Microwave-assisted solid-state pretreatment for fabrication of hemp fibres using ethanolamine at low temperature.

Conventional methods faced challenges in pretreating natural cellulose fibres due to their high energy consumption and large wastewater drainage. This research devised an efficient solid-state pretreatment method for pretreating hemp fibres using ethanolamine (ETA) assisted by microwave (MW) heating. This method produced a notable removal rate of lignin (85.4 %) with the highest cellulose content (83.0 %) at a high solid content (30 %) and low temperature (70 °C). Both FT-IR and XRD analyses indicated that the pretreatment did not alter the structure of cellulose within the hemp fibres but increased crystallinity as the CrI increased from 84 % in raw hemp fibre to 89 % in pretreated fibre. As a result, it produced hemp fibres with impressive fineness (4.6 dtex) and breaking strength (3.81 cN/dtex), meeting the requirement of textile fibre. In addition, an improvement in glucose concentration (15.6 %) was observed in enzymatic hydrolysis of the MW pretreated hemp fibres compared to the fibres pretreated without MW. Furthermore, the FT-IR and NMR data confirmed that the amination of lignin occurred even at low temperature, which contributed to the high lignin removal rate. Thus, this study presents a potentially effective energy-saving, and environmentally sustainable solid-state method for pretreating hemp fibres.

A Simple Normalization Technique Using Window Statistics to Improve the Out-of-Distribution Generalization on Medical Images.

Since data scarcity and data heterogeneity are prevailing for medical images, well-trained Convolutional Neural Networks (CNNs) using previous normalization methods may perform poorly when deployed to a new site. However, a reliable model for real-world clinical applications should generalize well both on in-distribution (IND) and out-of-distribution (OOD) data (e.g., the new site data). In this study, we present a novel normalization technique called window normalization (WIN) to improve the model generalization on heterogeneous medical images, which offers a simple yet effective alternative to existing normalization methods. Specifically, WIN perturbs the normalizing statistics with the local statistics computed within a window. This feature-level augmentation technique regularizes the models well and improves their OOD generalization significantly. Leveraging its advantage, we propose a novel self-distillation method called WIN-WIN. WIN-WIN can be easily implemented with two forward passes and a consistency constraint, serving as a simple extension to existing methods. Extensive experimental results on various tasks (6 tasks) and datasets (24 datasets) demonstrate the generality and effectiveness of our methods.

In-situ electrospinning with precise deposition of antioxidant nanofiber facial mask loaded with Enteromorpha prolifera polysaccharides.

In order to fabricate a novel antioxidant nanofiber facial mask, a metal cone modified in-situ electrospinning with precise deposition was employed by utilizing Enteromorpha prolifera polysaccharides (EPPs). The metal cone could control the deposition area to achieve precise fabrication of facial mask on skin. The EPPs exhibited remarkable antioxidant ability, as evidenced by the half-maximal inhibitory concentrations (IC50) of 1.44 mg/mL and 0.74 mg/mL against DPPH and HO• free radicals, respectively. The antioxidant ability of the facial mask was improved by elevating the electrospinning voltage from 15 kV to 19 kV, due to the improved release capacity of EPPs by 7.09 %. Moreover, the facial mask demonstrated robust skin adhesion and moisture-retaining properties compared with commercial facial mask, which was benefited by the in-situ electrospinning technology. Furthermore, cytotoxicity assay, animal skin irritation test, and ocular irritation test collectively affirmed the safety of the facial mask. Thus, this research introduces a novel in situ electrospinning with precise deposition method and a natural antioxidant additive for preparing facial mask.

Sustainable Production of Lactic Acid from Cellulose Using Au/W-ZnO Catalysts.

