Digital Commons Network^™

Biochar For Soil Amendment: Applications, Benefits, And Environmental Impacts, Ujjwal Pokharel, Gururaj Neelgund, Ram L. Ray, Venkatesh Balan, Sandeep Kumar

Civil & Environmental Engineering Faculty Publications

The excessive use of chemical fertilizers results in environmental issues, including loss of soil fertility, eutrophication, increased soil acidity, alterations in soil characteristics, and disrupted plant–microbe symbiosis. Here, we synthesize recent studies available from up to 2025, focusing on engineered biochar and its application in addressing issues of soil nutrient imbalance, soil pollution from inorganic and organic pollutants, soil acidification, salinity, and greenhouse gas emissions from fields. Application of engineered biochar enhanced the removal of Cr (VI), Cd²⁺, Ni²⁺, Zn²⁺, Hg²⁺, and Eu³⁺ by 85%, 73%, 57.2%, 12.7%, 99.3%, and 99.2%, …

Environmentally Friendly Chelation For Enhanced Algal Biomass Deashing, Agyare Asante, George Daramola, Ryan W. Davis, Sandeep Kumar

Civil & Environmental Engineering Faculty Publications

High ash content in algal biomass limits its suitability for biofuel production by reducing combustion efficiency and increasing fouling. This study presents a green deashing strategy using nitrilotriacetic acid (NTA) and deionized (DI) water to purify Scenedesmus algae, which was selected for its high ash removal potential. The optimized sequential treatment (DI, NTA chelation, and DI+NTA treatment at 90–130 °C) achieved up to 83.07% ash removal, reducing ash content from 15.2% to 3.8%. Elevated temperatures enhanced the removal of calcium, magnesium, and potassium, while heavy metals like lead and copper were reduced below detection limits. CHN analysis confirmed minimal …

Advances In Bacterial Cellulose-Based Scaffolds For Tissue Engineering: Review, Rewati Raman Ujjwal, Gymama Slaughter

Center for Bioelectronics Publications

Bacterial cellulose (BC) has emerged as a highly versatile and promising biomaterial in tissue engineering, with potential applications across skin, bone, cartilage, and vascular regeneration. Its exceptional properties like high mechanical strength, superior biocompatibility, excellent moisture retention, and inherent ability to support cell adhesion and proliferation, make BC particularly effective for wound healing and skin regeneration. These attributes accelerate tissue repair and foster new tissue formation, highlighting its value in skin-related applications. Additionally, BC's capacity to support osteogenic differentiation, combined with its mechanical robustness, positions it as a strong candidate for bone tissue engineering, facilitating regeneration and repair. Recent advancements …

A Comparison Of Microcrystal Electron Diffraction And X-Ray Powder Diffraction For The Structural Analysis Of Metal-Organic Frameworks, Erik Biehler, Silvana Pagola, Daniel Stam, Johannes Merkelbach, Christian Jandl, Tarek M. Abdel-Fattah

Chemistry & Biochemistry Faculty Publications

This study successfully implemented microcrystal electron diffraction (microED) and X-ray powder diffraction (XRPD) for the crystal structure determination of a new phase, TAF-CNU-1, Ni(C₈H₄O₄)·3H₂O, solved by microED from single microcrystals in the powder and refined at the kinematic and dynamic electron diffraction theory levels. This nickel metal–organic framework (MOF), together with its cobalt and manganese analogues with formula M (C₈H₄O₄)·2H₂O with M = Mn ^II or Co^II, were synthesized in aqueous media as one-pot preparations from the corresponding hydrated metal chlorides and sodium terephthalate, as a promising `green' synthetic route to moisture-stable MOFs. The crystal structures of the …

Decoding Tattoo And Permanent Makeup Pigments: Linking Physicochemical Properties To Absorption, Distribution, Metabolism, And Elimination Profiles Using Quantitative Structure-Activity Relationship (Qsar)-Based New Approach Methodologies (Nams), Girija Bansod, Ajay Vikram Singh, Preeti Bhardwaj, Tulika Rai, Sweta Vijay Nakhale, Amruta Shelar, Rajendra Patil, Peter Laux, Andreas Luch, Christopher J. Osgood, Michael W. Stacey

