Physical Sciences and Mathematics Commons^™

Machine Learning Classification Of Prostate Cancer Genomic Sequences Using K-Mer And Sequence-Derived Features, Kuldeep Rawat, Hirendra Nath Banerjee, Jamie Noble, Saa Naudia Deloatch, Satyendra Banerjee, Sachin Shetty, Soumya Banerjee

VMASC Publications

Prostate cancer disproportionately impacts African American men, who experience significantly higher mortality rates and earlier disease onset than other populations. Current diagnostic approaches, including prostate-specific antigen testing and biopsy, lack sufficient specificity and sensitivity, underscoring the need for accurate, molecular-level classification tools. This paper presents a machine learning framework for binary classification of genomic DNA sequences as cancerous or healthy. A dataset of 1684 FASTA-formatted sequences obtained from the National Library of Medicine - GenBank was analyzed, with 1662 sequences retained after quality control filtering. Feature engineering yielded 67 attributes, including GC content, Shannon entropy, sequence length, and trinucleotide k-mer …

Adaptive Self-Attention For Enhanced Segmentation Of Adult Gliomas In Multi-Modal Mri, Evan P. Savaria, Jiangwen Sun

Computer Science Faculty Publications

Every year there are an estimated 80,000–90,000 new glioma cases, highlighting the need for reliable imaging-based decision support. Although deep learning has improved tumor sub-region segmentation, many state-of-the-art models fail to fully capture complementary information across T1, T1Gd, T2, and FLAIR MRI modalities and often operate as “black boxes,” limiting physician trust when precise delineation is critical for surgical planning, radiation targeting, and treatment monitoring. To address these limitations, we propose AIMS, an Adaptive Integrated Multi-Modal Segmentation framework that maintains modality-specific feature streams and employs adaptive self-attention within a hierarchical CNN-Transformer architecture to prioritize and fuse multi-modal MRI features. We …

A Comprehensive Survey Of Prompt Engineering Techniques In Large Language Models, Tonmoy Debnath, Md Nurul Absar Siddiky, Muhammad Enayetur Rahman, Prosenjit Das, Antu Kumar Guha, Muhammad Rezaur Rahman, H. M. Dipu Kabir

Electrical & Computer Engineering Faculty Publications

Prompt engineering has arisen as a pivotal discipline in optimizing the performance of Large Language Models (LLMs) by structuring inputs to enhance coherence, accuracy, and task alignment. This paper comprehensively surveys various prompting techniques, systematically categorizing them according to their application domains and methodological foundations. Fundamental approaches like zero-shot and few-shot prompting are examined along with advanced strategies, including chain-of-thought reasoning, retrieval-augmented generation, and self-consistency mechanisms. A rigorous qualitative analysis is conducted to evaluate each technique's strengths, limitations, and optimal use cases, offering a structured framework for selecting the most effective prompting strategies. Theoretical insights and empirical findings are consolidated …

Physics-Informed Temperature Prediction Of Lithium-Ion Batteries Using Decomposition-Enhanced Lstm And Bilstm Models, Seyed Saeed Madani, Yasmin Shabeer, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, François Allard

Electrical & Computer Engineering Faculty Publications

Accurately forecasting the operating temperature of lithium-ion batteries (LIBs) is essential for preventing thermal runaway, extending service life, and ensuring the safe operation of electric vehicles and stationary energy-storage systems. This work introduces a unified, physics-informed, and data-driven temperature-prediction framework that integrates mathematically governed preprocessing, electrothermal decomposition, and sequential deep learning architectures. The methodology systematically applies the governing relations to convert raw temperature measurements into trend, seasonal, and residual components, thereby isolating long-term thermal accumulation, reversible entropy-driven oscillations, and irreversible resistive heating. These physically interpretable signatures serve as structured inputs to machine learning and deep learning models trained on temporally …

Markov Chain Wave Generative Adversarial Network For Bee Bioacoustic Signal Synthesis, Kumudu Samarappuli, Iman Ardekani, Mahsa Mohaghegh, Abdolhossein Sarrafzadeh

Electrical & Computer Engineering Faculty Publications

This paper presents a framework for synthesizing bee bioacoustic signals associated with hive events. While existing approaches like WaveGAN have shown promise in audio generation, they often fail to preserve the subtle temporal and spectral features of bioacoustic signals critical for event-specific classification. The proposed method, MCWaveGAN, extends WaveGAN with a Markov Chain refinement stage, producing synthetic signals that more closely match the distribution of real bioacoustic data. Experimental results show that this method captures signal characteristics more effectively than WaveGAN alone. Furthermore, when integrated into a classifier, synthesized signals improved hive status prediction accuracy. These results highlight the potential …