The catalytic conversion of cellulose to lactic acid (LA) has garnered significant attention in recent years due to the potential of cellulose as a renewable and sustainable biomass feedstock. Here, a series of Au/W-ZnO catalysts were synthesized and employed to transform cellulose into LA. Through the optimization of reaction parameters and catalyst compositions, we achieved complete cellulose conversion with a selectivity of 54.6% toward LA over Au/W-ZnO at 245 °C for 4 h. This catalyst system also proved effective at converting cotton and kenaf fibers. Structural and chemical characterizations revealed that the synergistic effect of W, ZnO, and Au facilitated mesoporous architecture generation and the establishment of an adequate acidic environment. The catalytic process proceeded through the hydrolysis of cellulose to glucose, isomerization to fructose, and its subsequent conversion to LA, with glucose isomerization identified as the rate-limiting step. These findings provide valuable insights for developing high-performance catalytic systems to convert cellulose.

Artificial SiNz:H Synapse Crossbar Arrays with Gradual Conductive Pathway for High-Accuracy Neuromorphic Computing.

Inspired by its highly efficient capability to deal with big data, the brain-like computational system has attracted a great amount of attention for its ability to outperform the von Neumann computation paradigm. As the core of the neuromorphic computing chip, an artificial synapse based on the memristor, with a high accuracy in processing images, is highly desired. We report, for the first time, that artificial synapse arrays with a high accuracy in image recognition can be obtained through the fabrication of a SiNz:H memristor with a gradient Si/N ratio. The training accuracy of SiNz:H synapse arrays for image learning can reach 93.65%. The temperature-dependent I-V characteristic reveals that the gradual Si dangling bond pathway makes the main contribution towards improving the linearity of the tunable conductance. The thinner diameter and fixed disconnection point in the gradual pathway are of benefit in enhancing the accuracy of visual identification. The artificial SiNz:H synapse arrays display stable and uniform biological functions, such as the short-term biosynaptic functions, including spike-duration-dependent plasticity, spike-number-dependent plasticity, and paired-pulse facilitation, as well as the long-term ones, such as long-term potentiation, long-term depression, and spike-time-dependent plasticity. The highly efficient visual learning capability of the artificial SiNz:H synapse with a gradual conductive pathway for neuromorphic systems hold great application potential in the age of artificial intelligence (AI).

Chromosome Detection in Metaphase Cell Images Using Morphological Priors.

Reliable chromosome detection in metaphase cell (MC) images can greatly alleviate the workload of cytogeneticists for karyotype analysis and the diagnosis of chromosomal disorders. However, it is still an extremely challenging task due to the complicated characteristics of chromosomes, e.g., dense distributions, arbitrary orientations, and various morphologies. In this article, we propose a novel rotated-anchor-based detection framework, named DeepCHM, for fast and accurate chromosome detection in MC images. Our framework has three main innovations: 1) A deep saliency map representing chromosomal morphological features is learned end-to-end with semantic features. This not only enhances the feature representations for anchor classification and regression but also guides the anchor setting to significantly reduce redundant anchors. This accelerates the detection and improves the performance; 2) A hardness-aware loss weights the contribution of positive anchors, which effectively reinforces the model to identify hard chromosomes; 3) A model-driven sampling strategy addresses the anchor imbalance issue by adaptively selecting hard negative anchors for model training. In addition, a large-scale benchmark dataset with a total of 624 images and 27,763 chromosome instances was built for chromosome detection and segmentation. Extensive experimental results demonstrate that our method outperforms most state-of-the-art (SOTA) approaches and successfully handles chromosome detection, with an AP score of 93.53%.

Controlling the Carrier Injection Efficiency in 3D Nanocrystalline Silicon Floating Gate Memory by Novel Design of Control Layer.

