Engineering Commons^™

Taylorkan-Vit: Parameter-Efficient Vision Transformers For Medical Image Classification, Kaniz Fatema, Dr. Emad Mohammed, Dr. Sukhjit Singh Sehra

Theses and Dissertations (Comprehensive)

Effective and interpretable classification of medical images remains a critical challenge in computer-aided diagnosis, particularly in data-scarce and resource-constrained clinical settings where traditional deep learning models prove impractical. This study addresses the fundamental barrier to Vision Transformer adoption in medical imaging—massive parameter counts and data requirements—through a systematic two-phase methodology. Phase 1 evaluates three spline-based Kolmogorov–Arnold Network (KAN) variants to identify the optimal nonlinear approximation function for parameter-efficient medical image classification: SBTAYLOR-KAN (B-splines with Taylor series), SBRBF-KAN (B-splines with Radial Basis Functions), and SBWAVELET-KAN (B-splines with Morlet wavelets). Comprehensive experiments across brain MRI, chest X-rays, and tuberculosis datasets—without any image …

Assessing Dissolved Organic Matter Sources And Dynamics In Urban Stormwater: Implications For Greenhouse Gases, Harper W. Schmalz

Theses and Dissertations (Comprehensive)

Stormwater management ponds (SWMPs) are important aspects of land-use planning and increasingly recognized as active sites of biogeochemical processing that influence carbon cycling; however, little research has investigated the controls on dissolved organic and dissolved inorganic carbon (DOC and DIC) within these systems. This thesis examined the processing and transformations of dissolved carbon between three compartments to support the development of a greenhouse gas (GHG) box-model for urban stormwater ponds, including SWMP sediment, surface water, and vegetation. The objective of this thesis was to assess the biogeochemical processes that govern the rate and transformation of DOC and DIC between these …

Evaluating The Impact Of Cognitive Distraction On Spaceflight-Relevant Task Performance Using Surface Electromyography And Motion Capture, Allyson K. Mitchell

UNF Graduate Theses and Dissertations

Cognitive distraction poses a risk to astronaut performance during complex, multitasking operations in spaceflight environments. This study examined the effects of cognitive load on neuromuscular coordination and task execution using surface electromyography (sEMG) and motion capture. Thirteen participants performed spaceflight-relevant tasks under undistracted and distracted conditions, with distraction induced through verbal questioning. EMG signals from eight upper-extremity muscles were processed using envelope filtering, peak normalization, and time normalization to enable inter-subject comparison, and group-level mean activation with standard deviation was analyzed. While overall muscle activation was similar between conditions, phase-dependent differences were observed, with undistracted trials showing higher activation during …

Mechanical Behavior Of Additively Manufactured Ti-6al-4v Eli Parts: Effects Of Laser Parameter Selection, Samuel Lopez

UNF Graduate Theses and Dissertations

Additive manufacturing (AM) of TI-6AL‑4V Extra‑Low Interstitial (ELI) enables complex geometries for fatigue‑critical medical device applications, yet fatigue performance remains sensitive to process‑induced defects. This work investigates the effect of laser process parameter selection on the microstructure, mechanical properties, and fatigue behavior of TI- 6AL‑4V ELI fabricated via laser powder bed fusion (L‑PBF) using a Renishaw RenAM system. The influence of laser parameters was isolated by holding powder chemistry, build orientation, scan strategy, sub‑transus annealing, and post‑processing constant between a non‑optimized baseline and an optimized parameter set selected based on tensile performance.

Optical microscopy showed the optimized condition exhibited improved …

Structural Batteries For Aerospace Applications, Tariqullah Wardak

Honors Undergraduate Theses

There is increasing pressure on the aviation sector to lower carbon emissions and switch to entirely electric and hybrid propulsion systems. However, the feasibility of standard lithium-ion batteries for long-range aircraft is limited, as they add substantial weight and occupy significant volume. Structural batteries, which combine load-bearing capability with energy storage, offer a potential pathway to lighter and more efficient aerospace systems.

