Application of Biomass Derived Materials in Nanocomposites and Drilling Fluids

Tar is inevitably produced from biomass thermochemical processes, and is often disposed as an industrial waste, leading to environmental pollution. As a result of its high carbon content, tar was expected to be a promising precursor for manufacturing carbon materials. Consequently, low-cost porous carbon nanofibers (CNFs) using tar, polyacrylonitrile (PAN), and silver nanoparticles was fabricated through electrospinning and subsequent stabilization and carbonization processes. The continuous electrospun nanofibers were obtained with diameters ranging from 392 to 903 nm. The addition of biomass tar resulted in increased fiber diameters, reduced thermal stabilities, and slowed cyclization reactions of PAN in the as-spun nanofibers. After stabilization and carbonization, the produced CNFs showed more uniformly sized and reduced average diameters. The CNFs exhibited high specific surface areas (\u3e400 m2/g) and microporosity. These porous features increased the exposures and contacts of silver nanoparticles to the bacteria, leading to excellent antimicrobial performances of as-spun nanofibers and CNFs. A new strategy is thus provided for utilizing tar as low-cost precursor to prepare functional CNFs and reduce environmental damage by direct disposal of tar. Additionally, nanocellulose, was used as an environmental friendly and high performance additive in drilling fluids for improving rheological and fluid filtration properties. Two types of nanocellulose, including cellulose nanocrystals (CNCs) and cellulose nanofibers (CNFs), were applied in the drilling fluids. The effects of nanocellulose dimensions and concentrations on the rheological and filtration properties of drilling fluids were investigated. With half of the bentonite (10 lb/bbl) replaced by a small fraction of nanocellulose (0.35-3.50 lb/bbl), the resultant low-solid drilling fluids showed excellent shear thinning behavior and the fluids’ viscosity, yield point, and gel strength increased with the concentrations of nanocellulose. On the other hand, the addition of nanocellulose reduced the fluid loss of the fluids under high temperature and high pressure (HTHP) conditions, demonstrating potential for HTHP well applications. Additionally, the CNCs and CNFs behaved differently in the rheological and fluid filtration properties attributed to their distinct morphologies. This study promoted the use of novel renewable biopolymer additives in drilling fluids with enhanced performance and advantages of low cost and ecologically friendly

Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators

Federated learning has become a popular method to learn from decentralized heterogeneous data. Federated semi-supervised learning (FSSL) emerges to train models from a small fraction of labeled data due to label scarcity on decentralized clients. Existing FSSL methods assume independent and identically distributed (IID) labeled data across clients and consistent class distribution between labeled and unlabeled data within a client. This work studies a more practical and challenging scenario of FSSL, where data distribution is different not only across clients but also within a client between labeled and unlabeled data. To address this challenge, we propose a novel FSSL framework with dual regulators, FedDure.} FedDure lifts the previous assumption with a coarse-grained regulator (C-reg) and a fine-grained regulator (F-reg): C-reg regularizes the updating of the local model by tracking the learning effect on labeled data distribution; F-reg learns an adaptive weighting scheme tailored for unlabeled instances in each client. We further formulate the client model training as bi-level optimization that adaptively optimizes the model in the client with two regulators. Theoretically, we show the convergence guarantee of the dual regulators. Empirically, we demonstrate that FedDure is superior to the existing methods across a wide range of settings, notably by more than 11% on CIFAR-10 and CINIC-10 datasets

An Improved ip − iq Harmonic Detection Method Based on Time-Varying Integral Duration

By analyzing harmonic related to instantaneous active current and instantaneous reactive current, we propose an improved harmonic detection method to remedy long response delay and low detection accuracy corresponding to harmonic of power system. This method introduces a series connection scheme composed of Low-Pass Filter (LPF) and a current average module firstly. Then, detection accuracy of harmonic current is enhanced by adaptive tuning cut-off frequency of LPF and integration time which was obtained by proposed algorithm of the current average module. Moreover, feedback loop is introduced to compensated delay caused by LPF. Simulations including uncontrollable rectifier and three-phase voltage type inverter show proposed method has many advantages such as high detection accuracy, low response delay and good generality. Our method provides reliable harmonic current detection for later harmonic suppression and harmonic compensation

Lignin-Modified Carbon Nanotube/Graphene Hybrid Coating as Efficient Flame Retardant

To reduce fire hazards and expand high-value applications of lignocellulosic materials, thin films comprising graphene nanoplatelets (GnPs) and multi-wall carbon nanotubes (CNTs) pre-adsorbed with alkali lignin were deposited by a Meyer rod process. Lightweight and highly flexible papers with increased gas impermeability were obtained by coating a protective layer of carbon nanomaterials in a randomly oriented and overlapped network structure. Assessment of the thermal and flammability properties of papers containing as low as 4 wt % carbon nanomaterials exhibited self-extinguishing behavior and yielded up to 83.5% and 87.7% reduction in weight loss and burning area, respectively, compared to the blank papers. The maximum burning temperature as measured by infrared pyrometry also decreased from 834 °C to 705 °C with the presence of flame retardants. Furthermore, papers coated with composites of GnPs and CNTs pre-adsorbed with lignin showed enhanced thermal stability and superior fire resistance than samples treated with either component alone. These outstanding flame-retardant properties can be attributed to the synergistic effects between GnPs, CNTs and lignin, enhancing physical barrier characteristics, formation of char and thermal management of the material. These results provide great opportunities for the development of efficient, cost-effective and environmentally sustainable flame retardants