Three-dimensional NAND flash memory with high carrier injection efficiency has been of great interest to computing in memory for its stronger capability to deal with big data than that of conventional von Neumann architecture. Here, we first report the carrier injection efficiency of 3D NAND flash memory based on a nanocrystalline silicon floating gate, which can be controlled by a novel design of the control layer. The carrier injection efficiency in nanocrystalline Si can be monitored by the capacitance-voltage (C-V) hysteresis direction of an nc-Si floating-gate MOS structure. When the control layer thickness of the nanocrystalline silicon floating gate is 25 nm, the C-V hysteresis always maintains the counterclockwise direction under different step sizes of scanning bias. In contrast, the direction of the C-V hysteresis can be changed from counterclockwise to clockwise when the thickness of the control barrier is reduced to 22 nm. The clockwise direction of the C-V curve is due to the carrier injection from the top electrode into the defect state of the SiNx control layer. Our discovery illustrates that the thicker SiNx control layer can block the transfer of carriers from the top electrode to the SiNx, thereby improving the carrier injection efficiency from the Si substrate to the nc-Si layer. The relationship between the carrier injection and the C-V hysteresis direction is further revealed by using the energy band model, thus explaining the transition mechanism of the C-V hysteresis direction. Our report is conducive to optimizing the performance of 3D NAND flash memory based on an nc-Si floating gate, which will be better used in the field of in-memory computing.

Synthesis and Characterization of Corn Stover-Based Cellulose Triacetate Catalyzed by Ionic Liquid Phosphotungstate.

Cellulose triacetate (CTA) was successfully synthesized from corn stover cellulose (CSC) in the presence of [PyPS]3PW12O40 (IL-POM). The effects of IL-POM contents, reaction temperature, and reaction time on the yield and degree of substitution of CTA were investigated. The synthesized CTA was characterized by SEM, FTIR, and TGA, and the degree of polymerization and solubility in various organic solvents were evaluated. Results showed that the optimum reaction conditions were as follows: 0.04 g of IL-POM, reaction temperature of 140 °C, and reaction time of 45 min, for 0.4 g of CSC and 9 mL of glacial acetic acid. The yield of CTA under optimum reaction conditions was as high as 79.27%, and the degree of substitution was 2.95. SEM and FTIR results showed that the cellulose acetylation occurred, and CTA was synthesized. The TGA results revealed that the decomposition temperature of CTA increased by about 30 °C when compared with that of CSC. A simple, environment-friendly, and efficient process for the preparation of CTA from CSC was constructed, which provides a new pathway for the high-value utilization of corn stover.

Improving the generalization of glaucoma detection on fundus images via feature alignment between augmented views.

Convolutional neural networks (CNNs) are commonly used in glaucoma detection. Due to the various data distribution shift, however, a well-behaved model may be plummeting in performance when deployed in a new environment. On the other hand, the most straightforward method, data collection, is costly and even unrealistic in practice. To address these challenges, we propose a new method named data augmentation-based (DA) feature alignment (DAFA) to improve the out-of-distribution (OOD) generalization with a single dataset, which is based on the principle of feature alignment to learn the invariant features and eliminate the effect of data distribution shifts. DAFA creates two views of a sample by data augmentation and performs the feature alignment between that augmented views through latent feature recalibration and semantic representation alignment. Latent feature recalibration is normalizing the middle features to the same distribution by instance normalization (IN) layers. Semantic representation alignment is conducted by minimizing the Topk NT-Xent loss and the maximum mean discrepancy (MMD), which maximize the semantic agreement across augmented views from individual and population levels. Furthermore, a benchmark is established with seven glaucoma detection datasets and a new metric named mean of clean area under curve (mcAUC) for a comprehensive evaluation of the model performance. Experimental results of five-fold cross-validation demonstrate that DAFA can consistently and significantly improve the out-of-distribution generalization (up to +16.3% mcAUC) regardless of the training data, network architectures, and augmentation policies and outperform lots of state-of-the-art methods.

C3D-UNET: A Comprehensive 3D Unet for Covid-19 Segmentation with Intact Encoding and Local Attention.