This work investigates a carbon-fiber-based structural battery that utilizes carbon fiber as both a current-collecting, load-bearing electrode and a component of the composite structure. In contrast to lithium-ion chemistries, a zinc-based aqueous electrolyte is chosen for better environmental …

Preventing G/J Tube Dislodgement: A Device And Communication Innovation, Sarah Clark

2026

The Problem
G/J tube dislodgement is a frequent complication in pediatric patients
Leads to:

• Emergency department visits
• Hospital admissions
• Delays in nutrition/medication

Impact on Patients & Families:

• IV placement (traumatic)
• Radiation exposure
• Overnight hospital stays

Impact on Nurses & System:

• Increased workload (admissions, coordination)
• Occupied inpatient beds for stable patients
• Inefficient care processes

Aims/Objectives
Aim: Reduce unplanned G/J tube dislodgements and related hospital utilization.
Objectives: Develop a breakaway connector prototype

Implementation and Evaluation
Setting: Pediatric inpatient & outpatient system
Participants: Nurses (bedside, GI, IR), caregiver, innovation team
Process:
Roundtable discussions → identified workflow gaps
Communication/workflow audit
Developed device …

Using A Study Journal To Support Class Engagement And The Development Of Entrepreneurial Mindset Habits, Otilia Popescu, Dimitrie C. Popescu

Engineering Technology Faculty Publications

Engineering Technology programs were historically introduced to support non-traditional students and offer college pathways for students working and having already a developing career. This is even more true in the current academic environment, with a large percentage of students enrolled in engineering technology programs being either fully or part-time employed, active or retired military, and at different stages in their lives, usually with families to care for. Often, non-traditional students attend classes online, either synchronously or even more often asynchronously, due to their schedule constraints. Course instructors regularly face schedule or time management constraints from the students’ side, and they …

Towards Supporting Real-Time Estimation Of Vehicle Fuel Consumption And Co2 Emissions In Smart City Applications, Abrar Alali, Stephan Olariu

Computer Science Faculty Publications

This paper evaluates a simplified physics-based energy demand model designed to estimate vehicle fuel consumption and CO₂ emissions—a critical tool for sustainable transportation planning and smart city applications. Unlike data-driven regression models that lack generalizability for user-defined conditions or complex physics-based approaches that rely on extensive, often proprietary data, the simplified model is distinguished by its minimal parameter requirements, depending primarily on a single, overarching powertrain efficiency value. A key contribution is the comprehensive empirical evaluation of the simplified model against official Environmental Protection Agency (EPA) test data across multiple driving cycles and vehicle types, providing a rigorous validation previously …

Privacy-Preserving Federated Learning With Optimized Ensemble Weighting And Knowledge Distillation For Covid-19 Detection From Non-Iid Medical Imaging Data, Richard Annan, Hong Qin, Robert Newman, Madhuri Siddula, Letu Qingge

Computer Science Faculty Publications

Medical imaging enables rapid and accurate diagnosis of COVID-19, with CT scans proving especially effective. However, data privacy concerns limit collaborative model development across hospitals. To address this issue, we introduce a novel federated learning framework. It is referred to as Independent Knowledge Distillation with post-Ensemble Federated Learning (IKDEFL). Differential Privacy (DP) is integrated into the framework to improve privacy guarantees. Three DP mechanisms are evaluated. These include Fixed Gaussian, Gaussian Adaptive, and Tree Adaptive. The evaluation has been conducted on heterogeneous and Non-Independent and Identically Distributed (Non-IID) datasets. These datasets reflect real-world hospital scenarios. Results show that IKDEFL significantly …

Biosecure-Llm Framework: Protecting Llms From Cyberbiosecurity Threats And The Case For Independent Ai Safety Governance, Xavier-Lewis Palmer, Lucas Potter, Srdjan Lesaja, Sotirios Karathanasis, Mohammad Ghasemigol

Computer Science Faculty Publications

Large Language Models (LLMs) are becoming critical infrastructure in scientific, healthcare, and governmental contexts. As frontier AI laboratories increasingly partner with government agencies, a fundamental question arises: Who should control the safety and policy-enforcement layers that constrain model behavior? Current safety mechanisms (LLM guardrails) are typically designed for generic "harmlessness" and operate by detecting semantic patterns and refusing requests. However, they are inadequate governance instruments because they cannot implement auditable, domain-specific controls tied to external regulatory policy objects (e.g., control lists or rules governing personally identifying information). Even a perfectly aligned model is not able to express institution-specific policy without …

A Comparative Analysis Of Explainable Ai (Xai) Techniques For Transparent And Reliable Image Classification, Sovon Chakraborty, Shakib Mahmud Dipto, Kevin R. Pilkiewicz, Michael L. Mayo, Pratip Rana

