Digital Commons Network^™

Relationship Between Technology Student Association Participation And Soft Skills Development, Lauren Lapinski, Mickey Kosloski, Philip Reed

Educational Leadership & Workforce Development Faculty Publications

The purpose of this study was to determine the relationship between student participation in Technology Student Association activities and soft skills development. This study specifically sought information on relationships between soft skills development and (a) time spent on Technology Student Association activities, (b) competitive event success, (c) assumption of leadership roles, and (d) gender. Data were provided by Pennsylvania Technology Student Association and consisted of survey responses from middle and high school students who are active Pennsylvania Technology Student Association members (n = 229). In addition to descriptive data, a multiple linear regression analysis was performed to determine the …

A Point Of Singularity For Technology And Engineering Education, Philip A. Reed

Educational Leadership & Workforce Development Faculty Publications

[First paragraph] I attended graduate school at Virginia Tech in the late 1990's and some of my fondest memories are from the side conversations with the faculty. The faculty at that time consisted of my mentor and program leader, Jim LaPorte, and other leaders in the field: Allen Bame, Sharon Brusic, Tom Jeffries, and Mark Sanders. Bill Dugger had recently retired from the university but maintained an office in Blacksburg to work full time on the Technology for All Americans Project (TfAAP, ITEEA, 2024) and he was very gracious about hosting students at the TfAAP office …

Enhancing Community College Leadership Development Through Ai: Enriching The Andragogical Approach, James Bartlett, Michelle Bartlett

Educational Leadership & Workforce Development Faculty Publications

This proposal is for a work-in-progress that proposes a study to explore the innovative use of Artificial Intelligence (AI), specifically ChatGPT, in advancing the development of training for leadership skills within community college settings. It aims to investigate how AI can be leveraged to provide a personalized and effective learning experience, aligning with the principles of andragogy to cater to adult learners in leadership roles. The study, currently in the data collection phase, anticipates revealing significant insights into the effectiveness of AI in professional development. It seeks to highlight the innovative aspects of AI integration in educational leadership, focusing on …

National Science Foundation- Experiential Learning For Emergent And Novel Technologies (Exlent): A Response To Emtech Workforce Shortages, C. Tomovic, M. Tomovic, M. Yang, S. Bawab

Educational Leadership & Workforce Development Faculty Publications

This paper presents how critical workforce shortages in the emerging STEM fields of technology (EmTech), i.e., advanced manufacturing, artificial intelligence, biotechnology, quantum information science, semiconductors, microelectronics, alternative energy, etc., are impacting the workforce in the United States. The demands for EmTech graduates have become so great that many employers have changed their hiring criteria; over 60% of today’s high-need EmTech employers no longer require new hires to have an earned bachelor’s degree. Instead, they are asking applicants to demonstrate that they can execute professional competencies or provide validated certificates as evidence of their expertise.

In an attempt to alleviate this …

Integrative Machine Learning Approaches For Enhanced Classification Of Genomic Sequences: A Next-Generation Sequencing Perspective, Sujatha Alla, Nagesh Bheesetty, Sai Gireesh Komaragiri, Prasanthi Chidipudi, Joshit Mohanty, Sathish Kumar Chintala, Jubin Thomas, Jayapal Vummadi, Hemanth Volikatla, Navin Kamuni

Engineering Management & Systems Engineering Faculty Publications

The advent of Next-Generation Sequencing (NGS) techniques has revolutionized genomic research by enabling the rapid sequencing of DNA and RNA. This data can be used for various applications, including genome sequencing, transcriptome profiling, metagenomics, and epigenetics studies. For this study, DNA classifier dataset was extracted from UCI repository of machine learning databases. This vast amount of genomic data necessitates the development of sophisticated machine learning (ML) models for effective classification and analysis. This study presents a comprehensive comparison of various ML models, including Support Vector Machines (SVM), Random Forests (RF), and Neural Networks (NNs), approaches, in classifying genomic data. We …

Integrating Generative Artificial Intelligence With Systems Architecting Diagram Creation: Advancement, Challenges, Opportunities And Future Perspectives, Cansu Yalim, Holly H. Handley