Comparative Assessment Of Energy And Emission Costs For Geothermal Heat Pumps And Fossil-Fuel Heating Systems Across U.S. Climatic Zones, Md Shahin Alam, Shima Afshar, Seyed Ali Arefifar, Mohammad Haq

Electrical & Computer Engineering Faculty Publications

In response to growing concerns over global warming and energy sustainability, transitioning from fossil-fuel-based heating systems to renewable alternatives is essential. This study evaluates the economic and environmental performance of geothermal heat pumps for building heating and compares it with conventional coal-fired boilers, natural-gas boilers, and diesel furnaces. Using the heating degree-day (HDD) method, heating energy demand was analyzed for four U.S. cities—Anchorage (AK), San Francisco (CA), Salt Lake City (UT), and Las Vegas (NV)—representing diverse climatic zones. The analysis integrates thermodynamic and economic parameters, including the coefficient of performance (COP = 2–5) and annual fuel-utilization efficiency (AFUE = 80–97%), …

Mtl_Tx: A Multi-Task Transformer Model For Improved Radiation Time-Series Estimation, Hongfang Zhang, Adam Stavola, Hal Ferguson, Bence Budavari, Hongyi Wu, Chiman Kwan, Jiang Li

Electrical & Computer Engineering Faculty Publications

Controlling radiation doses at potential radioactive facilities is critical to ensuring the safety of both personnel and the public. At the Thomas Jefferson National Accelerator Facility (JLab), multiple sensors are deployed around the three experimental halls to monitor key parameters, including single-beam current, energy levels, current leakage, and radiation values during accelerator operations. In this study, we developed a Multi-task Transformer model, MTL_TX, to accurately estimate radiation doses at sensor locations based on historical data, with the aim of enhancing safety in accelerator facilities and surrounding public areas. To improve estimation accuracy, we integrated two innovative components into the proposed …

Machine Learning-Based Lifetime Prediction Of Lithium Batteries: A Comparative Assessment For Electric Vehicle Applications, Abdelilah Hammou, Raffaele Petrone, Demba Diallo, Boubekeur Tala-Ighil, Philippe Makany Boussiengue, Hicham Chaoui, Hamid Gualous

Electrical & Computer Engineering Faculty Publications

This paper evaluates and compares four data-driven methods (Gaussian Process Regression (GPR), echo state network (ESN), gated recurrent unit (GRU), and long short-term memory (LSTM)) for lithium-ion capacity prognostics adapted to electric vehicle conditions. This comparison aims to find the most efficient prognosis method considering two constraints: the limitation of computational power and the unavailability of on-board capacity measurement that requires full charge and discharge conditions. The machine learning models are trained using capacity values estimated under vehicle conditions. The ageing data is collected from cycling tests of two battery chemistries, Lithium Fer Phosphate (LFP) and Nickel Manganese Cobalt (NMC), …

Healthcare Digital Twins: A Methodological Literature Review On Integrating Iot And Ai For Personalized Medicine And Predictive Care, Sara Shahnazinia, Mahsa Tavasoli, Abdolhossein Sarrafzadeh, Ali Karimoddini

Electrical & Computer Engineering Faculty Publications

Digital Twin (DT) technology has the potential to revolutionize healthcare delivery and enhance patient outcomes through personalized and precision medicine, simulation models for operations and interventions, and drug discovery. However, successful implementation of DTs in Internet of Things (IoT) and artificial intelligence (AI) healthcare is contingent upon addressing key challenges such as privacy, ethics, and robust data security. This paper presents a methodological literature review of DT applications in healthcare, systematically analyzing the current state of research, key enabling technologies, and implementation challenges. The review summarizes DT categorization approaches (application-based, technology-based, and real-time function-based); delineates core DT components such as …

Gem-Can: A Real-World Dataset Of Can-Bus Attack Scenarios On An Autonomous Vehicle For Intrusion-Detection Research, Mahsa Tavasoli, Abdolhossein Sarrafzadeh, Ali Karimoddini, Tienake Phuapaiboon, Milad Khaleghi, Daniel Tobias

Electrical & Computer Engineering Faculty Publications

This paper presents GEM-CAN, a labelled Controller Area Network (CAN) dataset captured from an autonomous GEM e6 platform under both normal operation and controlled cyber-attack conditions.