Computer Science Faculty Publications

Evaluating the trustworthiness of black-box machine learning models remains a significant methodological challenge. Their lack of transparency and interpretability limits applicability, because stakeholders often seek transparency before trusting the results of black-box machine learning models. Explainable AI (XAI) methods provide for human-understandable justifications and informed decision-making of these black-box architectures. Therefore, it is imperative to select the proper XAI model tailored to specific tasks. In this research, we focus on examining four XAI techniques: PEEK, LRP, GRAD-CAM, and LIME to understand how they perform against each other for image classification tasks. We evaluate the performance, robustness, generalizability, noise stability, and …

Isotropic Shrinkage Of Patterned Vacancies Enables Three-Dimensional Nanoprecise Metastructures For Visible Light Applications, Quansan Yang, Gaojie Yang, Takahiro Nambara, Hiroyuki Kusaka, Yuichiro Kunai, Alex C. Matlock, Corban Swain, Brett Pryor, Yannick Salamin, Daniel Oran, Hasindu Kariyawasam, Ramith Hettiarachchi, Dushan Wadduwage, Marin Soljačić, Peter T. C. Soflaei, Edward S. Boyden

Computer Science Faculty Publications

Three-dimensional metastructures with nanoscale feature sizes exhibit unique properties compared with structures with larger feature sizes, but are difficult to fabricate. Here we introduce implosion carving (ImpCarv), a method for photopatterning vacancies of complex geometry throughout materials, followed by isotropic shrinkage (>10-fold). ImpCarv works by photoactivating sensitizers to generate reactive oxygen species that cleave a swollen hydrogel at defined points, followed by controlled shrinkage via dehydration. ImpCarv creates three-dimensional metastructures where the refractive index of each point throughout a material can be specified with nanoscale precision via material presence or absence. By leveraging refractive index programmability for precise phase …

Memebuddy: Dialog-Style Audio Representations For Engaging Non-Visual Meme Experiences, Chirag Bhansali, Vikas Ashok, Hae-Na Lee

Computer Science Faculty Publications

Image memes are a pervasive form of online communication, widely used to convey humor, opinions, and cultural references. Prior work has explored making memes accessible to blind users, primarily through auto-generated descriptive captions. While these approaches improve comprehensibility and sometimes incorporate prosodic or emotional cues, they often fail to capture the humor, narrative structure, and contextual nuances that make memes engaging. We present MemeBuddy, a system that models memes as dialog, generating structured, multi-turn audio representations using role-based speakers. MemeBuddy reinterprets a meme as a conversation between two speakers, integrating extracted meme text with contextual knowledge implicitly inferred by a …

Susceptibility To High-Fidelity Misinformation: An Eye-Tracking Analysis, Yasasi Abeysinghe, Gavindya Jayawardena, Enkelejda Kasneci, Sampath Jayarathna

Computer Science Faculty Publications

With the rise of online misinformation and AI-generated text, understanding human perception of news truthfulness is critical. In this study, we examine visual attention and cognitive processing using eye-tracking measures as individuals read fake and real news articles sharing nearly identical structure and imagery, differing only in subtle textual changes. Using the public FakeNewsPerception dataset, we analyze advanced gaze measures, including scanpaths, AOI transitions, and luminance-corrected pupil measures, beyond basic gaze features, in relation to news truthfulness and perceived believability. Results show that, given the high fidelity of the fake news, readers exhibited comparable visual scanning patterns, attention allocation across …

Distributed Semi-Speculative Parallel Anisotropic Mesh Adaptation, Kevin Garner, Polykarpos Thomadakis, Nikos Chrisochoides

Computer Science Faculty Publications

This paper presents a distributed memory method for anisotropic mesh adaptation that is designed to avoid the use of collective communication and global synchronization techniques. In the presented method, meshing functionality is separated from performance aspects by utilizing a separate entity for each - a multicore cc-NUMA-based (shared memory) mesh generation software and a parallel runtime system that is designed to help applications leverage the concurrency offered by emerging high-performance computing (HPC) architectures. First, an initial mesh is decomposed and its interface elements (subdomain boundaries) are adapted on a single multicore node (shared memory). Subdomains are then distributed among the …

Untrained Position-Encoded Multilayer Perceptron Network For Structured Illumination Microscopy Reconstruction, Sahil Sharma, Leonidas Zimianitis, Krishnendu Samanta, Balpreet Singh Ahluwalia, Joby Joseph, Dushan N. Wadduwage

