ACTIVATED CARBON NANOFIBERS FROM RENEWABLE (LIGNIN) AND WASTE RESOURCES (RECYCLED PET) AND THEIR ADSORPTION CAPACITY OF REFRACTORY SULFUR COMPOUNDS FROM FOSSIL FUELS

The development of advanced engineering materials such as carbon nanofibers from low-cost, renewable and/or waste resources is a key aspect of sustainability. In addition, escalating concerns related to the presence of noxious sulfur compounds in commercial fuels are driving the need to develop more efficient desulfurization technologies. In this PhD dissertation research, activated carbon nanofibers were produced from a blend of lignin with recycled poly(ethylene terephthalate) (r-PET) and they were successfully tested for the adsorption of refractory sulfur compounds from a model diesel fuel. Starting from different lignin/r-PET mass ratios, precursor nanofibers of different morphologies were initially prepared using the electrospinning technique. With the aid of a Design-of-Experiments statistical methodology, electro-spun nanofibrous mats with a minimum average diameter of 80 nm were produced. The electro-spun nanofibers were characterized by Differential Scanning Calorimetry, Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy, Thermogravimetry and Scanning Electron Microscopy. Subsequently, electro-spun precursor nanofibers consisting of different lignin/r-PET mass ratios and of varying average diameters were carbonized into carbon nanofibers (CNFs). It was discovered that the morphology of the CNFs depends on a synergy between the average fibre diameter and the lignin/r-PET mass ratio of the precursor electro-spun nanofibers. These conditions were mapped and CNFs with an average fibre diameter close to 100 nm were prepared. The CNFs were characterized using N2 physisorption, Transmission Electron Microscopy, Raman spectroscopy, X-Ray Diffraction and Energy-Dispersive X-Ray Spectroscopy. Their structure consists mostly of disordered carbon, while the CNFs derived from 50/50 lignin/r-PET with ~400 nm average fibre diameter present the highest BET surface area (353 m2/g). Their chemical activation with KOH boosted their BET surface area to 1413 m2/g, while a further treatment with HNO3 anchored oxygen functional groups on their surface. These activated CNFs (ACNFs) were tested for the adsorption of 4,6-dimethyl dibenzothiophene (DMDBT) and of dibenzothiophene (DBT) from a model diesel fuel (n-dodecane). It was found that they exhibit a very high adsorption capacity (120.3 mgDMDBT/gC and 77.82 mgDBT/gC respectively), combined with remarkably fast adsorption kinetics. Therefore, the ACNFs have a great potential to be used as desulfurization adsorbents

ACTIVATED CARBON NANOFIBERS FROM RENEWABLE (LIGNIN) AND WASTE RESOURCES (RECYCLED PET) AND THEIR ADSORPTION CAPACITY OF REFRACTORY SULFUR COMPOUNDS FROM FOSSIL FUELS

Dementia is a condition in which higher mental functions are disrupted. It currently affects an estimated 57 million people throughout the world. Dementia diagnosis is difficult since neither anatomical indicator nor functional testing are currently sufficiently sensitive or specific. There remains a long list of outstanding issues that must be addressed. First, multimodal diagnosis has yet to be introduced into the early stages of dementia screening. Second, there is no accurate instrument for predicting the progression of pre-dementia. Third, non-invasive testing cannot be used to provide differential diagnoses. By creating ML models of normal and accelerated brain aging, we intend to better understand brain development. The combined analysis of distinct imaging and functional modalities will improve diagnostics of accelerated decline with advanced data science techniques, which is the main objective of our study. Hypothetically, an association between brain structural changes and cognitive performance differs between normal and accelerated aging. We propose using brain MRI scans to estimate the cognitive status of the cognitively preserved examinee and develop a structure-function model with machine learning (ML). Accelerated aging is suspected when a scanned individual’s findings do not align with the usual paradigm. We calculate the deviation from the model of normal aging (DMNA) as the error of cognitive score prediction. Then the obtained data may be compared with the results of conducted cognitive tests. The greater the difference between the expected and observed values, the greater the risk of dementia. DMNA can discern between cognitively normal and mild cognitive impairment (MCI) patients. The model was proven to perform well in the MCI-versus-Alzheimer’s disease (AD) categorization. DMNA is a potential diagnostic marker of dementia and its types

Carbon Nanomaterials for the Adsorptive Desulfurization of Fuels

The increasing demand for cleaner fuels and the recent stringent regulations of commercial fuel specifications have driven the research of alternative methods to upgrade the current industrial desulfurization technology. Adsorptive desulfurization, the removal of refractory sulfur compounds using appropriate selective tailor-made adsorbents, has shown up as a promising alternative in the recent years. Carbon nanomaterials, namely, graphene, graphene oxide, carbon nanotubes and carbon nanofibers, show a significant potential as desulfurization adsorbents. Their surface area and porosity, their ability of easy functionalization, and their suitability to serve as a support of different types of adsorbents have rendered them attractive candidates for this purpose. In this review, after a presentation of the current industrial desulfurization practice and its limitations, the structure and properties of the carbon nanomaterials of interest will be described, followed by a detailed account of their applications in adsorptive desulfurization. The major literature findings and conclusions will be presented and discussed as a road map for future research in the field