The dataset contains ∼143 K frames comprising (i) ∼ nominal autonomous operation (∼100k messages), (ii) DoS floods using arbitration ID 0 × 00000000 (∼41 K messages), and (iii) data-tampering injections that reuse legitimate IDs for brake and steering-lock (∼1.3 K messages). Each record includes timestamp, arbitration ID (11/29-bit), DLC, eight payload bytes, and a Normal/Attack label. A companion metadata file enumerates attack windows, PCAN bus-load traces, bitrate, and test conditions. Data were collected with …

Multimodal Machine Learning For Alzheimer's Disease Classification Using Adni Biomarker Fusion, Hesameddin Mostaghimi, Hamid R. Okhravi, Bahar Niknejad, Daniel A. Cohen, Michel A. Audette, Alzheimer's Disease Neuroimaging Initiative

Electrical & Computer Engineering Faculty Publications

Despite ongoing advances, accurate diagnosis of Alzheimer’s disease (AD) remains challenging due to its multifactorial nature, comorbidities, and clinical heterogeneity. Accordingly, approaches that combine multimodal data may improve AD classification by integrating complementary information. To investigate this, we evaluated classification performance using a preprocessed ADNI-3 dataset comprising a shared set of clinical/cognitive features along with four imaging modality-based cohorts: trimodal (MRI + amyloid PET + tau PET), MRI + amyloid PET, MRI + tau PET, and MRI-only. We trained a range of supervised machine learning (ML) and deep learning (DL) classifiers using stratified five-fold cross-validation and evaluated performance using accuracy, …

Interpretable Battery Soh Prediction: A Comparative Interpretability Framework For Multi-Architecture Ml Models, Shafiyee Islam, Gon Namkoong

Electrical & Computer Engineering Faculty Publications

This work introduces a unified interpretability-efficiency framework for lithium-ion battery state of health (SOH) prediction using hybrid deep learning architectures. We comparatively analyze four hybrid models: CNN LSTM MultiHead, CNN Feature Extractor LSTM, DNN LSTM, and DNN BiLSTM to disentangle how network topology, feature composition, and computational design influence both predictive fidelity and physical interpretability. By integrating Monte Carlo Shapley (MC Shapley), background occlusion SHAP (BoSHAP), and ablation analysis, we quantify the contribution and robustness of five electrochemical feature groups: time, capacity, voltage, dQ/dV and peaks of dQ/dV from NASA battery dataset. The results reveal a consistent dominance of differential …

An Explainable Cs-Mitigation Triangular (Ecsmt) Framework To Secure Graph Neural Networks, Sabah Ettahri, Sergio Pallas Enguita, Chung-Hao Chen, Wen-Chao Yang

Electrical & Computer Engineering Faculty Publications

This research addresses cyber risk by defending against backdoor attacks on Graph Neural Networks (GNNs). We propose the Explainable Complex System-Mitigation Triangular (ECSMT) Framework, which integrates Robust Training, Graph Regularization, and Data Sanitization into a lightweight, hardware-efficient defense layer. To evaluate structural generalizability, we conducted empirical evaluations across three distinct benchmark domains (AIDS, MUTAG, and PROTEINS) using a Graph Isomorphism Network (GIN) backbone. Under a baseline 5% backdoor subgraph trigger injection ratio, ECSMT achieves excellent utility retention, securing a Clean Accuracy (CA) of 97.33% (±0.62%) while reducing the Attack Success Rate (ASR) from 97.00% down to 69.45% on the primary …

Generalized Inverter Fault Detection Using Normalized Current Features And A Lightweight Bilstm Network, Mohammad Zamani Khaneghah, Mohamad Alzayed, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

Fault detection and diagnosis of three-phase inverter-fed motor drives is essential for ensuring system reliability, safety, and continuous operation in applications such as electric vehicles and industrial automation. This paper proposes a data-driven fault detection framework based on normalized current features and a lightweight bidirectional long short-term memory (BiLSTM) network which can be generalized to different motor power rating in the same controller system. A compact set of six time-domain features, consisting of the mean and root-mean-square (RMS) values of the phase currents, is extracted and normalized with respect to the average RMS value. This normalization effectively removes dependency on …

Calculus In Context: The Five College Calculus Project, James Callahan, Kenneth Hoffman, David Cox, Donal O'Shea, Harriet Pollatsek, Lester Senechal

Open Educational Resources: Textbooks

Calculus in Context: The Five College Calculus Project is a thorough reframing of what it means to learn calculus. It starts with a “clean slate;” there is no presumptive commitment to any aspect of the traditional course. Instead, the curriculum is guided by the following principles:

• Calculus is fundamentally a way of dealing with functional relationships that occur in scientific and mathematical contexts. The techniques of calculus must be subordinate to an overall view of the underlying questions.
• Technology radically enlarges the range of questions we can explore and the ways we can answer them. Computers can be engaged as …

Stakeholder Co-Design Of Sustainable Urban Pest Management Strategies, Elizabeth C. Lowe, Nathan J. Butterworth, Alexander Austin, Cameron Webb, Tanya Latty

Research outputs 2022 to 2026

The high levels of broad-spectrum insecticides used to manage invertebrates in many cities around the world has significant environmental and health impacts. Integrated pest management (IPM) is a framework for sustainable pest control practice, but uptake of IPM remains low in most cities. We used participatory action research with pest management industry stakeholders (practitioners, industry representatives, researchers and government employees) to identify key issues in urban pest management in Australia, set priorities for change and discuss collaborative solutions. Via an online survey and a face-to-face workshop, the participants identified the key themes of education and training, public awareness, environmental impacts …

The Value Of Saltbush Revegetation For Biodiversity In A Highly Fragmented Landscape, Robert A. Davis, Michael D. Craig, Tim S. Doherty, Jonathan D. Majer, Eddie J.B. Van Etten

Research outputs 2022 to 2026

Secondary salinization is a significant global issue affecting up to 1 billion hectares of land, impacting biodiversity and particularly vegetation. One form of revegetation that can provide a return to landholders is saltbush (Atriplex spp.), which provides forage for a wide range of livestock. The effectiveness of saltbush plantings in providing useful habitat for fauna in agricultural landscapes suffering from secondary salinity is poorly understood. We aimed to address this knowledge gap by surveying bird, ant, and spider communities, and quantifying vegetation structure and floristic composition in saltbush and native woodlands in agricultural landscapes affected by secondary salinization in Western …

Analysis Of Policies And Incentives For The Successful Implementation Of Hydrogen-Fueled Medium-Duty And Heavy-Duty Vehicles In Humboldt County, California, Alka Verma

Cal Poly Humboldt theses and projects

The 21st century has seen a significant rise in global greenhouse gas (GHG) emissions, with the transportation sector contributing 23% of these emissions. Medium-duty and heavy-duty vehicles (MD/HD) are particularly impactful, accounting for over a quarter of transport-related emissions. In Humboldt County, California, transportation represents 53% of total emissions, with MD/HD vehicles being a major contributor. As light-duty vehicles shift to zero-emission alternatives, the MD/HD sector faces unique challenges. Hydrogen fuel cell vehicles offer a promising solution, providing longer range, higher energy density, and quicker refueling compared to battery electric vehicles (BEVs). These features make hydrogen an attractive option for …

Assessing Coho Salmon (Oncorhynchus Kisutch) Distribution Using Traditional Sampling Methods In Conjunction With Edna On Trinity River Tributaries Within The Hoopa Tribal Boundaries, Ely D. Boone

Cal Poly Humboldt theses and projects

Detecting the presence of species using environmental DNA (eDNA) is a fast-growing field of study that shows much promise for population monitoring. In fisheries biology, eDNA is a potentially powerful tool compared to traditional fisheries monitoring techniques such as visual observation via snorkel surveys. When eDNA and traditional survey methods are employed in tandem, using the same spatial and temporal sampling design, eDNA often detects target species when traditional methods do not. In this study, instead of utilizing this paired study design, I implemented eDNA and snorkel surveys at different sampling scales with eDNA being collected at a single downstream …

Can Forest Thinning Activities Be Characterized With Public Data?: Evidence From California’S Million Acre Strategy (2020-2023), Selena Rowan

Cal Poly Humboldt theses and projects

Can forest thinning activities be quantitatively described using public data? This study evaluates the feasibility of doing so using geospatial datasets and project-level documentation associated with forest operations tracked under the California Wildfire & Forest Resilience Task Force’s Million Acre Strategy (2020–2023). As fuels reduction efforts expand to address increasing wildfire risk, there is growing demand for detailed information on thinning activity characteristics and associated biomass generation to support management evaluation, biomass utilization, and life-cycle emissions modeling. However, the extent to which existing public data provide sufficient detail to support such analyses remains unclear.

