Microwave assisted pretreatment for C4 plants in biorefinery

There is a rising global demand for energy and growing concerns about greenhouse gas emissions. Lignocellulosic biomass offers great potential for second generation bioethanol production, based on the biorefinery philosophy. It is composed of a network of interconnected polymers cellulose, hemicellulose and lignin which has evolved to develop recalcitrance against enzyme hydrolysis produced by microorganisms in nature. Therefore, pretreatment is necessary to make the biomass structure more accessible for enzyme to hydrolysis. The aim of this thesis is to demonstrate the potential of using microwave to assist thermo-chemical pretreatment for lignocellulosic biomass, namely Miscanthus, sugarcane bagasse and maize. The pretreatment process was influenced by pretreatment temperature, pretreatment media and holding time. 0.2 M -1 M H2SO4 and NaOH were used as preteatment media. Firstly, temperature optimisation was carried on Miscanthus and the results showed that 180 oC was the optimal temperature to efficiently release monosaccharides from biomass. In comparison with classic conventional heating pretreatment, microwave assisted pretreatments maximally released 12.5 times more reducing sugars during the pretreatment process. Secondly, the reducing sugar constitutions were tuned by change holding time or pretreatment media, because hemicellulose was easier to be broken down than cellulose. Xylose and glucose were selectively produced by using NaOH and H2SO4 (or FeCl3) respectively as pretreatment media. Chemical compositions and biomass morphological changes were investigated and compared. The significant removal of hemicellulose and lignin, as well as more dismantled fibre structure led to enhanced bioethanol conversion via SSF process (simultaneous saccharification fermentation). Similar study was conducted on sugarcane bagasse and maize. The performance of pretreatment media was similar. However, their optimal conditions for reducing sugar release were different, probably due to different chemical compositions percentages and biomass structure. Overall, in comparison with conventional heating pretreatment, microwave assisted pretreatment is much energy efficient and effective, showing promising potential in the biorefinery proces

Suboptimal subspace construction for log-determinant approximation

Variance reduction is a crucial idea for Monte Carlo simulation and the stochastic Lanczos quadrature method is a dedicated method to approximate the trace of a matrix function. Inspired by their advantages, we combine these two techniques to approximate the log-determinant of large-scale symmetric positive definite matrices. Key questions to be answered for such a method are how to construct or choose an appropriate projection subspace and derive guaranteed theoretical analysis. This paper applies some probabilistic approaches including the projection-cost-preserving sketch and matrix concentration inequalities to construct a suboptimal subspace. Furthermore, we provide some insights on choosing design parameters in the underlying algorithm by deriving corresponding approximation error and probabilistic error estimations. Numerical experiments demonstrate our method's effectiveness and illustrate the quality of the derived error bounds

An analysis on stochastic Lanczos quadrature with asymmetric quadrature nodes

The stochastic Lanczos quadrature method has garnered significant attention recently. Upon examination of the error analyses given by Ubaru, Chen and Saad and Cortinovis and Kressner, certain notable inconsistencies arise. It turns out that the former's results are valid for cases with symmetric quadrature nodes and may not be adequate for many practical cases such as estimating log determinant of matrices. This paper analyzes probabilistic error bound of the stochastic Lanczos quadrature method for cases with asymmetric quadrature nodes. Besides, an optimized error allocation technique is employed to minimize the overall number of matrix vector multiplications required by the stochastic Lanczos quadrature method.Comment: 20 pages, 3 figure

Efficient sugar production from sugarcane bagasse by microwave assisted acid and alkali pretreatment