For COVID-19 prevention and treatment, it is essential to screen the pneumonia lesions in the lung region and analyze them in a qualitative and quantitative manner. Three-dimensional (3D) computed tomography (CT) volumes can provide sufficient information; however, extra boundaries of the lesions are also needed. The major challenge of automatic 3D segmentation of COVID-19 from CT volumes lies in the inadequacy of datasets and the wide variations of pneumonia lesions in their appearance, shape, and location. In this paper, we introduce a novel network called Comprehensive 3D UNet (C3D-UNet). Compared to 3D-UNet, an intact encoding (IE) strategy designed as residual dilated convolutional blocks with increased dilation rates is proposed to extract features from wider receptive fields. Moreover, a local attention (LA) mechanism is applied in skip connections for more robust and effective information fusion. We conduct five-fold cross-validation on a private dataset and independent offline evaluation on a public dataset. Experimental results demonstrate that our method outperforms other compared methods.

Promoting enzymatic saccharification of organosolv-pretreated poplar sawdust by saponin-rich tea seed waste.

Organosolv pretreatment with two ethanol concentrations (25% and 50%, v/v) was performed to improve enzymatic saccharification of poplar sawdust. It was found that lower ethanol concentration (25%, v/v) pretreatment resulted in a higher enzymatic digestibility of poplar (38.1%) due to its higher xylan removal and similar lignin removal ratios, compared to that pretreated with 50% (v/v) ethanol pretreatment (27.5%). However, the residual lignin still exhibited a strong inhibition on enzymatic hydrolysis of organosolv-pretreated poplar (OP). Bio-surfactant preparations including tea saponin (TS), TS crude extract, and tea seed waste were applied in enzymatic hydrolysis of OP, due to their potential ability of reducing enzyme non-productive binding on lignin. Their optimal loadings in enzymatic hydrolysis of OP were optimized, which indicated that adding 0.075 g/g glucan of TS improved the 72-h glucose yield of OP by 48.3%. Moreover, adding TS crude extract and tea seed waste exhibited the better performance than TS for improving enzymatic hydrolysis of OP. It was verified that the presence of protein in TS crude extract and tea seed waste accounted for the higher improvement. More importantly, the directly application of tea seed waste in enzymatic hydrolysis could achieve the similar improvement on enzymatic hydrolysis of OP, where chemosynthetic surfactant (PEG6000) was added. The residual enzyme activities in supernatant of enzymatic hydrolysis were also determined to reveal the changes on enzyme adsorption after adding surfactants. Generally, tea seed waste could be directly applied as an alternative to chemosynthetic surfactants to promote enzymatic hydrolysis of lignocelluloses.

Facilitating enzymatic digestibility of larch by in-situ lignin modification during combined acid and alkali pretreatment.

To overcome the recalcitrance of residual lignins in acid-pretreated larch (AL), a combined acid and alkali pretreatment with in-situ lignin modification was developed in this study. The results showed that introducing in-situ lignin modification with 2-naphthol to acid pretreatment (160 and 180 oC) improved the enzymatic digestibility of AL by 12.7-14.4%, through suppressing lignin repolymerization. The obviously higher improvement (57.8-88.3%) was achieved by applying alkali post-treatment (90 oC) with poly (ethylene glycol) diglycidyl ether (PEGDE) on AL, mainly due to the function of in-situ lignin modification with PEGDE for reducing enzyme non-productive binding on lignins. More importantly, the synergism of 2-naphthol and PEGDE modification facilitated the enzymatic hydrolysis of AL more significantly. Its beneficial mechanism was explored by investigating the effects of in-situ lignin modification on lignin properties, including extraction yields, functional groups, and enzyme affinity of lignins. Results will give insights into establishing an efficient pretreatment of softwood biomass.

In Situ Electrospun Zein/Thyme Essential Oil-Based Membranes as an Effective Antibacterial Wound Dressing.

