Conductive Cellulose Nanocrystals for Electrochemical Applications

Recognized as the ‘nature’s latest wonder material’, cellulose nanocrystals (CNCs), can be extracted from cellulose, the most abundant biopolymer on earth. This nanomaterial is inherently renewable, nontoxic, sustainable, biodegradable, and has recently been realized at an industrial-scale production at a competitive cost (targeted at $10/kg). Unlike typical nanomaterials, CNC possesses several attractive properties, such as its remarkable colloidal stability in water, which makes facile solution processing possible. It also has outstanding mechanical properties, high aspect ratio, high surface area, and surface chemical reactivity. The material is extremely versatile such that it can be either used as a reinforcing agent, or be chemically/physically modified to produce the desired functionalities needed for different applications. In this work, an innovative transformation of CNCs from an intrinsically insulating material into highly conductive materials was achieved. This was made possible by two different approaches: (1) introducing CNCs with another conductive polymer - polypyrrole (PPy) to form hybrid composites; and (2) carbonizing CNCs into carbon nanorods (CNRs). The prepared conductive CNC was further applied to various electrochemical applications, such as supercapacitor (SP), supported metal catalyst, metal-free catalyst, and electrochemical sensor. In the first approach, two generations of conductive CNCs were developed. Both PPy/CNCs have a core-shell structure with PPy polymerizing in situ on the surface of CNCs forming the coating. CNC served as an ideal nanotemplate to promote an ordered growth of PPy on individual CNC rather than in the bulk solution. CNC also provided significantly improved dispersion stability in water for PPy/CNC composites compared to PPy polymerized under similar condition in the absence of CNCs. Gen 1 PPy/CNC synthesis was conducted by first performing simple chemistry to introduce more negatively charged functional group (i.e. carboxylic groups) to the surface of CNCs. These carboxylic groups provided stronger interaction with PPy layer through hydrogen bonding and dopant effect. In Gen 2, surface property of CNCs was tuned by coating a thin layer of an amphiphilic polymer before PPy polymerization. This coating provided a more favorable substrate for PPy growth compared with Gen 1. Improved synthesis of PPy/CNCs also exhibited an enhanced supercapacitive behavior and more robust cycling stability. In the second approach, conductive CNCs were fabricated through a calcination process up to 1000 oC, where organic polymers of CNCs were decomposed into carbonaceous materials. In this strategy, CNCs were used as carbon precursor and non-sacrificing template for controlling the carbonized nanostructure. To further improve the electrochemical properties of the synthesized carbon nanorods (CNRs), nitrogen (N)-doping was achieved using melamine-formaldehyde resin (MF) as N precursor that was pre-coated on individual CNC nanorods. The highly-crosslinked porous network of MF resin not only introduced the desired N-doping to the carbon framework, but also generated ordered mesoporous structure during pyrolysis. Moreover, it served as a pivotal role in protecting and stabilizing the structural integrity of the rod-shaped CNCs and preserved the fibrous morphology at high enough temperatures for graphitization. The high surface area, abundant mesopores, and N-functionality make N-doped CNRs (NCNRs) promising candidate as electrode material for supercapacitors and metal-free catalyst. The conductive NCNRs were further used as conductive carbon substrates for synthesizing supported metal nanoparticle (MNP) catalyst. However, carbon materials are generally unstable in water due to structural hydrophobicity and they also lack surface functionalities for effective stabilization of MNPs. Therefore, a bio-inspired polydopamine (PDa) polymerization on NCNR surface was performed to prepare NCNRs prior to the metal reduction step. PDa modification introduced strong chelation with metal/metal ions and dramatically increased the dispersibility of the supported metal composites, leading to improved reactant accessibility, higher catalytic activity and utilization efficiency of the metal catalysts. Both Pd and Pt nanoparticles as model metal nanoparticles of 1-2 nm were homogeneously reduced on PDa-NCNRs. The supported metal catalysts showed remarkable catalytic activity when used as catalyst for 4-nitrophenol reduction and oxygen reduction reactions. In addition, the electrode modified with prepared Pt/PDa-NCNRs displayed favorable sensing capability towards non-enzymatic glucose detection with very low overpotential in neutral media. To the best of our knowledge, this work is among the very few pioneering studies that explore the potential of CNCs as highly conductive materials/composites for various electrochemical applications. This thesis unlocks many unexplored applications of CNCs and the findings will provide many sustainable alternatives for the next generation energy storage, electrocatalysts, and electrochemical sensors. It is also expected that this work will contribute to the revival of the forestry industry in Canada by enabling high value-added wood-derived products on the market in the near future