Biological Sciences Faculty Publications

The safety and quality of tattoo and permanent makeup (PMU) pigments are subject to increased scrutiny due to their potential to cause adverse effects like anaphylaxis, photoallergic responses, and long-term toxicity. These undesirable reactions governed by their chemical structure possess varied physicochemical properties and absorption, distribution, metabolism, and elimination (ADME) characteristics. These properties control the pigment behavior during application, stability, and interaction with human tissue. The correlation between these physicochemical characteristics and ADME parameters of tattoo/PMU pigments remain under-explored despite the current advances in toxicology. Our study aims to address and bridge the gap by leveraging open-access QSAR computational toxicology …

The Pluralistic Natural Capital Values Of A Tropical City, Adrienne Grêt-Regamey, Justine Saunders, Peter Edwards, Daniel Richards, Jahson I. Alemu, Natasha Bhatia, Roman Carrasco, Zuzana Drillet, Tze Kwan Fung, Yan Feng Leon Gaw, Wanggi Jaung, Andrea Law, Rachel Ai Ting Leong, Aikeen Youu Ming Lim, Mahyar Masoudi, Yudhishthra Nathan, Rachel Rui Ying Oh, Wen Ting Ooi, Fairul Edros Ahmad Shaikh Shaikh, Xiao Ping Song, Claudia L.Y. Tan, Puay Yok Tan, Sergio Wicki, Lynn-Wei Wong, Yanyun Yan, Erik Yando, Alex Thiam Koon Yee, Jingyuan Zhang, Daniel A. Friess

Biological Sciences Faculty Publications

Nature in cities is essential for human well-being. Quantifying and valuing the goods and services provided by nature to city dwellers is missing in tropical contexts. Yet, as cities worldwide face similar challenges, understanding the services provided by tropical urban ecosystems becomes imperative for effective management. Here, we present the first Natural Capital Assessment of a tropical city, unveiling three critical insights. Firstly, we demonstrate the vital reliance of a developed tropical city on nature, particularly for climate change mitigation through regulating services. Secondly, we identify intact natural areas as Singapore’s most valuable assets, stressing the significance of the quality …

Emerging Nanotechnology Approaches For Blood Disorders: A Comprehensive Review Of Nano-Hematinics And Plant-Based Nanomedicines, Hassan Abdulsalam Adewuyi, Khairat Shamsudeen, Fatima Mahmoud Muhammad, Adeola Kolawole Victor, Sakariyau Adio Waheed, Adepeju Matilda Adekoya, Kashim Ibrahim Muhammad, Blessing Temitayo Longe, Abdulgaffar Abdulquddus

Chemistry & Biochemistry Faculty Publications

Background: Disorders affecting blood components, such as anemia, coagulopathies, and hematologic malignancies, continue to pose significant global health burdens. Traditional therapies often fall short due to adverse effects, limited bioavailability, and insufficient targeting of disease sites.

Objective: This review synthesizes current advances in nano-hematinics and plant-based nanomedicines (phyto-nanomedicines) as innovative strategies for managing blood-related conditions. These nanoscale interventions are designed to enhance therapeutic precision, bioactivity, and safety.

Methods: A systematic review was conducted using major scientific databases including PubMed, Scopus, Web of Science, and ScienceDirect. Search terms were carefully selected to retrieve literature published between 2015 and 2024 that discussed …

Robust Quantification Of Cortical Hemodynamic Response To Tactile Stimulation: A Comprehensive Fnirs Methodology To Mitigate Physiological Confounds, Mohsen Hozan

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation establishes a quantitative framework for studying somatosensory processing using functional Near-Infrared Spectroscopy (fNIRS), with a focus on applications in neurorehabilitation. The central aim is to investigate how the brain's hemodynamic response is modulated by the velocity of patterned tactile stimulation. While fNIRS is a promising neuroimaging tool, its data quality is often compromised by physiological noise and motion artifacts, challenging the reliability of its findings and hindering the development of effective, quantifiable neurotherapeutics.

