Melissa Pasquinelli PhD
Area(s) of Expertise
Fibrous and (bio)polymeric materials, environmental chemistry, interfacial engineering, devise molecular modeling and data science approaches to optimize material properties and processing variables
- Microplastic and Nanoplastic Pollution: Characterization, Transport, Fate, and Remediation Strategies , FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING (2022)
- Molecular Insights into the Interfacial Properties of Cellulose Surfaces with Varying Types of Ionic Liquid Epoxies , ACS Applied Polymer Materials (2022)
- Multiscale Constitutive Modeling of the Mechanical Properties of Polypropylene Fibers from Molecular Simulation Data , MACROMOLECULES (2022)
- Ammonia Sensing Performance of Polyaniline-Coated Polyamide 6 Nanofibers , ACS OMEGA (2021)
- Cyclization kinetics of gel-spun polyacrylonitrile/aldaric-acid sugars using the isoconversional approach , JOURNAL OF APPLIED POLYMER SCIENCE (2021)
- Effects of Ionic Liquid Nanoconfinement on the CO2/CH4 Separation in Poly(vinylidene fluoride)/1-Ethyl-3-methylimidazolium Thiocyanate Membranes , ACS APPLIED MATERIALS & INTERFACES (2021)
- Exploring secondary interactions and the role of temperature in moisture-contaminated polymer networks through molecular simulations , SOFT MATTER (2021)
- Exploring secondary interactions and the role of temperature in moisture-contaminated polymer networks through molecular simulations (vol 17, pg 2942, 2021) , SOFT MATTER (2021)
- Hydrolytic Degradation of Polylactic Acid Fibers as a Function of pH and Exposure Time , MOLECULES (2021)
- Process-Property Relationships for Melt-Spun Poly(lactic acid) Yarn , ACS OMEGA (2021)
The REU site: Materials Engineering with Data Science (MATDAT) at North Carolina State University focuses on an emergent intersection between materials science and engineering and data science, which are traditionally disparate fields. The demand by the materials community for data-driven research for the analysis, design and development of materials has grown in the past few years, motivating a new, interdisciplinary approach to materials education and research. We seek to introduce such training through an interdisciplinary materials and data science REU. Ten (10) participants classified as rising juniors and seniors from various academic backgrounds in the physical mathematical sciences, and engineering will be recruited to spend ten summer weeks of mentor-guided research experiences. Projects will integrate machine learning, informatics, statistical and mathematical methods, and other data science tools in experimental and computational-based materials discovery. A diverse cohort of participants will be recruited each year, leveraging established relationships with minority-serving institutions such as the University of Puerto Rico- Mayaguez and North Carolina Central University (NCCU), a historically black college, which is the collaborating institution of the NRT to attract students historically underrepresented in STEM disciplines. Additional recruitment efforts will focus on students enrolled in institutions with limited research opportunities. Students will be supported by state-of-the-art resources such as the Analytical Instrumentation Facility (AIF), computing facilities, laboratories, and the NCSU Hunt Library, which provides a number of data visualization and coding resources that are available to all participants. Research activities will be supported by training in data science tools, responsible code of conduct, ethics, and cultivate a safe and productive environment. A range of professional skills seminars complement technical training, increasing participantâ€™s self-efficacy, engineering identities, problem-solving skills, literacy in other disciplines as related to data science, scientific communication, time management, and preparation for graduate studies. Exposing the students to relevant concepts and tools in data science through this REU program is aimed at encouraging them to pursue careers in STEM-related fields.
Data-Enabled, Interdisciplinary Research Traineeships in the Science and Engineering of Atomic Structure (SEAS) are proposed at the intersection of materials research, statistics, mathematics, and education. The vision of the SEAS traineeship program is to train a new generation of interdisciplinary, data-driven physical scientists who can develop and apply advanced statistical methods to the data being generated from cutting-edge scattering and imaging experiments; SEAS specifically addresses the NRT priority area of Data-Enabled Science and Engineering (DESE). SEAS graduates will be prepared to develop new ways to analyze data (including â€œBig Dataâ€) coming from a new and evolving generation of atomically sensitive instruments, including modern synchrotron and free electron laser X-ray sources, reactor and spallation neutron sources, and state-of-the-art electron microscopes. SEAS trainees and the tools they create (e.g., algorithms, software) are urgently needed in the field of materials science and physics, where instrumentation for materials research has evolved significantly faster than the ability to properly analyze the data. The effort integrates advanced instrumentation, data analysis, and computational tools, consistent with the NSF Strategic Plan and contributes directly and indirectly to the national Materials Genome Initiative (MGI), a multi-agency initiative spearheaded by the White House that advances the U.S. economy by enabling faster deployment of new materials. The proposed SEAS traineeships are the result of a two-year planning effort at NC State to bring together the materials science and statistics communities, including hosting of two interdisciplinary workshops and seeding interdisciplinary collaborative research projects (sponsored by the Kenan Institute for Engineering, Technology and Science and the Eastman Chemical Company - University Engagement Fund). Under SEAS, NC State will collaborate with staff scientists from national user facilities to pilot a new graduate training model for interdisciplinary traineeships in this national priority area. The effort will leverage several programs and initiatives at NC State, including the Analytical Instrumentation Facility (AIF, Director: J. Jones), Laboratory for Analytic Sciences (LAS, PI: Wilson), Statistical and Applied Mathematical Sciences Initiative (SAMSI, former A/Director: Smith), Data Sciences Initiative (DSI, Founding Director: Vouk), Research Triangle Nanotechnology Network (RTNN, Director: J. Jones), Center for Dielectrics and Piezoelectrics (CDP, Director: Dickey), and the Data-Driven Science cluster (Coordinator: Wilson).