Sugarcane bagasse represents one of the best potential feedstocks for the production of second generation bioethanol. The most efficient method to produce fermentable sugars is by enzymatic hydrolysis, assisted by thermochemical pretreatments. Previous research was focused on conventional heating pretreatment and the pretreated biomass residue characteristics. In this work, microwave energy is applied to facilitate sodium hydroxide (NaOH) and sulphuric acid (H2SO4) pretreatments on sugarcane bagasse and the efficiency of sugar production was evaluated on the soluble sugars released during pretreatment. The results show that microwave assisted pretreatment was more efficient than conventional heating pretreatment and it gave rise to 4 times higher reducing sugar release by using 5.7 times less pretreatment time. It is highlighted that enrichment of xylose and glucose can be tuned by changing pretreatment media (NaOH/H2SO4) and holding time. SEM study shows significant delignification effect of NaOH pretreatment, suggesting a possible improved enzymatic hydrolysis process. However, severe acid conditions should be avoided (long holding time or high acid concentration) under microwave heating conditions. It led to biomass carbonization, reducing sugar production and forming ‘humins’. Overall, in comparison with conventional pretreatment, microwave assisted pretreatment removed significant amount of hemicellulose and lignin and led to high amount of sugar production during pretreatment process, suggesting microwave heating pretreatment is an effective and efficient pretreatment method

Towards Open-Scenario Semi-supervised Medical Image Classification

Semi-supervised learning (SSL) has attracted much attention since it reduces the expensive costs of collecting adequate well-labeled training data, especially for deep learning methods. However, traditional SSL is built upon an assumption that labeled and unlabeled data should be from the same distribution e.g., classes and domains. However, in practical scenarios, unlabeled data would be from unseen classes or unseen domains, and it is still challenging to exploit them by existing SSL methods. Therefore, in this paper, we proposed a unified framework to leverage these unseen unlabeled data for open-scenario semi-supervised medical image classification. We first design a novel scoring mechanism, called dual-path outliers estimation, to identify samples from unseen classes. Meanwhile, to extract unseen-domain samples, we then apply an effective variational autoencoder (VAE) pre-training. After that, we conduct domain adaptation to fully exploit the value of the detected unseen-domain samples to boost semi-supervised training. We evaluated our proposed framework on dermatology and ophthalmology tasks. Extensive experiments demonstrate our model can achieve superior classification performance in various medical SSL scenarios

Solitary beam propagation in a nonlinear optical resonator enables high-efficiency pulse compression and mode self-cleaning

Generating intense ultrashort pulses with high-quality spatial modes is crucial for ultrafast and strong-field science. This can be accomplished by controlling propagation of femtosecond pulses under the influence of Kerr nonlinearity and achieving stable propagation with high intensity. In this work, we propose that the generation of spatial solitons in periodic layered Kerr media can provide an optimum condition for supercontinuum generation and pulse compression using multiple thin plates. With both the experimental and theoretical investigations, we successfully identify these solitary modes and reveal a universal relationship between the beam size and the critical nonlinear phase. Space-time coupling is shown to strongly influence the spectral, spatial and temporal profiles of femtosecond pulses. Taking advantage of the unique characters of these solitary modes, we demonstrate single-stage supercontinuum generation and compression of femtosecond pulses from initially 170 fs down to 22 fs with an efficiency ~90%. We also provide evidence of efficient mode self-cleaning which suggests rich spatial-temporal self-organization processes of laser beams in a nonlinear resonator

Esterification of residual palm oil using solid acid catalyst derived from rice husk

In this study, carbon-silica based acid catalysts derived from rice husks (RH) were successfully synthesised using microwave (MW) technology. The results showed that MW sulphonation produced Sulphur (S) content of 17.2–18.5 times higher than in raw RH. Fourier-transform Infrared Spectroscopy (FTIR) showed peak at 1035 cm−1 which corresponded to O˭S˭O stretching of sulphonic (-SO3H) group. XRD showed sulfonated RH catalysts (SRHCs) have amorphous structure, and through SEM, broadening of the RH voids and also formation of pores is observed. RH600 had the highest surface area of 14.52 m2/g. SRHCs showed high catalytic activity for esterification of oleic acid with methanol with RH600 had the highest initial formation rate (6.33 mmol L−1 min−1) and yield (97%). The reusability of the catalyst showed gradually dropped yield of product for every recycle, which might be due to leaching of –SO3H. Finally, esterification of oil recovered from palm oil mill effluent (POME) with methanol achieved a conversion of 87.3% free fatty acids (FFA) into fatty acid methyl esters (FAME)