Two preliminary investigations informed the final experimental design. The first pilot study confirmed that pneumotactile stimulation of the hand, during both passive (somatosensory) and active …

Same Dynamics, Different Graph: Exploring Correlations, Similarities, And Renormalization Of Dynamics On Networks Using Temporal Distance Theory, Matthew Hockenbrock

Master’s Theses

Analyzing dynamics that take place on graphs (networks) is fundamental to modern network theory, with applications spanning biology, social networks, and engineering systems. A significant body of research exists that considers the similarity and scaling of network topologies. However, understanding how dynamic behavior varies across different graph topologies - particularly how signals propagate and dynamic behaviors scale between small and large networks - remains a significant challenge.

To address this challenge, this thesis presents a comprehensive framework to analyze how dynamic signals propagate and scale on network topologies using temporal distance theory. Three research goals were accomplished. First, a …

Improvement Of Cellular Pattern Organization And Clarity Through Centrifugal Force, Lauren E. Mehanna, James D. Boyd, Shelley Remus-Williams, Nicole M. Racca, Dawson P. Spraggins, Martha E. Grady, Brad J. Berron

Chemical and Materials Engineering Faculty Publications

Rapid and strategic cell placement is necessary for high throughput tissue fabrication. Current adhesive cell patterning systems rely on fluidic shear flow to remove cells outside of the patterned regions, but limitations in washing complexity and uniformity prevent adhesive patterns from being widely applied. Centrifugation is commonly used to study the adhesive strength of cells to various substrates; however, the approach has not been applied to selective cell adhesion systems to create highly organized cell patterns. This study shows centrifugation as a promising method to wash cellular patterns after selective binding of cells to the surface has taken place. After …

Integrating Protein Language Model And Molecular Dynamics Simulations To Discover Antibiofouling Peptides, Ibrahim A. Imam, Shea Bailey, Duolin Wang, Shuai Zeng, Dong Xu, Qing Shao

Chemical and Materials Engineering Faculty Publications

Antibiofouling peptide materials prevent the nonspecific adsorption of proteins on devices, enabling them to perform their designed functions as desired in complex biological environments. Due to their importance, research on antibiofouling peptide materials has been one of the central subjects of interfacial engineering. However, only a few antibiofouling peptide sequences have been developed. This narrow scope of antibiofouling peptide materials limits their capacity to adapt to the broad spectrum of application scenarios. To address this issue, we searched for antibiofouling peptides in the vast sequence pool of the microbiome library using a combination of deep learning-based high-throughput search and molecular …

Editorial: A Decade Of Apasti – Advancing Regional Prosperity Through Science, Technology And Innovation, Basilios Tsikouras

ASEAN Journal on Science and Technology for Development

A decade after its establishment, APASTI has emerged as a key regional mechanism for promoting science, technology and innovation as drivers of sustainable development, resilience, and shared prosperity in Southeast Asia. This editorial highlights the contributions of APASTI to policy alignment, knowledge exchange, research visibility, and collaborative network-building across ASEAN, while also acknowledging ongoing constraints related to governance, capacity, funding disparities, and uneven regional integration. It introduces a special issue of 14 papers published in AJSTD that collectively evaluate the implementation and impact of APASTI across diverse sectors, including geohazards, energy, materials, health, infrastructure, transport, and information systems. By positioning …

The Antibiofilm Efficacy Of Copper And Zinc-Enhanced Borate Bioactive Glasses On Polymicrobial Biofilms, Sarah Fakher, David J. Westenberg

Biological Sciences Faculty Research & Creative Works

Healthcare-acquired infections (HAIs) are a significant global challenge driven by biofilm-forming pathogens. Polymicrobial biofilms, involving interactions between multiple microbial species, exacerbate treatment difficulties due to their enhanced resistance to antimicrobial therapies. Borate bioactive glasses (BBGs) are an emerging class of biomaterials that have attracted significant interest in infection control. The incorporation of copper and zinc into the BBG matrix can effectively disrupt biofilm formation and bacterial colonization. This study investigates the antibiofilm efficacy of copper and zinc-doped BBGs against polymicrobial biofilms formed by S. epidermidis, E. coli, and P. aeruginosa. Using static and dynamic biofilm models, BBGs were applied through …