This thesis analyzes forest thinning activities …

Integrating Multi-Method Monitoring To Characterize Seasonal Habitat Use Of Green Sturgeon (Acipenser Medirostris) In Humboldt County, California, Olivia S. Boeberitz

Cal Poly Humboldt theses and projects

Green sturgeon (Acipenser medirostris) are a long-lived, anadromous, iteroparous species that spawns in large West Coast North American rivers but ranges widely in the coastal ocean. For coastal California Native American Tribes, green sturgeon are a vital part of their cultural heritage and traditions, and central to their food security. Two genetically distinct population segments (DPSs) of green sturgeon are recognized in the United States, Canada, and Mexico; the southern DPS is listed as “threatened” under the Endangered Species Act and the northern DPS is considered of “conservation concern.” Although green sturgeon are known to use large estuaries …

Dimension Reduction Involving Exogenous Variables With Applications In Manufacturing And Healthcare, Linxi Li

Theses and Dissertations

High-dimensional data analysis presents diverse challenges, including the curse of dimensionality, the complexities of working with datasets that combine large feature spaces with limited sample sizes, and difficulties in identifying meaningful relationships among variables. As datasets grow in size and complexity across different fields, it is increasingly important to develop practical approaches for extracting essential information from such data. Dimension reduction methods address these challenges by alleviating the effects of high dimensionality, enhancing the ability to reveal hidden patterns, and uncovering latent structures within the data to support further analysis. Some methods reduce dimensionality while preserving all relevant information, offering …

An Investigation Into The Mechanisms, Barriers, Degree And Sphere Of Risk Influence In Corporate Security, Nicola Lockhart

Theses: Doctorates and Masters

This study investigates the sphere of corporate security risk influence within organisations, addressing the conceptual and practical ambiguity surrounding the activity’s capacity to shape organisational decisions, behaviours, and risk priorities. While corporate security’s protective role is widely recognised, its broader organisational risk influence remains under-theorised. The study defines the sphere of risk influence as the range of organisational stakeholders and environments with which the corporate security activity interacts, and within which it may engage, persuade, and mobilise action. This sphere is analytically constituted through the intersection of three dimensions: the mechanisms through which influence is attempted, the barriers that constrain …

Development Of Virtual Reality Learning Environments In Science To Engage Secondary Students In Hazardous Activities, Luke Spartalis

Theses: Doctorates and Masters

This research examines the need for change in including hazardous activities in education. As different learning technologies develop, platforms that retain authentic outcomes via virtual reality are needed. The main objective of this research is to examine the inclusion of Virtual Reality Learning Environments (VRLEs) to determine their value in areas that include hazardous conditions. The research considered the ways that VR tools could be optimised to support these activities, as well as considering the challenges of VRLE implementation. The study examined whether VRLE’s allowed for authentic experiences to sufficiently drive an acceptance of VR to complement existing teaching and …

Enhancing Healthcare Security: Manifold-Aware Machine Learning For Robust Adversarial Attack Detection In Iomt Networks, Mohmmad Al-Fawa’Reh, Mohammed Kaosar

Research outputs 2022 to 2026

The widespread adoption of Internet of Medical Things (IoMT) devices and the increasing movement towards telehealth have revolutionized healthcare delivery but also introduced significant security challenges. Tiny Machine Learning (TinyML) models deployed on resource-constrained medical devices are vulnerable to adversarial attacks that can compromise patient data and device functionality, posing risks to patient safety. To address these critical security concerns, this paper proposes MARD (Manifold-Aware Robust Defense), a defense mechanism designed to enhance the robustness of TinyML models. MARD trains a compact student model by transferring knowledge from a teacher model that incorporates Graph-based Manifold Regularization (GMR) and Manifold Mixup …

Priority Questions For The Next Decade Of Blue Carbon Science, Peter I. Macreadie, George E. Biddulph, Pere Masque, Hilary Kennedy, Jimena Samper-Villarreal, J. Patrick Megonigal, Hannah K. Morrissette, Tania E. Romero-Gonzalez, Vanessa Hatje, Jana Friedrich, Sigit D. Sasmito, Kenta Watanabe, Inés Mazarrasa, Dorte Krause-Jensen, Janine B. Adams, Miguel Cifuentes-Jara, Ariane Arias-Ortiz, Andre S. Rovai, Milica Stankovic, Kirsten Isensee, Ana M. Queirós, Luzhen Chen, Jorge Herrera-Silveira, Catriona L. Hurd, Rashid Ismail, Ken W. Krauss, Anna Lafratta, Maria M. Palacios, William E.N. Austin

