Bimetallic Catalyst for Lignin Depolymerization

This thesis is motivated by concerns regarding the need to develop more sustainable and economic technologies to meet rising global manufacturing and energy demands. These concerns have renewed governmental, industrial, and societal determination to reduce the world’s dependence on conventional natural resources and has led to considerable research on producing fuels and chemicals from feedstocks other than petroleum. Lignocellulosic biomass represents an abundant and renewable resource that could displace petroleum feedstock producing biofuels and multiple valuable chemical products with reduced greenhouse gas emissions. Lignin is the second abundant biopolymer source in nature and is found almost everywhere. Since the 1950’s, there have been reports of lignin depolymerization research to develop valorization technologies that convert lignin in energy, fuels, and chemicals through thermal and biological approached. Most of these technologies targeting chemical production have insufficient processing and economic performance for widespread adoption, in part due to lack product selectivity that results from lignin depolymerization. Heterogeneous metal catalysis is an ideal solution for improving lignin depolymerization process performance by promoting more selective reactions under lower energy input. Among different kinds of catalytic systems, a copper-doped porous metal catalyst has been researched often due to the ability to product hydrogen via alcohol reforming and perform hydrogenolysis for lignin depolymerization at aryl-ether linkages. Process. However, the use of nickel in other catalytic systems suggest a nickel-doped catalyst might have a greater ability hydrogenolysis on aryl-ether linkages, further reducing the lignin linkage activation energy and improving product selectivity. This thesis will focus on the development of a bimetallic catalyst with copper and nickel co-doped on a hydrotalcite support, testing the hypothesis that a bimetallic catalyst containing copper and nickel will have better reforming ability than a catalyst containing only nickel and will have better hydrogenolysis of aryl-ether ability than a catalyst containing only copper. Chapter I will present a detailed overview of the background and motivation of lignin structure and conversion. Chapter II will present detailed research on the performance of copper and nickel bimetallic catalysts for the hydrogenolysis of a lignin aryl-ether model compound. Chapter III will present unfinished work and future plan about using the catalysts been made in Chapter II for real lignin test

Pre-training with Aspect-Content Text Mutual Prediction for Multi-Aspect Dense Retrieval

Grounded on pre-trained language models (PLMs), dense retrieval has been studied extensively on plain text. In contrast, there has been little research on retrieving data with multiple aspects using dense models. In the scenarios such as product search, the aspect information plays an essential role in relevance matching, e.g., category: Electronics, Computers, and Pet Supplies. A common way of leveraging aspect information for multi-aspect retrieval is to introduce an auxiliary classification objective, i.e., using item contents to predict the annotated value IDs of item aspects. However, by learning the value embeddings from scratch, this approach may not capture the various semantic similarities between the values sufficiently. To address this limitation, we leverage the aspect information as text strings rather than class IDs during pre-training so that their semantic similarities can be naturally captured in the PLMs. To facilitate effective retrieval with the aspect strings, we propose mutual prediction objectives between the text of the item aspect and content. In this way, our model makes more sufficient use of aspect information than conducting undifferentiated masked language modeling (MLM) on the concatenated text of aspects and content. Extensive experiments on two real-world datasets (product and mini-program search) show that our approach can outperform competitive baselines both treating aspect values as classes and conducting the same MLM for aspect and content strings. Code and related dataset will be available at the URL \footnote{https://github.com/sunxiaojie99/ATTEMPT}.Comment: accepted by cikm202