Online Parameter Adaptation Of Lqr Controllers Via Rls For Prosthetic Joint Control: Experimental Validation On A Quanser Qube-Servo 2 Platform, Cynthia Lopez-Jordan

Theses and Dissertations

This thesis develops and experimentally validates an online adaptive Linear Quadratic Regulator (LQR) control method for prosthetic joint systems using Recursive Least Squares (RLS)-based real-time parameter estimation on the Quanser QUBE-Servo 2 platform. Traditional LQR controllers assume a fxed system model, which limits adaptability and results in reduced tracking accuracy, poor robustness, and loss of optimal performance when applied to dynamically changing prosthetic joints, infuenced by load variations, user gait changes, and mechanical wear. Limited experimental validation exists for combining RLS with online LQR adaptation in prosthetic-like systems.

To address these limitations, an RLS-driven Adaptive LQR framework was implemented to …

Nanotube Spectral Fingerprinting And Machine Learning For Optimized Bioimaging/Sensing And Disease Detection Applications In Als, Rodrigo Monroy Lopez

Open Access Master's Theses

Single-walled carbon nanotubes (SWCNTs) possess unique physicochemical and optical properties that make them ideal candidates for biomedical imaging, biosensing, and disease diagnostics. This thesis explores the potential of SWCNT-based spectral fingerprinting combined with ML (Machine Learning) algorithms to optimize bioimaging, disease detection and prediction, with a specific focus on differentiating between healthy and amyotrophic lateral sclerosis (ALS) lymphoblastic patient samples. By functionalizing SWCNTs with single-stranded DNA, we enhance their stability and target specificity, enabling their application in serum patient samples.

A comprehensive spectral analysis of DNA-SWCNTs was conducted using near-infrared fluorescence spectroscopy and other characterization techniques, including UV-Vis-absorption spectroscopy. The …

The Coral Carousel: A Device And Method For In-Situ Propagation Of Deep-Sea Corals, Gregory Bales

Open Access Master's Theses

The research described herein covers the development of a tool station for performing in-situ propagation of corals using a work class ROV. This includes a system for manipulating coral fragments and affixing them to a cement base plug. To validate this tool station, testing was performed both in the lab as well as at depth in the Gulf of Mexico. As restoration of shallow-water corals has grown in popularity, many techniques have been developed for propagation. However, it is difficult or inappropriate to directly apply these techniques to deep-sea corals. While some forms of diving are capable of approaching the …

Multimodal Feature Fusion And Machine Learning For Adhd Detection Using Neuroimaging Data, Isabel Pham

Master's Projects

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopment disorder that can significantly affect a person’s attention, impulse control, and executive function. Currently, the traditional diagnosis method often relies on clinical assessments and observations. However, these methods can be subjective and lead to inconsistencies in diagnosis between individuals. To address this challenge, neuroimaging and machine learning (ML) are promising tools for providing a more objective diagnosis of ADHD. The goal of this project is to apply a multimodal approach in which structural and functional features of specific regions of the brain are used to develop a more accurate and objective …

Effects Of Data Augmentation On Sponge Identification Using Computer Vision Models, George Ku

Master's Projects

Coral reefs can be primarily found in tropical and sub-tropical regions of our oceans, providing a thriving habitat for millions of species. Marine sponges, which can be frequently found in coral reefs, play a critical role that contributes to the maintenance of these ecosystems, including the recycling of nutrients through water filtration. However, rising ocean temperatures and acidification due to climate change have resulted in the bleaching and death of coral reefs worldwide. In order to preserve these reefs and the sponges that depend on them, scientists have been performing studies on their biodiversity. This includes collecting numerous images of …