Research outputs 2022 to 2026

Blue carbon ecosystems, classically defined as mangroves, tidal marshes and seagrasses, but increasingly expanded to include ecosystems such as tidal flats, macroalgal forests and shelf sediments, contribute to climate change mitigation and biodiversity support. Here, seven years after the last global assessment of research priorities, we conducted a priority-setting exercise to identify persistent knowledge and implementation gaps, and the strategic priorities that must be addressed to enable scalable, high-integrity and equitable management of blue carbon ecosystems in a rapidly evolving policy and finance landscape. The highest priority focuses on managing blue carbon ecosystems to support coastal communities while integrating traditional …

Redox-Mediator Enhanced Electrochemiluminescence Under Non-Aqueous Conditions, Steven J. Blom, Fazeleh Mesgari, El M.S. Martin, Egan H. Doeven, David J. Hayne, Timothy U. Connell, Peter J. Barnard, Narges Saeedizadeh, Seyed Mohammad Jafar Jalali, Paul S. Francis

Research outputs 2022 to 2026

The introduction of small-molecule redox mediators into aqueous co-reactant electrochemiluminescence (ECL) systems has emerged as an effective strategy to increase signal intensity. Herein, we investigate the influence of a series of neutral iridium(iii) complexes (Ir(pmi)3, Ir(ppy)3, Ir(ppz)3 and Ir(ppy)2(acac)) as redox mediators for co-reactant ECL in acetonitrile. Using [Ru(bpy)3]²⁺ as a benchmark luminophore and tri-n-propylamine (TPrA) as a co-reactant, the redox mediators elicit similar effects in this solvent to those of their sulfonated [Ir(sppy)3]³⁻ and [Ir(sppz)3]³⁻ analogues under aqueous conditions. The Ir(ppz)3 complex was most effective; at a concentration of 100 µM it produced an 11-fold increase …

A Knowledge-Driven, Ai-Assisted Cyber Defence Framework For Iomt Remote Patient Monitoring, Kulsoom S. Bughio, David M. Cook, Abdul M. Unar

Research outputs 2022 to 2026

The rapid adoption of Internet Medical Things (IoMT) technologies in remote patient monitoring has reshaped healthcare delivery by enabling continuous, real-time clinical observation outside traditional care settings. However, this shift has also expanded the cyber-attack surface across heterogeneous, resource-constrained medical devices, wireless networks, cloud services, and third-party platforms. In cyber warfare, healthcare has become an incorporated target of geopolitics, with hospitals, remote monitoring systems, and emergency health systems being used to broaden the attack surface for adversaries to exploit. Existing security approaches for IoMT environments remain largely manual, fragmented, and reactive, limiting their effectiveness in dynamically assessing vulnerabilities and supporting …

Design, Synthesis, And Characterization Of Phosphonate-Functionalized Diimide Ligands For Metal-Organic Framework Construction, Kenya Rosas, Jonathan Uribe

Posters - 2026

• Metal–organic frameworks (MOFs) are solid, porous materials composed of metalions reacted with organic linkers

• MOFs are customizable through variations in their metal ions, organic linkers, and the functional groups of the organic linker all yielding a structure with different properties

• MOFs have a variety of applications, ranging from storage and catalysis to drug delivery

• The synthesis and characterization of four phosphonate-based diimide ligands:
  • N,N´-bis(phosphonomethyl)-pyromellitimide (PPMI).
  • N,N´-bis(phosphonobenzyl)-pyromellitimide (PPMI-Ph).
  • N,N´-bis(phosphonomethyl)-3,3′,4,4′-biphenylenediimide (PBDI).
  • N,N´-bis(phosphonobenzyl)-3,3′,4,4′-biphenylenediimide (PBDI-Ph).

• These ligands were then coordinated with zinc or copper metal ions to make MOFs.

De Novo Protein Binding To Zinc Oxide Through Biomineralization Pathways, Jean-Mark A. Francis

Theses and Dissertations

De novo proteins are structurally distinct from proteins found in nature and thus capable of having their amino acid sequence modified to accomplish tasks such as increasing protein-nanoparticle binding without unfolding or decomposing. Zinc Oxide nanoparticles are functionally distinct from their bulk counterparts and are widely used as semiconductors in a variety of fields such as medicine and agriculture. With demand for these nanoparticles increasing, environmentally sustainable methods of Zinc Oxide nanoparticle synthesis are being investigated as an eco-friendly alternative to currently utilized but environmentally hazardous chemical and physical techniques. This research investigates the binding characteristics between Zinc Oxide nanoparticles …