Wound dressings are an important element in promoting the healing of wounds. Electrospun fibrous materials have a highly porous structure and controllable antibacterial activity and are therefore popular as potential wound dressings. However, electrospun fibrous wound dressings are usually conveniently packaged for immediate use but cannot accommodate irregularly shaped wounds, and their misuse runs the risk of causing a secondary injury to the wound. To overcome these issues, in situ electrospun zein/thyme essential oil (TEO) nanofibrous membranes are proposed as a potential type of wound dressing and applied for wound management through an in situ electrospinning process, which uses a portable electrospinning device. The as-spun zein/TEO membranes show high gas permeability up to 154 ± 20.9 m2/s and superhydrophilicity with a 0° contact angle. With the addition of TEO, good antibacterial effects are also imparted onto the membrane to prevent infection. Moreover, the in situ electrospinning can directly deposit the zein/TEO membranes onto the site of the wound to accommodate the shape of the wound with increased convenience and perceived comfort. Experiments carried out on mice suggest that the in situ electrospun zein/TEO membrane greatly promotes the wound healing process within 11 days. The study results, therefore, suggest that wound dressings in the form of in situ electrospun zein/TEO membranes can be used to facilitate wound healing.

Monitoring Chemical Changes on the Surface of Kenaf Fiber during Degumming Process Using Infrared Microspectroscopy.

Degumming is the dominant method to obtain lignocellulosic fibers in the textile industry. Traditionally, wet chemistry methods are used to monitor the evolution of major chemical components during the degumming process. However, these methods lack the ability to provide spatial information for these heterogeneous materials. In this study, besides wet chemistry and scanning electron microscopy (SEM) analysis, a Fourier-transform infrared microspectroscopy (FTIRM) method was employed to monitor the changes in spatial distribution of the main chemical components on the kenaf surface during a steam explosion followed by chemical degum process. The results showed that hemicellulose and lignin were degummed at different rates, and the mechanisms of their degumming are different. The infrared microspectral images revealed the distribution changes of chemical components on the fiber bundle surface during the process, indicating that FTIRM is an effective tool to analyze the degumming process and improve degumming methods.

Classification and Identification of Plant Fibrous Material with Different Species Using near Infrared Technique-A New Way to Approach Determining Biomass Properties Accurately within Different Species.

Plant fibrous material is a good resource in textile and other industries. Normally, several kinds of plant fibrous materials used in one process are needed to be identified and characterized in advance. It is easy to identify them when they are in raw condition. However, most of the materials are semi products which are ground, rotted or pre-hydrolyzed. To classify these samples which include different species with high accuracy is a big challenge. In this research, both qualitative and quantitative analysis methods were chosen to classify six different species of samples, including softwood, hardwood, bast, and aquatic plant. Soft Independent Modeling of Class Analogy (SIMCA) and partial least squares (PLS) were used. The algorithm to classify different species of samples using PLS was created independently in this research. Results found that the six species can be successfully classified using SIMCA and PLS methods, and these two methods show similar results. The identification rates of kenaf, ramie and pine are 100%, and the identification rates of lotus, eucalyptus and tallow are higher than 94%. It is also found that spectra loadings can help pick up best wavenumber ranges for constructing the NIR model. Inter material distance can show how close between two species. Scores graph is helpful to choose the principal components numbers during the model construction.

Multivariate Calibration and Model Integrity for Wood Chemistry Using Fourier Transform Infrared Spectroscopy.

This research addressed a rapid method to monitor hardwood chemical composition by applying Fourier transform infrared (FT-IR) spectroscopy, with particular interest in model performance for interpretation and prediction. Partial least squares (PLS) and principal components regression (PCR) were chosen as the primary models for comparison. Standard laboratory chemistry methods were employed on a mixed genus/species hardwood sample set to collect the original data. PLS was found to provide better predictive capability while PCR exhibited a more precise estimate of loading peaks and suggests that PCR is better for model interpretation of key underlying functional groups. Specifically, when PCR was utilized, an error in peak loading of ±15 cm(-1) from the true mean was quantified. Application of the first derivative appeared to assist in improving both PCR and PLS loading precision. Research results identified the wavenumbers important in the prediction of extractives, lignin, cellulose, and hemicellulose and further demonstrated the utility in FT-IR for rapid monitoring of wood chemistry.