Identifying Red Sponges On Arms Plates By Preprocessing Images Using Histogram Equalization, Barry Ng

Master's Projects

Sponges play a vital role in marine ecosystems, being the only organisms capable of converting dissolved organic matter (DOM) into particulate organic matter (POM). They provide nutrients for coral reefs to thrive in oligotrophic waters. Autonomous reef monitoring structures (ARMS) are used to measure the biodiversity of coral reefs by simulating the complex cavities inside reef structures. Organisms settle on them and scientists can retrieve them after a period of time for analysis. Images are taken of ARMS plates after they are retrieved. Human analysis is unsuitable for the analysis of ARMS plates due to the huge number of images. …

An Evidence-Based Approach To Predicting Pancreatic Ductal Adenocarcinoma, Surya Teja Nalluri

Master's Projects

Pancreatic ductal adenocarcinoma (PDAC) is a complex disease with hidden clinical indicators, so a reliable diagnosis of PDAC requires high precision and sophisticated analysis. Traditional probabilistic methods often rely on making unwarranted assumptions or undesirable approximations about probabilistic estimates, limiting their ability to provide the precision needed for correct diagnosis and treatment planning. In contrast, Dempster–Shafer Theory offers a formal framework for integrating uncertain and potentially conflicting evidence. This makes it well-suited for analyzing incomplete and ambiguous data typically associated with PDAC. By employing an evidential reasoning (ER) model based on Dempster-Shafer Theory, this approach systematically combines and evaluates imperfect …

Image-To-Text Transcription: Analyzing And Describing Visual Content, Zixiao Fan

Master's Projects

Image captioning, which provides a textual understanding of visual content, is the fundamental support for the advancement of Human-A.I. Interaction technology. In the hope of exploring the application of such technology, this project focuses on two specific goals. One is to directly explore the application of the image informationretrieving abilities, and the other is to dive into the specifics of the pipeline and components of image captioning models. As a result, this project presents a working app that exploits the text retrieval functionalities to enable image storage with functions like tagging and transcription. It also supports search functionality with a …

Galora: A Lightweight Graph-Aware Llm Framework For Node Classification On Text-Attributed Graphs, Mayur Choudhary

Master's Projects

With the exponential rise of language models (LMs) and their potential to understand semantic relationships, large LMs are being used across a wide range of applications. Text-attributed graphs (TAGs) are one notable example where LLMs can be combined with Graph Neural Networks (GNNs) to enhance node classification results. TAGs associate textual content with each node and are commonly seen in various domains such as social networks, citation graphs, recommendation systems, etc. Effectively modeling TAGs would enable deeper insights into different aspects of the graph and improve decision-making in relevant domains. We present GaLoRA, a parameter-efficient framework to integrate structural information …

Extraction Of A Knowledge Graph Of Biomedical Relationships, Brian Tran

Master's Projects

Rapid release in biomedical literature poses a challenge in linking information. This thesis aims to extract data from expanding datasets to identify and form meaningful relationships between biomedical entities. Large language models (LLMs) enable us to learn at a rapid pace. Creation of LLms from scratch are impractical. This thesis aims to collect a small dataset, containing biomedical papers, and use it to train large language models (LLMs) to extract entities from the text and learn the relationships between these entities. The experiment will be divided into two stages and utilize EU-ADR and ChemProt dataset. Starting with named entity recognition …

Performance Comparison Of Machine Learning Across Metal, Cuda, And Neuromorphic Frameworks, Ryan Saini

Master's Projects

Machine learning’s computational demands necessitate optimal performance and utilization. This research compares Apple Silicon M3 Pro with MPS, NVIDIA RTX 3070 GPU with CUDA, and neuromorphic computing for machine learning methods. We provide a cross-platform and cross-architecture performance analysis of machine learning methods to identify optimal configurations for training and inference scenarios. On traditional neural networks, Apple Silicon with MPS delivers superior energy efficiency at the cost of longer processing times for training and inference. NVIDIA with CUDA offers faster computation in training and inference at higher energy costs. Convolutional spiking neural networks perform competitively on event-based data, particularly on …