Tumornet: Lung Nodule Characterization Using Multi-View Convolutional Neural Network With Gaussian Process

Characterization of lung nodules as benign or malignant is one of the most important tasks in lung cancer diagnosis, staging and treatment planning. While the variation in the appearance of the nodules remains large, there is a need for a fast and robust computer aided system. In this work, we propose an end-to-end trainable multi-view deep Convolutional Neural Network (CNN) for nodule characterization. First, we use median intensity projection to obtain a 2D patch corresponding to each dimension. The three images are then concatenated to form a tensor, where the images serve as different channels of the input image. In order to increase the number of training samples, we perform data augmentation by scaling, rotating and adding noise to the input image. The trained network is used to extract features from the input image followed by a Gaussian Process (GP) regression to obtain the malignancy score. We also empirically establish the significance of different high level nodule attributes such as calcification, sphericity and others for malignancy determination. These attributes are found to be complementary to the deep multi-view CNN features and a significant improvement over other methods is obtained

Probabilistic Estimation of Tropical Cyclone Intensity Based on Multi-Source Satellite Remote Sensing Images

Estimating the intensity of tropical cyclones (TCs) is beneficial for preventing and reducing the impact of natural disasters. Most existing methods for estimating TC intensity utilize single-satellite or single-band remote sensing images, but they lack the ability to quantify the uncertainty of the estimation results. However, TC, as a typical chaotic system, often requires confidence intervals for intensity estimates in real-world emergency decision-making scenarios. Additionally, the use of multi-source image inputs contributes to the uncertainty of the model. Consequently, this study introduces a neural network (MTCIE) that utilizes multi-source satellite images to provide probabilistic estimates of TC intensity. The model utilizes infrared and microwave images from multiple satellites as inputs. It uses a dual-branch self-attention encoder to extract TC image features and provides uncertainty estimates for TC intensity. Furthermore, a dataset for estimating the intensity of multi-source TC remote sensing images (MTCID) is constructed through the registration of latitude, longitude, and time, along with data augmentation. The proposed method achieves a MAE of 7.42 kt in deterministic estimation, comparable to mainstream networks like TCIENet. In uncertain estimation, it outperforms methods like MC Dropout in the PICP metric, providing reliable probability estimates. This supports TC disaster emergency decision making, enhancing risk mitigation in real-world applications

Cellulose nanoparticles as modifiers for rheology and fluid loss in bentonite water-based fluids

Rheological and filtration characteristics of drilling fluids are considered as two critical aspects to ensure the success of a drilling operation. This research demonstrates the effectiveness of cellulose nanoparticles (CNPs), including microfibrillated cellulose (MFC) and cellulose nanocrystals (CNCs) in enhancing the rheological and filtration performances of bentonite (BT) water-based drilling fluids (WDFs). CNCs were isolated from MFC through sulfuric acid hydrolysis. In comparison with MFC, the resultant CNCs had much smaller dimensions, more negative surface charge, higher stability in aqueous solutions, lower viscosity, and less evident shear thinning behavior. These differences resulted in the distinctive microstructures between MFC/BT- and CNC/BT-WDFs. A typical core-shell structure was created in CNC/BT-WDFs due to the strong surface interactions among BT layers, CNCs, and immobilized water molecules. However, a similar structure was not formed in MFC/BT-WDFs. As a result, CNC/BT-WDFs had superior rheological properties, higher temperature stability, less fluid loss volume, and thinner filter cakes than BT and MFC/BT-WDFs. Moreover, the presence of polyanionic cellulose (PAC) further improved the rheological and filtration performances of CNC/BT-WDFs, suggesting a synergistic effect between PAC and CNCs

Morphological influence of cellulose nanoparticles (CNs) from cottonseed hulls on rheological properties of polyvinyl alcohol/CN suspensions

The present work describes the isolation of cellulose nanoparticles (CNs) with different morphologies and their influence on rheological properties of CN and CN-poly (vinyl alcohol) (PVA) suspensions. Cottonseed hulls were used for the first time to extract three types of CNs, including fibrous cellulose nanofibers, rod-like cellulose nanocrystals and spherical cellulose nanoparticles through mechanical and chemical methods. Rheology results showed that the rheological behavior of the CN suspensions was strongly dependent on CN concentration and particle morphology. For PVA/CN systems, concentration of PVA/CN suspension, morphology of CNs, and weight ratio of CN to PVA were three main factors that influenced their rheology behaviors. This research reveals the importance of CN morphology and composition concentration on the rheological properties of PVA/CN, providing new insight in preparing high performance hydrogels, fibers and films base on PVA/CN suspension systems