Prediction of mixed hardwood lignin and carbohydrate content using ATR-FTIR and FT-NIR.

This study used Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and Fourier transform near-infrared (FT-NIR) spectroscopy with principal component regression (PCR) and partial least squares regression (PLS) to build hardwood prediction models. Wet chemistry analysis coupled with high performance liquid chromatography (HPLC) was employed to obtain the chemical composition of these samples. Spectra loadings were studied to identify key wavenumber in the prediction of chemical composition. NIR-PLS and FTIR-PLS performed the best for extractives, lignin and xylose, whose residual predictive deviation (RPD) values were all over 3 and indicates the potential for either instrument to provide superior prediction models with NIR performing slightly better. During testing, it was found that more accurate determination of holocellulose content was possible when HPLC was used. Independent chemometric models, for FT-NIR and ATR-FTIR, identified similar functional groups responsible for the prediction of chemical composition and suggested that coupling the two techniques could strengthen interpretation and prediction.

Near infrared spectroscopy calibration for wood chemistry: which chemometric technique is best for prediction and interpretation?

This paper addresses the precision in factor loadings during partial least squares (PLS) and principal components regression (PCR) of wood chemistry content from near infrared reflectance (NIR) spectra. The precision of the loadings is considered important because these estimates are often utilized to interpret chemometric models or selection of meaningful wavenumbers. Standard laboratory chemistry methods were employed on a mixed genus/species hardwood sample set. PLS and PCR, before and after 1st derivative pretreatment, was utilized for model building and loadings investigation. As demonstrated by others, PLS was found to provide better predictive diagnostics. However, PCR exhibited a more precise estimate of loading peaks which makes PCR better for interpretation. Application of the 1st derivative appeared to assist in improving both PCR and PLS loading precision, but due to the small sample size, the two chemometric methods could not be compared statistically. This work is important because to date most research works have committed to PLS because it yields better predictive performance. But this research suggests there is a tradeoff between better prediction and model interpretation. Future work is needed to compare PLS and PCR for a suite of spectral pretreatment techniques.

Facile synthesis of soluble graphene quantum dots and its improved property in detecting heavy metal ions.

An effective approach to produce graphene quantum dots (GQDs) has been developed, which based on the cutting of graphene oxide (GO) powder into smaller pieces and being reduced by a green approach, using sodium polystyrene sulfonate (PSS) as a dispersant and l-ascorbic acid (l-AA) as the reducing agent, which is environmentally friendly. Then the as-prepared GQDs were further used for the detection of heavy metal ions Pb(2+). This kind of GQDs has greater solubility in water and is more biocompatible than GO that has been reduced by hydrazine hydrate. The few-layers of GQDs with defects and residual OH groups were shown to be particularly well suited for the determination of metal ions in the liquid phase using an electrochemical method, in which a remarkably low detection limit of 7×10(-9)M for Pb(2+) was achieved.

Synthesis of strongly green-photoluminescent graphene quantum dots for drug carrier.

A novel approach has been developed for the preparation of strongly green-photoluminescent graphene quantum dots (GQDs-PEG) which have been surface-passivated by polyethylene glycol. The photoluminescence (PL) quantum yield of the GQDs-PEG with 400 nm excitation was about 18.8%, which was higher than other GQDs reported in the literature. More importantly, the surface-passivated PEG on GQDs can not only enhance PL intensity but also load drug by hydrogen bonding. Moreover, the high specific surface area of GQDs-PEG endowed them high loading capability (2.5 mg/mg) to carry drug. The results demonstrated that the GQDs-PEG were suitable for drug carrier and cell imaging.