Association of antioxidants use with the risk of dementia among community-dwelling adults in the United Kingdom biobank

BackgroundData regarding the association between antioxidant supplementation and incident dementia are limited.MethodsWe included 494,632 adults (54.5% females) aged 40–71 years at baseline from the United Kingdom Biobank in the final analysis. Incident dementia was ascertained using hospital inpatient and death records up to January 2021.ResultsOver a median follow-up of 11.9 years, 7,128 new cases of all-cause dementia, 2,772 cases of Alzheimer’s disease, and 1,397 cases of vascular dementia were recorded. The hazard ratio (95% CI) for incident dementia associated with zinc supplementation was 0.84 (0.74–0.96), and the association remained significant after adjusting for all confounders (0.84 (0.74–0.96)). In the full model, zinc supplementation was associated with a reduced risk of Alzheimer’s disease [HR (95% CI): 0.71 (0.57–0.88)]. There was no significant association between zinc supplementation and the risk of vascular dementia. No significant associations with incident dementia were observed for other antioxidant supplementation. The association between zinc supplementation and incident dementia was significant among individuals with [HR (95% CI): 0.34 (0.15–0.77)] and without cataract [0.87 (0.77–0.99)] but it was stronger among those with cataract (p value for interaction = 0.0271).ConclusionOur findings suggest that zinc supplementation may help reduce the risk of all-cause dementia and Alzheimer’s disease in middle-aged or older adults, especially among those with cataracts

Eightfold Fermionic Excitation in a Charge Density Wave Compound

Unconventional quasiparticle excitations in condensed matter systems have become one of the most important research frontiers. Beyond two- and fourfold degenerate Weyl and Dirac fermions, three-, six- and eightfold symmetry protected degeneracies have been predicted however remain challenging to realize in solid state materials. Here, charge density wave compound TaTe4 is proposed to hold eightfold fermionic excitation and Dirac point in energy bands. High quality TaTe4 single crystals are prepared, where the charge density wave is revealed by directly imaging the atomic structure and a pseudogap of about 45 meV on the surface. Shubnikov de-Haas oscillations of TaTe4 are consistent with band structure calculation. Scanning tunneling microscopy reveals atomic step edge states on the surface of TaTe4. This work uncovers that charge density wave is able to induce new topological phases and sheds new light on the novel excitations in condensed matter materials.Comment: Accepted by PRB: https://journals.aps.org/prb/accepted/7907cK4eW0b1ee0b93fd67c1b42942bbb08eafc3

COVID−19 hospitalization increases the risk of developing glioblastoma: a bidirectional Mendelian-randomization study

BackgroundAs a result of the COVID-19 pandemic, patients with glioblastoma (GBM) are considered a highly vulnerable population. Despite this, the extent of the causative relationship between GBM and COVID-19 infection is uncertain.MethodsGenetic instruments for SARS-CoV-2 infection (38,984 cases and 1,644,784 control individuals), COVID-19 hospitalization (8,316 cases and 1,549,095 control individuals), and COVID-19 severity (4,792 cases and 1,054,664 control individuals) were obtained from a genome-wide association study (GWAS) from European populations. A total of 6,183 GBM cases and 18,169 controls from GWAS were enrolled in our study. Their associations were evaluated by applying Mendelian randomization (MR) including IVW meta-analysis, MR-Egger regression, and weighted-median analysis. To make the conclusions more robust and reliable, sensitivity analyses were performed.ResultsOur results showed that genetically predicted COVID−19 hospitalization increases the risk of GBM (OR = 1.202, 95% CI = 1.035–1.395, p = 0.016). In addition, no increased risk of SARS-CoV-2 infection, COVID-19 hospitalization and severity were observed in patients with any type of genetically predicted GBM.ConclusionOur MR study indicated for the first time that genetically predicted COVID−19 hospitalization was demonstrated as a risk factor for the development of GBM