update

Exploring accurate structure, composition and mechanical properties of η carbides in high tungsten iron-based alloy: High-throughput mapping and DFT calculations

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Hybrid Data-driven Framework for Shale Gas Production Performance Analysis via Game Theory, Machine Learning and Optimization Approaches

A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential, designing field development plan, and making investment decisions. However, quantitative analysis can be challenging because production performance is dominated by a complex interaction among a series of geological and engineering factors. In this study, we propose a hybrid data-driven procedure for analyzing shale gas production performance, which consists of a complete workflow for dominant factor analysis, production forecast, and development optimization. More specifically, game theory and machine learning models are coupled to determine the dominating geological and engineering factors. The Shapley value with definite physical meanings is employed to quantitatively measure the effects of individual factors. A multi-model-fused stacked model is trained for production forecast, on the basis of which derivative-free optimization algorithms are introduced to optimize the development plan. The complete workflow is validated with actual production data collected from the Fuling shale gas field, Sichuan Basin, China. The validation results show that the proposed procedure can draw rigorous conclusions with quantified evidence and thereby provide specific and reliable suggestions for development plan optimization. Comparing with traditional and experience-based approaches, the hybrid data-driven procedure is advanced in terms of both efficiency and accuracy.Comment: 37 pages, 15 figures, 6 table

Pursuing Counterfactual Fairness via Sequential Autoencoder Across Domains

Recognizing the prevalence of domain shift as a common challenge in machine learning, various domain generalization (DG) techniques have been developed to enhance the performance of machine learning systems when dealing with out-of-distribution (OOD) data. Furthermore, in real-world scenarios, data distributions can gradually change across a sequence of sequential domains. While current methodologies primarily focus on improving model effectiveness within these new domains, they often overlook fairness issues throughout the learning process. In response, we introduce an innovative framework called Counterfactual Fairness-Aware Domain Generalization with Sequential Autoencoder (CDSAE). This approach effectively separates environmental information and sensitive attributes from the embedded representation of classification features. This concurrent separation not only greatly improves model generalization across diverse and unfamiliar domains but also effectively addresses challenges related to unfair classification. Our strategy is rooted in the principles of causal inference to tackle these dual issues. To examine the intricate relationship between semantic information, sensitive attributes, and environmental cues, we systematically categorize exogenous uncertainty factors into four latent variables: 1) semantic information influenced by sensitive attributes, 2) semantic information unaffected by sensitive attributes, 3) environmental cues influenced by sensitive attributes, and 4) environmental cues unaffected by sensitive attributes. By incorporating fairness regularization, we exclusively employ semantic information for classification purposes. Empirical validation on synthetic and real-world datasets substantiates the effectiveness of our approach, demonstrating improved accuracy levels while ensuring the preservation of fairness in the evolving landscape of continuous domains

Asymptomatic traumatic neuroma after neck dissection in a patient with thyroid cancer

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THE INFLUENCE OF NANOCELLULOSE AND SILICON DIOXIDE ON THE MECHANICAL PROPERTIES OF THE CELL WALL WITH RELATION TO THE BOND INTERFACE BETWEEN WOOD AND UREA-FORMALDEHYDE RESIN

Urea-formaldehyde (UF) resin is used as an adhesive in the most wood-based composite plants in China. The quality of such composites is strongly affected by the mechanical properties of the cell wall in relation to the interface between UF resin and wood. This research investigates the mechanical properties of the cell wall in the bond interface of wood and UF resin with nanocellulose and silicon dioxide, and compares the mechanical properties of wood-adhesive interface cell walls to their gluing strength. The hardness and reduced modulus of the cell wall were investigated by means of nanoindentation. The test results show that there was a close relationship between the mechanical properties of the cell walls at the wood-adhesive interface and the percentage of nanocellulose or SiO2 in the UF. The shear strength of UF resin with nanofibrillated cellulose (NFC) or nano-SiO2 in bonded wood also gradually increased when the content of these two kinds of nanomaterials was increased from 0% to 2%

The prevalence of adverse reactions among individuals with three-dose COVID-19 vaccination

Background: Considering the adverse reactions to vaccination against coronavirus disease 2019 (COVID-19), some people, particularly the elderly and those with underlying medical conditions, are hesitant to be vaccinated. This study aimed to explore the prevalence of adverse reactions and provide direct evidence of vaccine safety, mainly for the elderly and people with underlying medical conditions, to receive COVID-19 vaccination. Methods: From 1st March to 30th April 2022, we conducted an online survey of people who had completed three doses of COVID-19 vaccination by convenience sampling. Adverse reaction rates and 95% confidence intervals were calculated. In addition, conditional logistic regression was used to compare the differences in adverse reactions among the elderly and those with underlying medical conditions with the general population. Results: A total of 3339 individuals were included in this study, of which 2335 (69.9%) were female, with an average age of 32.1 ± 11.4 years. The prevalence of adverse reactions after the first dose of inactivated vaccine was 24.6 % (23.1 – 26.2 %), 19.2 % (17.8 – 20.7 %) for the second dose, and 19.1 % (17.7 – 20.6 %) for the booster dose; among individuals using messenger RNA vaccines, the prevalence was 42.7 % (32.3 – 53.6 %) for the first dose, 47.2 % (36.5 – 58.1 %) for the second dose, and 46.1 % (35.4 – 57.0 %) for the booster dose. Compared with the general population, the prevalence of adverse events did not differ in individuals with underlying medical conditions and those aged 60 and above. Conclusions: For individuals with underlying medical conditions and those aged 60 and above, the prevalence of adverse reactions is similar to that of the general population, which provides a scientific basis regarding vaccination safety for these populations