Computer Science Faculty Publications

Structured Illumination Microscopy (SIM) enables super-resolution imaging by encoding high-frequency spatial information through patterned light. While traditional Fourier-based reconstruction methods are prone to artifacts under suboptimal conditions, recent deep learning approaches often require large training datasets and lack adaptability across different imaging setups. In this work, we present Position Encoded Multi-Layer Perceptron (PEM) network that leverages implicit neural representations (INRs) and SIM forward-model-driven modeling to reconstruct super-resolved images without any training data. PEM-SIM represents each spatial coordinate as a combination of sinusoidal functions across multiple frequencies, enabling rich encoding of fine spatial detail. A forward model grounded in SIM image …

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 …

Application Paths Of Semantic Modeling In Financial Fraud Detection And Risk Identification, Victor P. Gauthier, Daniel S. Wu

Computer Science Faculty Publications

Financial fraud and risk pose significant threats to economic stability and individual well-being. Traditional detection methods often struggle to keep pace with increasingly sophisticated fraudulent schemes. Semantic modeling, which focuses on understanding the meaning and relationships within data, offers a promising avenue for enhancing fraud detection and risk identification. This review paper explores the application paths of semantic modeling in this domain. We begin with a historical overview of fraud detection techniques, highlighting the limitations of traditional approaches. Subsequently, we delve into core themes, including knowledge graph-based fraud detection and semantic rule-based inference for risk assessment. We then compare and …

Land Use And Land Cover Simulation Via Integrated Modelling With Gis Techniques For Sustainable Land Utilization Development In The Northeast Khong Subwatershed Of Thailand, ฺBanchongsak Faksomboon, Thipphaphone Keoviyavong

Applied Environmental Research

Land utilization is an important indicator of socioeconomic and environmental changes caused by both natural and man-made factors. Land use and land cover (LULC) simulation is a critical tool for monitoring and predicting LULC and is essential for sustainable development, land resource management and planning. The cellular automata (CA) Markov model is the basis for the current study’s prediction of LULC changes in the Northeast Khong Sub Watershed (NKSW). Landsat data from 2013 to 2023 were used to investigate LULC classification and determine the spatiotemporal distributions of LULC. In addition, LULC data from 2013 and 2023 were used to generate …

Reduction Of Cr(Vi) With Infrared Light And Chemical Adsorption Of Cr(Iii) By A God-Crown/Bentonite Composite For Electroplating Waste Remediation, Andre Taufik Kurniawan, Muhammad Djoni Bustan, Sri Haryati

Applied Environmental Research

Heavy metal pollution, particularly chromium (Cr) from electroplating industrial waste, has severely threatened environmental quality and human health. This study aims to develop a composite adsorbent material based on bentonite and god crown biomass capable of removing chromium ions from liquid waste through a combination of reduction and adsorption mechanisms. The god crown/bentonite (GC/Bt) composite was synthesized at a mass ratio of 2:1 and calcined at 900°C. FTIR characterization revealed active functional groups (–OH, C=O, Si–O, and Al–O–Si), whereas BET analysis revealed a mesoporous structure (surface area 31.12 m2 g-1, pore diameter 4.37 nm) suitable for ion diffusion. The reduction …

Fabrication Of Urea With Amorphous Silica To Increase Rice Growth In Saline Soils, Thi Ha Chi Nguyen, Thi Hang Thuc Ha, Minh Phuong Nguyen, Ngoc Chuc Pham, Quang Bac Nguyen, Nguyen Huy Tuan Do, Ngoc Nhiem Dao, Trung Kien Nguyen

Applied Environmental Research

In recent decades, the decrease in rice cultivation areas in Vietnam, particularly in the Mekong River Delta, caused by climate change and soil salinization, has posed serious challenges for sustainable development. In addition to irrigation and agronomic practices, fertilizers that sustain rice growth under saline conditions play a crucial role in improving crop productivity. Amorphous silica has been reported to increase plant tolerance to drought, salinity, and heavy metal stress. In this study, nanosilica-coated urea (UCS) fertilizers were synthesized by coating urea granules with 1–5 wt% amorphous silica. The products were characterized by SEM, XRD, FTIR, TG‒DSC, and EDX to …