Engineering Management & Systems Engineering Faculty Publications

Generative AI (GenAI) serves as a powerful tool that can create a wide range of content, including but not limited to text, speech, images, code, videos, and 3D models. ChatGPT stands out as a particularly appealing Generative Pretrained Transformer (GPT) model that offers supplementary capabilities through GPTs and plugins. These extensions enable users to engage with the chatbot and improve its functionality, surpassing mere content generation. Our study delves into the potential of ChatGPT, specifically GPT-4, to expedite the creation of diagrams to support the system architecting process. To this end, we explored the use of ChatGPT's Diagrams Show Me …

Data Science In Finance: Challenges And Opportunities, Xianrong Zheng, Elizabeth Gildea, Sheng Chai, Tongxiao Zhang, Shuxi Wang

Information Technology & Decision Sciences Faculty Publications

Data science has become increasingly popular due to emerging technologies, including generative AI, big data, deep learning, etc. It can provide insights from data that are hard to determine from a human perspective. Data science in finance helps to provide more personal and safer experiences for customers and develop cutting-edge solutions for a company. This paper surveys the challenges and opportunities in applying data science to finance. It provides a state-of-the-art review of financial technologies, algorithmic trading, and fraud detection. Also, the paper identifies two research topics. One is how to use generative AI in algorithmic trading. The other is …

Differences In Student-Ai Interaction Process On A Drawing Task: Focusing On Students' Attitude Towards Ai And The Level Of Drawing Skills, Jinhee Kim, Yoonhee Ham, Sang-Soog Lee

STEMPS Faculty Publications

Recent advances and applications of artificial intelligence (AI) have increased the opportunities for students to interact with AI in their learning tasks. Although various fields of scholarly research have investigated human-AI collaboration, the underlying processes of how students collaborate with AI in a student-AI teaming scenario have been scarcely investigated. To develop effective AI applications in education, it is necessary to understand differences in the student-AI interaction (SAI) process depending on students' characteristics. The present study attempts to fill this gap by exploring the differences in the SAI process amongst students with varying drawing proficiencies and attitudes towards AI in …

The Role Of Shopping Orientations And Intrinsic Experiential Value In Consumer's Willingness To Follow Embodied-Ai's Advice In Fashion Shoe Stores, Christina Soyoung Song, Ji Young Lee, Dooyoung Choi

STEMPS Faculty Publications

This study employs a synthesis of Intrinsic Motivation Theory with three shopping orientations, namely “adventure,” “idea,” and “personalized” shopping, in order to examine their potential influence on individuals' motivation towards shopping. We proposed that consumers’ experiential value of intrinsic enjoyment is an indispensable mediator that affects their willingness to follow EAI’s advice. The study offers novel insights into the way that consumers’ characteristics of influencing others’ clothing consumption affect their shopping motivations to find adventure and stimulation, keep up with new fashion trends and products information, and their preference to patronize stores and interact with store staff on a personal …

Higher Education Faculty Perceptions Of Chatgpt And The Influencing Factors: A Sentiment Analysis Of X, Yoseph Mamo, Helen Crompton, Diane Burke, Christine E. Nickel

STEMPS Faculty Publications

ChatGPT, an AI chatbot developed by OpenAI, was released in November 2022, sparking a significant surge in global awareness and utilization of generative AI across various domains. Although recent studies have acknowledged the significance of ChatGPT in the education sector, they have yet to focus on exploring faculty attitudes toward ChatGPT. We gathered a comprehensive corpus of tweets containing “#ChatGPT” and “#highered” between November 30th, 2022, and April 30th, 2023. We analyzed data by triangulating VADER, NRC lexicon, and ground coding. Findings suggest that 40% of the expressed sentiments were positive, 51% were neutral, and 9% were negative. The study …

The Manifesto For Teaching And Learning In A Time Of Generative Ai: A Critical Collective Stance To Better Navigate The Future, Aras Bozkurt, Junhong Xiao, Robert Farrow, John Y. H. Bai, Chrissi Nerantzi, Stephanie Moore, Jon Dron, Christian M. Stracke, Lenandlar Singh, Helen Crompton, Apostolos Koutropoulos, Evgenii Terentev, Angelica Pazurek, Mark Nichols, Alexander M. Sidorkin, Eamon Costello, Steven Watson, Dónal Mulligan, Sarah Honeychurch, Charles B. Hodges, Mike Sharples, Andrew Swindell, Isak Frumin, Ahmed Tlili, Patricia J. Slagter Van Tryon, Melissa Bond, Maha Bali, Jing Leng, Kai Zhang, Mutlu Cukurnova, Thomas K. F. Chiu, Kyungmee Lee, Stefan Hrastinski, Manuel B. Garcia, Ramesh Chander Sharma, Bryan Alexander, Olaf Zawacki-Richter, Henk Huijser, Petar Jandrić, Chanjin Zheng, Peter Shea, Josep M. Duart, Chryssa Themeli, Anton Vorochkov, Sunagül Sani-Bozkurt, Robert L. Moore, Tutaleni Iita Asino