Towards Versatile and Efficient Visual Knowledge Integration into Pre-trained Language Models with Cross-Modal Adapters

Humans learn language via multi-modal knowledge. However, due to the text-only pre-training scheme, most existing pre-trained language models (PLMs) are hindered from the multi-modal information. To inject visual knowledge into PLMs, existing methods incorporate either the text or image encoder of vision-language models (VLMs) to encode the visual information and update all the original parameters of PLMs for knowledge fusion. In this paper, we propose a new plug-and-play module, X-adapter, to flexibly leverage the aligned visual and textual knowledge learned in pre-trained VLMs and efficiently inject them into PLMs. Specifically, we insert X-adapters into PLMs, and only the added parameters are updated during adaptation. To fully exploit the potential in VLMs, X-adapters consist of two sub-modules, V-expert and T-expert, to fuse VLMs' image and text representations, respectively. We can opt for activating different sub-modules depending on the downstream tasks. Experimental results show that our method can significantly improve the performance on object-color reasoning and natural language understanding (NLU) tasks compared with PLM baselines

Siliceous foam material and its application in post-combustion carbon capture for NGCC plants: effects of aging conditions

In an effort to reduce the overall energy penalty and capital expenditure associated with carbon capture technologies, a variety of porous solid adsorbents have been developed. The limitations of solid sorbent in large-scale process are related to its CO2 uptake, physicochemical stability, lifecycle, regenerability and operation condition. In this paper, siliceous foam materials were synthesized via a modified microemulsion templating method and functionalized with branched polyethylenimine (PEI). The physical characteristics of synthesized silica adsorbents under different aging conditions were analysed via N2 sorption analysis and Scanned Electron Microscopy (SEM) morphological analysis. CO2 uptake was evaluated by thermogravimetric analyser (TGA). The results show that CO2 uptake is desirable even under low CO2 partial pressure and is predictable with multiple linear regression (MLR) model in the range of examined materials

ChatEDA: A Large Language Model Powered Autonomous Agent for EDA

The integration of a complex set of Electronic Design Automation (EDA) tools to enhance interoperability is a critical concern for circuit designers. Recent advancements in large language models (LLMs) have showcased their exceptional capabilities in natural language processing and comprehension, offering a novel approach to interfacing with EDA tools. This research paper introduces ChatEDA, an autonomous agent for EDA empowered by a large language model, AutoMage, complemented by EDA tools serving as executors. ChatEDA streamlines the design flow from the Register-Transfer Level (RTL) to the Graphic Data System Version II (GDSII) by effectively managing task planning, script generation, and task execution. Through comprehensive experimental evaluations, ChatEDA has demonstrated its proficiency in handling diverse requirements, and our fine-tuned AutoMage model has exhibited superior performance compared to GPT-4 and other similar LLMs

PDS-MAR: a fine-grained Projection-Domain Segmentation-based Metal Artifact Reduction method for intraoperative CBCT images with guidewires

Since the invention of modern CT systems, metal artifacts have been a persistent problem. Due to increased scattering, amplified noise, and insufficient data collection, it is more difficult to suppress metal artifacts in cone-beam CT, limiting its use in human- and robot-assisted spine surgeries where metallic guidewires and screws are commonly used. In this paper, we demonstrate that conventional image-domain segmentation-based MAR methods are unable to eliminate metal artifacts for intraoperative CBCT images with guidewires. To solve this problem, we present a fine-grained projection-domain segmentation-based MAR method termed PDS-MAR, in which metal traces are augmented and segmented in the projection domain before being inpainted using triangular interpolation. In addition, a metal reconstruction phase is proposed to restore metal areas in the image domain. The digital phantom study and real CBCT data study demonstrate that the proposed algorithm achieves significantly better artifact suppression than other comparing methods and has the potential to advance the use of intraoperative CBCT imaging in clinical spine surgeries.Comment: 19 Page