Heavy Metals And Organic Carbon In Sediments Of Seagrass Sediments Of Trang Province, Thailand, Siriporn Pradit, Pornthep Wirachwong, Thongchai Nitiratsuwan, Sujaree Bureekul, Supraewpan Lohalaksanadech, Thawanrat Kobkeatthawin, Monticha Jirajaras, Prakrit Noppradit, Sanya Sirivithayapakorn

Applied Environmental Research

In this study, the accumulation of heavy metals (Cd, Cr, Cu, Fe, and Pb) in water, suspended sediments, sediments, and seagrass in Kalase Bay, Trang Province, Thailand, during the 2024 dry season was investigated. These findings indicate that the enrichment factor (EF) for all the metals was less than 1, suggesting that anthropogenic contamination is not a significant concern in the area. The translocation factor (TF) values were less than 1 for all the metals except Cu, whose TF was greater than 1; however, these values were not statistically significant, indicating limited phytoextraction capacity. The bioconcentration factor (BCF) values for …

Spatial Modeling Of Natural Disaster Risk By Integrating Hazard, Vulnerability, And Exposure Factors Using Geospatial Techniques : Evidence From Mueang Tak District, Thailand, Suphatphong Ruthamnong

Applied Environmental Research

This study presents a comprehensive multihazard risk assessment for Mueang Tak District, Thailand, that integrates hazard, vulnerability, and exposure (H-V-E) dimensions. By utilizing geographic information systems (GIS) and remote sensing, the analytic hierarchy process (AHP) was employed to prioritize critical risk factors. GIS analysis integrated multisource spatial data, including Landsat 9 and Sentinel-2 imagery, CHIRPS precipitation, and SRTM-derived topography, while AHP weights were established on the basis of expert judgment and socioeconomic indices such as the relative wealth index (RWI) and population statistics. The results delineate distinct spatial risk clusters: high landslide potential is concentrated in the steep terrain of …

Carbon Stock Assessment In Sonneratia Apetala Afforested Mangroves : A Case Study From Cox’S Bazar, Bangladesh, Mohammad Ismail, Sayed Abu Johany, Tanmoy Dey, Zakia Sultana Teasa, Trishna Das, Prabal Barua, Tonima Hossain, Sajib Ahmed

Applied Environmental Research

Sonneratia apetala is a key species for mangrove afforestation projects in Bangladesh and plays a crucial role in ecosystem restoration and carbon sequestration. The research on how the total carbon stock (TCS) varies across S. apetala plantations in Cox’s Bazar remains limited. Thus, in this study, the biomass and soil carbon stock were quantified across nine S. apetala afforested sites in Cox’s Bazar to assess spatial variation and influencing factors. The total biomass carbon (TBC) was calculated by summing the aboveground (AGBC) and belowground (BGBC) biomass carbon, while the soil organic carbon stock (SOCS) was determined from the 0–40 cm …

Seasonal Wastewater Monitoring And Quantitative Microbial Risk Assessment Of Escherichia Coli In A University Wastewater System, Pathum Thani, Central Thailand, Natsima Tokhun, Natagarn Tongphanpharn, Weerawat Ounsaneha, Montip Jankeaw, Wiriyabhorn Klomsungcharoen, Patsara Wongsudi, Yawanart Ngamnon, Cheerawit Rattanapan, Kwang Mo Yang

Applied Environmental Research

In this study, the physicochemical properties of wastewater were characterized, and a quantitative microbial risk assessment (QMRA) of E. coli was conducted in the wastewater treatment system of the Faculty of Science and Technology, Valaya Alongkorn Rajabhat University, under the Royal Patronage, Pathum Thani, Thailand (VRU-SciTech). This study was conducted because the current water-quality assessment does not include the QMRA or evaluate the occupa-tional and incidental public risk of infection. Over a one-year monitoring period, this study quantified wastewater generation (~6.09 L person-1 day-1; ~3.21 × 103 m3 year-1) and analyzed its physicochemical parameters and E. coli concentrations. High concentrations …

Evaluation Of Soil Moisture Content, Ph, And Dominant Microorganisms In Crude Oil–Contaminated Soil Treated With Cow Dung, Indole-3-Acetic Acid (Iaa), And Simulated Microgravity, Amenze Ovenseri, Tawari-Fufeyin P.