STEMPS Faculty Publications

This manifesto critically examines the unfolding integration of Generative AI (GenAI), chatbots, and algorithms into higher education, using a collective and thoughtful approach to navigate the future of teaching and learning. GenAI, while celebrated for its potential to personalize learning, enhance efficiency, and expand educational accessibility, is far from a neutral tool. Algorithms now shape human interaction, communication, and content creation, raising profound questions about human agency and biases and values embedded in their designs. As GenAI continues to evolve, we face critical challenges in maintaining human oversight, safeguarding equity, and facilitating meaningful, authentic learning experiences. This manifesto emphasizes that …

Effect Of Resin Bleed Out On Compaction Behavior Of The Fiber Tow Gap Region During Automated Fiber Placement Manufacturing, Von Clyde Jamora, Virginia Rauch, Sergii G. Kravchenko, Oleksandr G. Kravchenko

Mechanical & Aerospace Engineering Faculty Publications

Automated fiber placement is a state-of-the-art manufacturing method which allows for precise control over layup design. However, AFP results in irregular morphology due to fiber tow deposition induced features such as tow gaps and overlaps. Factors such as the squeeze flow and resin bleed out, combined with large non-linear deformation, lead to morphological variability. To understand these complex interacting phenomena, a coupled multiphysics finite element framework was developed to simulate the compaction behavior around fiber tow gap regions, which consists of coupled chemo-rheological and flow-compaction analysis. The compaction analysis incorporated a visco-hyperelastic constitutive model with anisotropic tensorial prepreg viscosity, which …

A Case Of One Step Forward And Two Steps Back? An Examination Of Herbicide-Resistant Weed Management Using A Simple Agroecosystem Dynamics Model, Srinadh Kodali, Chris Flores-Lopez, Isabelle Lobdell, Branson Kim, James C. Russell, Lane Michna, Benjamin L. Turner

Mechanical & Aerospace Engineering Faculty Publications

Global herbicide-resistant weed populations continue rising due to selection pressures exerted by herbicides. Despite this, herbicides continue to be farmers’ preferred weed-control method due to cost and efficiency relative to physical or biological methods. However, weeds developing resistance to herbicides not only challenges crop production but also threatens ecosystem services by disrupting biodiversity, reducing soil health, and impacting water quality. Our objective was to develop a simulation model that captures the feedback between weed population dynamics, agricultural management, profitability, and farmer decision-making processes that interact in unique ways to reinforce herbicide resistance in weeds. After calibration to observed data and …

A Secure And Privacy-Preserving Signature Protocol Using Quantum Teleportation In Metaverse Environment, Pankaj Kumar, Vivek Bharmaik, Sunil Prajapat, Garima Thakur, Ashok Kumar Das, Sachin Shetty, Joel J. P. C. Rodrigues

VMASC Publications

The burgeoning concept of the metaverse as an interconnected virtual space represents the forefront of the next-generation internet. Quantum teleportation, known for its prowess in ensuring secure and reliable communications, stands poised to revolutionize interactions within this immersive digital realm. In this context, we propose a comprehensive interaction protocol tailored for the metaverse environment. The designed protocol entails two fundamental components: first, the interaction between a user and their avatar, facilitated by a secure seven-qubit entangled state; and second, the interaction between two avatars, enabled through an efficient two-qubit entangled state. To fortify the protocol’s resilience, quantum key distribution (QKD) …

Designing A Blockchain-Empowered Telehealth Artifact For Decentralized Identity Management And Trustworthy Communication: Interdisciplinary Approach, Xueping Liang, Nabid Alam, Tahmina Sultana, Eranga Bandara, Sachin Shetty

VMASC Publications

Background: Telehealth played a critical role during the COVID-19 pandemic and continues to function as an essential component of health care. Existing platforms cannot ensure privacy and prevent cyberattacks.

Objective: The main objectives of this study are to understand existing cybersecurity issues in identity management and trustworthy communication processes in telehealth platforms and to design a software architecture integrated with blockchain to improve security and trustworthiness with acceptable performance.