The COVID-19 vaccines: recent development, challenges and prospects

The highly infectious coronavirus disease 2019 (COVID-19) associated with the pathogenic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread to become a global pandemic. At present, the world is relying mainly on containment and hygiene-related measures, as well as repurposed drugs to control the outbreak. The development of COVID-19 vaccines is crucial for the world to return to pre-pandemic normalcy, and a collective global effort has been invested into protection against SARS-CoV-2. As of March 2021, thirteen vaccines have been approved for application whilst over 90 vaccine candidates are under clinical trials. This review focuses on the development of COVID-19 vaccines and highlights the efficacy and vaccination reactions of the authorised vaccines. The mechanisms, storage, and dosage specification of vaccine candidates at the advanced stage of development are also critically reviewed together with considerations for potential challenges. Whilst the development of a vaccine is, in general, in its infancy, current progress is promising. However, the world population will have to continue to adapt to the “new normal” and practice social distancing and hygienic measures, at least until effective vaccines are available to the general public

Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models

Sparse Mixture-of-Experts (MoE) is a neural architecture design that can be utilized to add learnable parameters to Large Language Models (LLMs) without increasing inference cost. Instruction tuning is a technique for training LLMs to follow instructions. We advocate combining these two approaches, as we find that MoE models benefit more from instruction tuning than dense models. In particular, we conduct empirical studies across three experimental setups: (i) Direct finetuning on individual downstream tasks devoid of instruction tuning; (ii) Instructiontuning followed by in-context few-shot or zero-shot generalization on downstream tasks; and (iii) Instruction tuning supplemented by further finetuning on individual downstream tasks. In the first scenario, MoE models overall underperform dense models of identical computational capacity. This narrative, however, dramatically changes with the introduction of instruction tuning (second and third scenario), used independently or in conjunction with task-specific finetuning. Our most powerful model, FLAN-MOE-32B, surpasses the performance of FLAN-PALM-62B on four benchmark tasks, while using only a third of the FLOPs. The advancements embodied byFLAN-MOE inspire a reevaluation of the design principles of large-scale, high-performance language models in the framework of task-agnostic learning.Comment: Preprin

Regulation of Wnt Singaling Pathway by Poly (ADP-Ribose) Glycohydrolase (PARG) Silencing Suppresses Lung Cancer in Mice Induced by Benzo(a)pyrene Inhalation Exposure

Benzo(a)pyrene (BaP) is a polycyclic aromatic hydrocarbon that specifically causes cancer and is widely distributed in the environment. Poly (ADP-ribosylation), as a key post-translational modification in BaP-induced carcinogenesis, is mainly catalyzed by poly (ADP-ribose) glycohydrolase (PARG) in eukaryotic organisms. Previously, it is found that PARG silencing can counteract BaP-induced carcinogenesis in vitro, but the mechanism remained unclear. In this study, we further examined this process in vivo by using heterozygous PARG knockout mice (PARG+/−). Wild-type and PARG+/− mice were individually treated with 0 or 10 μg/m3 BaP for 90 or 180 days by dynamic inhalation exposure. Pathological analysis of lung tissues showed that, with extended exposure time, carcinogenesis and injury in the lungs of WT mice was progressively worse; however, the injury was minimal and carcinogenesis was not detected in the lungs of PARG+/− mice. These results indicate that PARG gene silencing protects mice against lung cancer induced by BaP inhalation exposure. Furthermore, as the exposure time was extended, the protein phosphorylation level was down-regulated in WT mice, but up-regulated in PARG+/− mice. The relative expression of Wnt2b and Wnt5b mRNA in WT mice were significantly higher than those in the control group, but there was no significant difference in PARG+/− mice. Meanwhile, the relative expression of Wnt2b and Wnt5b proteins, as assessed by immunohistochemistry and Western blot analysis, was significantly up-regulated by BaP in WT mice; while in PARG+/− mice it was not statistically affected. Our work provides initial evidence that PARG silencing suppresses BaP induced lung cancer and stabilizes the expression of Wnt ligands, PARG gene and Wnt ligands may provide new options for the diagnosis and treatment of lung cancer