Applied Environmental Research

Crude oil negatively affects soil physicochemical properties and microbial activity, thereby hindering plant growth and posing notable environmental and agricultural issues. The combined effects of cow dung, indole-3-acetic acid (IAA), and simulated microgravity on the recovery of soil degraded by crude oil were explored using Zea mays as the test plant. Soil was contaminated with crude oil at different concentrations (0%, 1%, 3%, and 5% v/w), with each concentration comprising eight treatments: cow dung, Zea mays seeds exposed to microgravity, and IAA alone or in combination. Changes in plant height, soil pH, soil moisture content, and soil microbial diversity were …

Metal–Organic Frameworks : Opportunities And Challenges In Nutrient Recovery, Suchana Amnuaychaichana, Chi-Wang Li, Pongsak (Lek) Noophan, Sumeth Wongkiew

Applied Environmental Research

Nutrient recovery from waste streams is a critical strategy for promoting sustainable agriculture and mitigating environmental degradation. Essential nutrients commonly present in organic waste and wastewater, such as nitrogen and phosphorus, can be reclaimed and reintegrated into agricultural systems. Metal‒organic frameworks (MOFs), which are composed of metal clusters coordinated with organic ligands, have emerged as promising materials for adsorption and have the potential for nutrient recovery and pollutant removal because of their high porosity, large surface area, and tunable physicochemical properties. These attributes enable MOFs to serve in diverse applications, including heavy metal adsorption, organic pollutant degradation, and nutrient transformations. …

Comparative Life Cycle Assessment Of End-Of-Life Crystal Silicon Photovoltaic Panels: Recovery Methods And Extended Life In Agricultural Application, Patima Chaichana, Vacharaporn Soonsin, Nattapong Tuntiwiwattanapun

Applied Environmental Research

The increasing deployment of crystalline silicon (c-Si) photovoltaic (PV) panels has raised concerns about their waste management. This study evaluated management strategies for discarded c-Si PV panels in Thailand, integrating environmental and economic analyses. Life cycle assessment (LCA) and cost-effectiveness analysis (CEA) were applied. The LCA can be divided into 2 parts: (1) secured landfill vs decentralized recycling by existing facilities vs centralized full recovery and (2) reusing PV panels in agricultural applications. The results revealed that secured landfills were the most environmentally burdensome (34.43 Pt), whereas centralized recycling achieved net benefits (-211.93 Pt) through emission reductions and recovery of …

Seasonal Variations In Co2 Emission From Poultry Manure-Amended Soils In Two Contrasting Tropical Agroecosystems, Gladys M. Akande, Adebayo J. Adeyemo, Chidozie J. Oraegbunam, Iduh J.J. Otene, Ezekiel C. Are, Babatunde S. Ewulo, Chioma M. Ahukaemere, Samuel O. Kolawole, Sunday E. Obalum

Applied Environmental Research

The soil remains the largest carbon sink, the capacity of which varies spatiotemporally and with anthropogenic activities, with implications for carbon dioxide (CO2) emissions and associated global warming. Despite the increasing popularity of organic amendments in the ecologically and anthropoculturally diverse farming systems of tropical Africa, manure-induced CO2 emissions in this region with high soil-carbon-sequestration potential are poorly documented. This study assessed seasonal CO2 emissions from poultry manure-amended sandy-clay-loam and sandy-loam soils in the Rainforest-Savannah and Southern Guinea Savannah zones, respectively, of Nigeria. Following manure application at 3 rates (0, 10, and 15 t ha-1), soil samples were collected during …

Life Cycle Assessment Of Co2-To-Methanol: Comparative Evaluation Of Direct And Alcohol-Assisted Hydrogenation Routes, Chayet Worathitanon, Naphat Chansonthi, Pawat Pinthong, Kritsana Suwanamad, Viganda Varabuntoonvit

Applied Environmental Research

The increasing severity of global warming, primarily driven by greenhouse gas emissions, underscores the urgent need for CO2 reduction and utilization strategies. Converting CO2 into methanol presents a promising approach, as methanol serves both as a fuel and a feedstock in various industries. This study evaluates the life cycle environmental impacts of three methanol production routes: (1) direct CO2 hydrogenation, (2) ethanol-assisted CO2 hydrogenation, and (3) propanol-assisted CO2 hydrogenation. Two energy scenarios are considered: conventional energy and wind power. Process simulations were performed using Aspen Plus V.14, and inventories were analyzed through Life Cycle Assessment (LCA) using the ReCiPe 2016 …