Methods: We improved personal information security in existing telehealth platforms by adopting an innovative interdisciplinary approach combining design science, social science, and computer science in the health care domain, with prototype …

A Defensive Strategy Against Android Adversarial Malware Attacks, Fabrice Setephin Atedjio, Jean-Pierre Lienou, Frederica F. Nelson, Sachin S. Shetty, Charles A. Kamhoua

VMASC Publications

Due to the popularity of Android mobile devices over the past ten years, malicious Android applications have significantly increased. Systems utilizing machine learning techniques have been successfully applied for Android malware detection to counter the constantly changing Android malware threats. However, attackers have developed new strategies to circumvent these systems by using adversarial attacks. An attacker can carefully craft a malicious sample to deceive a classifier. Among the evasion attacks, there is the more potent one, which is based on solid optimization constraints: the Carlini-Wagner attack. Carlini-Wagner is an attack that uses margin loss, which is more efficient than cross-entropy …

Efficiently Learning An Encoder That Classifies Token Replacements And Masked Permuted Network-Based Bigru Attention Classifier For Enhancing Sentiment Classification Of Scientific Text, Muhammad Inaam Ul Haq, Khalid Mahmood, Qianmu Li, Ashok Kumar Das, Sachin Shetty, Majid Hussain

VMASC Publications

The exponential growth of scientific literature in digital repositories poses challenges in interpreting complex attitudes within academic texts. Traditional sentiment analysis methods often struggle with nuanced word meanings due to contextual variations. To address this, we propose the Electra-MPNet-based BiGRU attention classifier that extracts the high-level semantic features from citation sentences using the combined strength of Electra and MPNet encoder layers. These features are then combined to extract long-range dependencies through a stacked BiGRU layer. A linear attention mechanism is imposed to estimate the attention weights and context vector which enables the model to selectively focus on relevant information. The …

Automated Classification Of Elementary Instructional Activities: Analyzing The Consistency Of Human Annotations, Jonathan K. Foster, Peter Youngs, Rachel Van Aswegen, Samarth Singh, Ginger S. Watson, Scott T. Acton

VMASC Publications

Despite a tremendous increase in the use of video for conducting research in classrooms as well as preparing and evaluating teachers, there remain notable challenges to using classroom videos at scale, including time and financial costs. Recent advances in artificial intelligence could make the process of analyzing, scoring, and cataloguing videos more efficient. These advances include natural language processing, automated speech recognition, and deep neural networks. To train artificial intelligence to accurately classify activities in classroom videos, humans must first annotate a set of videos in a consistent way. This paper describes our investigation of the degree of inter-annotator reliability …

Automatic Classification Of Activities In Classroom Videos, Jonathan K. Foster, Matthew Korban, Peter Youngs, Ginger S. Watson, Scott T. Acton

VMASC Publications

Classroom videos are a common source of data for educational researchers studying classroom interactions as well as a resource for teacher education and professional development. Over the last several decades emerging technologies have been applied to classroom videos to record, transcribe, and analyze classroom interactions. With the rise of machine learning, we report on the development and validation of neural networks to classify instructional activities using video signals, without analyzing speech or audio features, from a large corpus of nearly 250 h of classroom videos from elementary mathematics and English language arts instruction. Results indicated that the neural networks performed …

Machine Learning As A Tool For Early Detection: A Focus On Late-Stage Colorectal Cancer Across Socioeconomic Spectrums, Hadiza Galadima, Rexford Anson-Dwamena, Ashley Johnson, Ghalib Bello, Georges Adunlin, James Blando

Community & Environmental Health Faculty Publications

Purpose: To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities. Methods: An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy. Spatio-temporal analysis was used to identify disparities in late-stage CRC diagnosis probabilities. Results: Gradient boosting emerged as the superior model, with the top predictors for late-stage CRC diagnosis being anatomic site, …

A New Approach For Discriminating Spatially Acquired Sers Spectra Using Antiretroviral Drug Emtricitabine As A Test Sample, Jana Hrncirova, Marguerite R. Butler, Meredith R. Clark, Gustavo F. Doncel, John B. Cooper