Malware Opcode Embedding And Quality Assessment Of Generative Sample Embeddings, Atishay Jain

Master's Projects

Malware is software used to damage and disrupt computer systems with the intent to cause damage to the victim. Malware detection and classification into malware families is a crucial problem for cybersecurity researchers. One of the major bottlenecks in improving these systems is the shortage of good quality labeled malware data, especially for malware families with scarce samples. Researchers have utilized generative models to generate malware data to address this issue. Malware embeddings encode patterns within a malware file, which can be used to detect and classify malware. Recently, encouraging results have been obtained in generating malware embeddings using generative …

Clustering Organ Cell Types, Venkata Satya Swathi Mattaparthi

Master's Projects

The Human Cell Atlas (HCA) created a reference map of all human cells. My project uses the Tabula Sapiens dataset, developed under HCA and based on single-cell RNA sequencing data, to explore cell type and tissue diversity. I performed experiments using the Elbow method and a formula based on dataset observations to determine the number of clusters, then applied k-means clustering on two representative subsets of the All Cells dataset. Clusters were selected for analysis using Shannon’s Diversity Index and Pielou’s Evenness. A novel algorithm based on the cell differentiation tree was used to validate the biological coherence of the …

Framework For Identity Privacy Through Gender Based Skeletonization, Harrison Hwang

Master's Projects

The protection of one’s privacy and sensitive information is becoming increasingly difficult in the modern age full of surveillance and data collection. Through the use of image based object detection machine learning models trained for human and facial recognition, people can be identified and tracked to a terrifyingly accurate degree. On the other hand, the information present in surveillance media can play a key role in security and law enforcement. This presents a problem of how to preserve key information without compromising the privacy of any individuals present in the video. In this research project, Computer Vision techniques and a …

Large Language Models For Bacterial Genomic Analysis, Manvendra Chavan

Master's Projects

Identification of bacterial gene sequences with agricultural applications has the potential to transform agricultural biotechnology. These genes can be used in environmentally friendly pest control strategies. One such use case is identifying genes with potential insecticidal properties. With an increasing number of genomic information and decreasing numbers of available annotated sequences, finding new insecticidal genes has become more challenging.The traditional methods relying on sequence alignment and annotated databases are not effective in detecting functionally relevant genes lacking close homology to known cases. This project investigates the data-driven classification of genes by sequence modeling. This research is focused on learning DNA …

Advanced Knowledge Extraction With Biomedical Data Using Llms, Akshat Krishna

Master's Projects

The rapid growth of biomedical research has led to an overwhelming volume of unstructured textual data in the scientific literature. This has necessitated the development of an automated approach for knowledge extraction and integration. In

this project, we present a comprehensive pipeline for constructing a unified biomed- ical knowledge graph by combining two well-known datasets: CHEMPROT [1],

which captures chemical–protein interactions, and EU-ADR [2], which annotates drug–gene–disease relationships. In order to identify important biomedical entities and interactions from CHEMPROT dataset, we perform Named Entity Recognition (NER) and relation Extraction (RE) using state-of-the-art biomedical models like BioBERT [3], BioGPT [4] and …

Investigation Of Dynamic Adsorption And Desorption Of Polymer Nanogel In Porous Media Through Microfluidics, Junchen Liu, Fuqiao Bai, Abdulaziz A. Almakimi, Mingzhen Wei, Xiaoming He, Ibnelwaleed A. Hussein, Baojun Bai

Mathematics and Statistics Faculty Research & Creative Works

Understanding the transport and retention of elastic nanogel and microgel particles in porous media has been a significant research subject for decades, essential to the application of enhanced oil recovery (EOR). However, a lack of dynamic adsorption and desorption studies, in which the kinetics in porous media are seldom investigated, hinders the design and application of polymer nanogel in underground porous media. In this work, we visualized and quantified the transport and dynamic adsorption of polymer nanogel in 3D glass micromodels that were manufactured by packing glass beads in capillaries. Calibrating the linearity of fluorescence intensity to concentration, we calculated …