A Multi-Granularity-Aware Aspect Learning Model for Multi-Aspect Dense Retrieval

Dense retrieval methods have been mostly focused on unstructured text and less attention has been drawn to structured data with various aspects, e.g., products with aspects such as category and brand. Recent work has proposed two approaches to incorporate the aspect information into item representations for effective retrieval by predicting the values associated with the item aspects. Despite their efficacy, they treat the values as isolated classes (e.g., "Smart Homes", "Home, Garden & Tools", and "Beauty & Health") and ignore their fine-grained semantic relation. Furthermore, they either enforce the learning of aspects into the CLS token, which could confuse it from its designated use for representing the entire content semantics, or learn extra aspect embeddings only with the value prediction objective, which could be insufficient especially when there are no annotated values for an item aspect. Aware of these limitations, we propose a MUlti-granulaRity-aware Aspect Learning model (MURAL) for multi-aspect dense retrieval. It leverages aspect information across various granularities to capture both coarse and fine-grained semantic relations between values. Moreover, MURAL incorporates separate aspect embeddings as input to transformer encoders so that the masked language model objective can assist implicit aspect learning even without aspect-value annotations. Extensive experiments on two real-world datasets of products and mini-programs show that MURAL outperforms state-of-the-art baselines significantly.Comment: Accepted by WSDM2024, updat

Harnessing the Power of David against Goliath: Exploring Instruction Data Generation without Using Closed-Source Models

Instruction tuning is instrumental in enabling Large Language Models~(LLMs) to follow user instructions to complete various open-domain tasks. The success of instruction tuning depends on the availability of high-quality instruction data. Owing to the exorbitant cost and substandard quality of human annotation, recent works have been deeply engaged in the exploration of the utilization of powerful closed-source models to generate instruction data automatically. However, these methods carry potential risks arising from the usage requirements of powerful closed-source models, which strictly forbid the utilization of their outputs to develop machine learning models. To deal with this problem, in this work, we explore alternative approaches to generate high-quality instruction data that do not rely on closed-source models. Our exploration includes an investigation of various existing instruction generation methods, culminating in the integration of the most efficient variant with two novel strategies to enhance the quality further. Evaluation results from two benchmarks and the GPT-4 model demonstrate the effectiveness of our generated instruction data, which can outperform Alpaca, a method reliant on closed-source models. We hope that more progress can be achieved in generating high-quality instruction data without using closed-source models

Legitimacy and the Making of International Tax Law: The Challenges of Multilateralism

This article aims to analyse the multilateral action and instruments that have been and are being developed by the Organization for Economic Cooperation and Development (“OECD”) to enhance transparency and exchange of information and the Base Erosion Profit Shifting (“BEPS”) Project in light of the principle of legitimacy vis-à-vis non-OECD (developing) countries. The question addressed in this article is under what conditions can the OECD multilateral instruments and the BEPS Project be regarded as legitimate for non-OECD (developing) countries? For this purpose, the definition of Scharpf, including the distinction between input legitimacy i.e. government by the people and output legitimacy i.e. government for the people, will be taken into account. In order to answer this question, this article will provide a description of the legitimacy of international tax law making by international organizations and the role of the OECD in respect of OECD and non-OECD countries. Thereafter, the OECD multilateral instruments to enhance transparency and exchange of information and of the BEPS Project will be assessed in respect of the input and output legitimacy. The assessment of input legitimacy will take into account transparency, participation, and representation of developing (non-OECD) countries in the setting of the agenda and the drafting of the content of the OECD multilateral instruments to exchange information and the BEPS multilateral instrument. The analysis of output legitimacy will address the shared goals i.e. to tackle tax fraud, tax evasion and aggressive tax planning and the solutions presented by the G20 and OECD, adopted by OECD and non-OECD countries. The analysis of output legitimacy will also take into account the differences in objectives and resources between OECD and non-OECD (developing) countries. The first part will address the relationship between legitimacy and international tax law making. The second part will address the role of the OECD vis-à-vis developing countries and the membership of countries to the G20, OECD and the Global Transparency Forum. The third part will address the assessment of the input and output conditions for legitimacy of the OECD multilateral instruments to exchange information and the BEPS Project. Finally, a conclusion and recommendations for further research will be provided

Characterization of ordering in Fe-6.5%Si alloy using X-ray, TEM, and magnetic TGA methods