Chemistry & Biochemistry Faculty Publications

In this study, an antiretroviral drug emtricitabine (also known as FTC or Emtriva) was detected and quantified down to 40 ng/ml by using surface-enhanced Raman spectroscopy (SERS). Its aqueous standards were tested with two types of silver nanoparticles (colloidal and dendritic) using a specially developed aluminum well plate. The SERS spectra were acquired using a Raman scanning device with 30-mu m spatial resolution. The spectral data were analyzed using a new approach for the discrimination of the spatially acquired spectra based on the Quality index (Qi). After the Qi is calculated, the spectral data are sorted based on the Qi. …

Type-B Energetic Processes: Introduction And Invitation To Special Issue Of Energies, James Weifu Lee, Daniel P. Sheehan

Chemistry & Biochemistry Faculty Publications

[First paragraph] Currently, there is substantial scientific evidence for the existence of Type-B energetic processes on Earth, such as the naturally occurring transmembrane-electrostatically localized protons (TELP) thermotrophic properties in life [1]. A number of groundbreaking human-made Type-B energetic processes, including (but not limited to) “asymmetric function-gated isothermal electricity production” [2], “epicatalysis generating a temperature difference between catalysis-asymmetric filaments” [3], “artificially made asymmetric membrane concentration cell” [4,5], “Fu’s experiment on heat–electric conversion using surface electron emission under a static magnetic field” [6], and the “dynamic processes in superconductors indicating Type-B energetic process” [7,8,9] have been invented and/or experimentally demonstrated. The innovative …

Trends In Atmospheric Composition Between 2004-2023 Using Version 5 Ace-Fts Data, Matthew Schmidt, Peter Bernath, Chris Boone, Michael Lecours, Johnathan Steffen

Chemistry & Biochemistry Faculty Publications

The Atmospheric Chemistry Experiment (ACE) is a satellite mission that has been in orbit since 2003. The primary instrument on ACE is a Fourier transform spectrometer (FTS) that records infrared atmospheric transmittance spectra in the limb geometry using the Sun as a light source. Version 5 of ACE-FTS data processing contains improved volume mixing ratio (VMR) profiles for 46 molecules and 24 isotopologues, including HFC-32 (CH₂F₂) and HOCl as new routine data products. VMR trends for each of the 46 molecules are reported for regions of atmospheric interest. Specifically, the longevity of the ACE mission has …

Stabilization Of Pfas-Contaminated Soil With Sewage Sludge- And Wood-Based Biochar Sorbents, Erlend Sørmo, Clara Benedikte Mader Lade, Julie Zhang, Alexandros G. Asimakopoulos, Geir Wold Åsli, Michel Hubert, Aleksandar I. Goranov, Hans Peter H. Arp, Gerard Cornelissen

Chemistry & Biochemistry Faculty Publications

Sustainable and effective remediation technologies for the treatment of soil contaminated with per- and polyfluoroalkyl substances (PFAS) are greatly needed. This study investigated the effects of waste-based biochars on the leaching of PFAS from a sandy soil with a low total organic carbon content (TOC) of 0.57 ± 0.04% impacted by PFAS from aqueous film forming foam (AFFF) dispersed at a former fire-fighting facility. Six different biochars (pyrolyzed at 700–900°C) were tested, made from clean wood chips (CWC), waste timber (WT), activated waste timber (aWT), two digested sewage sludges (DSS-1 and DSS-2) and de-watered raw sewage sludge (DWSS). Up-flow column …

Green Oxidation Of Aromatic Hydrazide Derivatives Using An Oxoammonium Salt, Nidheesh Phadnis, Jessica A. Molen, Shannon M. Stephens, Shayne M. Weierbach, Kyle M. Lambert, John A. Milligan

Chemistry & Biochemistry Faculty Publications

Aromatic diazenes are often prepared by oxidation of the corresponding hydrazides using stoichiometric quantities of nonrecyclable oxidants. We developed a convenient alternative protocol for the oxidation of aromatic hydrazides using Bobbitt's salt (1), a metal-free, recyclable, and commercially available oxoammonium reagent. A variety of aryl hydrazides were oxidized within 75 min at room temperature using the developed protocol. Computational insight suggests that this oxidation occurs by a polar hydride transfer mechanism.

Unraveling Sources Of Cyanate In The Marine Environment: Insights From Cyanate Distributions And Production During The Photochemical Degradation Of Dissolved Organic Matter, Rui Wang, Jihua Liu, Yongle Xu, Li Liu, Kenneth Mopper

Chemistry & Biochemistry Faculty Publications

Cyanate is a nitrogen and energy source for diverse marine microorganisms, playing important roles in the nitrogen cycle. Despite the extensive research on cyanate utilization, the sources of this nitrogen compound remain largely enigmatic. To unravel the sources of cyanate, distributions and production of cyanate during photochemical degradation of natural dissolved organic matter (DOM) were investigated across various environments, including freshwater, estuarine, coastal areas in Florida, and the continental and slope regions of the North American mid-Atlantic Ocean (NATL). Cyanate production was also examined during the photochemical degradation of exudates from a typical strain of Synechococcus, an important phytoplankton …

Evidence Of Direct Interaction Between Cisplatin And The Caspase-Cleaved Prostate Apoptosis Response-4 Tumor Suppressor, Krishna K. Raut, Samjhana Pandey, Gyanendra Kharel, Steven M. Pascal

Chemistry & Biochemistry Faculty Publications

Prostate apoptosis response-4 (Par-4) tumor suppressor protein has gained attention as a potential therapeutic target owing to its unique ability to selectively induce apoptosis in cancer cells, sensitize them to chemotherapy and radiotherapy, and mitigate drug resistance. It has recently been reported that Par-4 interacts synergistically with cisplatin, a widely used anticancer drug. However, the mechanistic details underlying this relationship remain elusive. In this investigation, we employed an array of biophysical techniques, including circular dichroism spectroscopy, dynamic light scattering, and UV–vis absorption spectroscopy, to characterize the interaction between the active caspase-cleaved Par-4 (cl-Par-4) fragment and cisplatin. Additionally, elemental analysis was …

Reducing The Uncertainty In Estimating Soil Microbial-Derived Carbon Storage, Han Hu, Chao Qian, Ke Xue, Rainer Georg Jörgensen, Marco Keiluweit, Chao Liang, Xuefeng Zhu, Ji Chen, Yishen Sun, Haowei Ni, Jixian Ding, Weigen Huang, Jingdong Mao, Rong-Xi Tan, Jizhong Zhou, Thomas W. Crowther, Zhi-Hua Zhou, Jiabao Zhang, Yuting Liang

Chemistry & Biochemistry Faculty Publications

Soil organic carbon (SOC) is the largest carbon pool in terrestrial ecosystems and plays a crucial role in mitigating climate change and enhancing soil productivity. Microbial-derived carbon (MDC) is the main component of the persistent SOC pool. However, current formulas used to estimate the proportional contribution of MDC are plagued by uncertainties due to limited sample sizes and the neglect of bacterial group composition effects. Here, we compiled the comprehensive global dataset and employed machine learning approaches to refine our quantitative understanding of MDC contributions to total carbon storage. Our efforts resulted in a reduction in the relative standard errors …

Potentially Massive And Global Non-Pyrogenic Production Of Condensed "Black" Carbon Through Biomass Oxidation, Aleksandar I. Goranov, Hongmei Chen, Jianshu Duan, Satish C. B. Myneni, Patrick G. Hatcher

Chemistry & Biochemistry Faculty Publications

With the increased occurrences of wildfires worldwide, there has been an increase in scientific interest surrounding the chemistry of fire-derived "black" carbon (BC). Traditionally, wildfire research has assumed that condensed aromatic carbon (ConAC) is exclusively produced via combustion, and thus, ConAC is equated to BC. However, the lack of correlations between ConAC in soils or rivers and wildfire history suggests that ConAC may be produced non-pyrogenically. Here, we show quantitative evidence that this occurs during the oxidation of biomass with environmentally ubiquitous hydroxyl radicals. Pine wood boards exposed to iron nails and natural weather conditions for 12 years yielded a …

Synthetic Approaches Of Carbohydrate Based Self-Assembling Systems, Guijun Wang, Anji Chen, Pramod Aryal, Jonathan Bietsch

Chemistry & Biochemistry Faculty Publications

Carbohydrate-based self-assembling systems are essential for the formation of advanced biocompatible materials via a bottom-up approach. The self-assembling of sugar-based small molecules has applications encompassing many research fields and has been studied extensively. In this focused review, we will discuss the synthetic approaches for carbohydrate-based self-assembling (SA) systems, the mechanisms of the assembly, as well as the main properties and applications. This review will mainly cover recent publications in the last four years from January 2020 to December 2023. We will essentially focus on small molecule self-assembly, excluding polymer-based systems, which include various derivatives of monosaccharides, disaccharides, and oligosaccharides. Glycolipids, …