Fe-6.5wt%Si steel surpasses the current extensively used Fe-3.2wt%Si steel in lower iron loss, higher permeability, and near zero magnetostriction. As a cost effective soft magnetic material, Fe-6.5wt%Si may find applications in motors, transformers, and electronic components. However, the brittleness of the alloy poses processing challenges. The brittleness in Fe-6.5wt%Si is attributed to the formation of ordered phases. Evaluation of the amount of ordered phases is important for the research and development of Fe-6.5wt%Si. This paper aims to find effective ways to evaluate the ordering degree through a comparison of various characterization techniques. In order to tune the ordering degree, various speeds were used to prepare Fe-6.5wt%Si samples via melt spinning. The varying wheel speed changes the cooling rate, which was confirmed by thermal imaging. In addition to the widely used TEM and normal theta-2theta X-ray diffraction methods, two quantitative methods were adopted for this Fe-6.5wt%Si system to study the ordering degree. One method is based on rotating crystal XRD technique, and the other is magnetic thermal analysis technique. These two methods effectively quantified the varying degree of ordering presented in the samples and were deemed more suitable than the TEM, normal theta-2theta XRD methods for Fe-Si due to their ease of sample preparation and short turn-around time

Aerosol Microdroplets Exhibit a Stable pH Gradient

Suspended aqueous aerosol droplets (\u3c50 μm) are microreactors for many important atmospheric reactions. In droplets and other aquatic environments, pH is arguably the key parameter dictating chemical and biological processes. The nature of the droplet air/ water interface has the potential to significantly alter droplet pH relative to bulk water. Historically, it has been challenging to measure the pH of individual droplets because of their inaccessibility to conventional pH probes. In this study, we scanned droplets containing 4-mercaptobenzoic acid–functionalized gold nanoparticle pH nanoprobes by 2D and 3D laser confocal Raman microscopy. Using surface-enhanced Raman scattering, we acquired the pH distribution inside approximately 20-μm-diameter phosphate-buffered aerosol droplets and found that the pH in the core of a droplet is higher than that of bulk solution by up to 3.6 pH units. This finding suggests the accumulation of protons at the air/water interface and is consistent with recent thermodynamic model results. The existence of this pH shift was corroborated by the observation that a catalytic reaction that occurs only under basic conditions (i.e., dimerization of 4-aminothiophenol to produce dimercaptoazobenzene) occurs within the high pH core of a droplet, but not in bulk solution. Our nanoparticle probe enables pH quantification through the cross-section of an aerosol droplet, revealing a spatial gradient that has implications for acid-base–catalyzed atmospheric chemistry

Mechanistic theory predicts the effects of temperature and humidity on inactivation of SARS-CoV-2 and other enveloped viruses

Ambient temperature and humidity strongly affect inactivation rates of enveloped viruses, but a mechanistic, quantitative theory of these effects has been elusive. We measure the stability of SARS-CoV-2 on an inert surface at nine temperature and humidity conditions and develop a mechanistic model to explain and predict how temperature and humidity alter virus inactivation. We find SARS-CoV-2 survives longest at low temperatures and extreme relative humidities (RH); median estimated virus half-life is >24 hr at 10°C and 40% RH, but ∼1.5 hr at 27°C and 65% RH. Our mechanistic model uses fundamental chemistry to explain why inactivation rate increases with increased temperature and shows a U-shaped dependence on RH. The model accurately predicts existing measurements of five different human coronaviruses, suggesting that shared mechanisms may affect stability for many viruses. The results indicate scenarios of high transmission risk, point to mitigation strategies, and advance the mechanistic study of virus transmission

Peer Firms' Earnings Disclosure and Corporate Risk-taking

Besides firms’ own ones, peer firms' financial disclosures may also affect corporate decision-making. While most existing studies examine how peers’ disclosures influence firms’ investment levels, firms’ risk-taking, which closely correlates to economic stability, remains under-explored. I hypothesize that firms learn important information from their peers’ disclosures, based on which they adjust their risk-taking behaviors. I provide large sample evidence (127,896 firm-year observations) corroborating a negative correlation between peer firms' earnings performance and the focal firm's level of risk-taking. This suggests that when peers disclose outstanding earnings, firms’ fear of competition outweighs their positive expectations of the industry outlook, thus firms tend to be more risk-averse. I also find that compared with over-performing firms, under-performing ones are more risk-loving, which accords with the prospect theory. Finally, I show that under-performing firms are less responsive to peers' earnings in terms of risk propensity because they are always more risk-seeking regardless of the degree of competition