The solution and solid-state behavior of CA has been extensively studied with common techniques such as light scattering and x-ray scattering. However, these tools have limitations when used for studies of 1) the behavior of CA in concentrated or turbid solutions, 2) mixtures of CA with other similar carbohydrates or soluble polymers, and 3) the solid-state behavior of CA blends. There is also interest in the behavior of CA in a dynamic environment, such as solvent evaporation as the CA transitions from a solution to a gel to a solid. Small Angle Neutron Scattering (SANS) is one analytical tool that can overcome these limitations. The use of SANS is also attractive since CA is soluble in common deuterated solvents, e.g., acetone, DMSO, THF, acetic acid, chloroform, etc., and deuterated CA can be easily prepared using deuterated acetic acid and acetic anhydride. This proof of principle project is intended to demonstrate that SANS can be used to study CA solutions, and to lay the groundwork for more elaborate studies of the behavior of CA copolymers with other polymers in solution and the solid state. This work requires collaboration with Oak Ridge National Laboratory (ORNL). We expect to use EQ-SANS diffractometer at SNS BL-6, which is designed to study non-crystalline, nano-sized materials in liquids, and should be suitable for the proposed study of CAs in deuterated solution. BIOSANS at HFIR could be an alternative beamline, though we understand that the HFIR is recovering from a prolonged downtime, which may limit availability. The high neutron flux at EQ-SANS is ideal for efficient proof-of-principle experiments.
In this project, we will develop a modeling framework for the prediction of fiber properties that can be employed in finite element simulations of nonwovens. The constitutive relations will be obtained through coarse-grained molecular dynamics simulations and validated experimentally. The fibers in this project are monocomponent or bicomponent polymer fibers involving polymer resins such as polypropylene (PP), polyethylene (PE), poly(ethylene terephthalate) (PET), polylactic acid (PLA), and polyamides. Experimental information of some fibers with experimentally known resin composition, processing conditions, characterization of morphology, and experimental testing of mechanical properties will be obtained. Using a coarse grained molecular modeling approach, polymers will be simulated and tested for mechanical properties. Multiscale molecular simulations with more than one level of coarse graining will allow for simulation of an entire fiber cross section with the specificity of the molecules corresponding to an experimental fiber. Using information from mechanical testing of fibers with a molecular modeling approach that has been validated from experimental information, we will fit constitutive equations to the simulation data. The theoretical framework for the constitutive modeling of fibers will be made versatile enough to fit fibers of various forms, accounting for behavior in a range of strain rates and temperatures. This information dictating the behavior of fibers will be tested in macroscale finite element simulations of nonwovens.
Many typical textile azo dyes, such as Remazol Red, Disperse Red 1, and Sudan dyes are known to metabolize to cytotoxic and genotoxic products. Many azo dyes containing amine groups have been shown to be carcinogenic. However, little research has been conducted to explore the epigenetic effects of commonly used azo dyes and their metabolites, modifying and affecting covalent modifications of DNA, and thus potentially leading to health outcomes such as cancer. Small molecules such as azo dyes have been demonstrated to effect epigenetic modifiers, such as DNA methyltransferases, and histone acetylases. The goal of this project is to use a variety of computational approaches to identify biological and azo dye metabolic pathways and epigenetic protein targets. Once the metabolic pathways and products have been identified, computational molecular modeling studies will be performed of the covalently modified DNA-dye adducts to see if chemical structural modifications can be made to the dye molecules to ameliorate their genotoxic effects.
There are growing numbers of biomedical applications where the use of a resorbable biomaterial is increasingly important from the point of view of the patient, the surgeon, the device manufacturer and the healthcare system. For example, a growing number of clinical operations are now relying on resorbable sutures, stents, vascular devices and tissue engineering scaffolds. One of the main advantages of the resorbable device for the clinician and the healthcare system is that the patient does not require a second surgical operation for device removal. There are however, a number of disadvantages from the manufacturers point of view in processing, cleaning, sterilizing and storing a resorbable polymer that tends to resorb (i.e. degrade) whenever it is exposed to elevated temperatures and relative humidity. The purpose of this study is to select a couple of key fiber forming resorbable polymers and study their resorption profile experimentally during melt spinning and drawing in terms of their loss in molecular weight and loss in breaking strength. This experimental approach will be complemented by mathematical modeling to train an artificial neural network, to predict the change in molecular weight due to the melt spinning processing and subsequent thermal drawing conditions as well as predicting the resorption profile in terms of the loss in strength and loss in mass based on the final molecular weight and its distribution. It is anticipated that this will generate relationships between melt processing conditions, POY drawing conditions and the rate of resorption that can be applied to other resorbable polymers that experience the same resorption mechanism.
The presence of Chemical and Biological Warfare Agents (CBWA) can dramatically threaten the defensibility of our forces and disrupt real-time communication in the battle field. In the presence of CBWA, the earliest avoidance or removal of contamination will not only provide war fighter life-sustaining protection but also continue real-time operational capability. Current individual and collective protective gear is primarily made of textiles that require enhanced protection against CBWA at lower weight. In terms of reducing logistics load for decontamination, there would be considerable benefits if the proposed technology provides enhanced protection at lower weight by (1) minimizing contact area and contact time between fabric and CBWA having a wide range of size and surface tension, (2) repelling CBWA and in addition to other chemical or biological threat materials such as toxic industrial chemicals, (3) keeping high air-permeability, (4) making CBWA get removed easily even after being compressed into the fabric structure, and (5) maintaining the quality after exposing the fabric to harsh conditions, e.g. dry and wet abrasion, hot and cold temperature, high and low relative humidity, etc.
The goal of this work is to develop innovative new approaches to integrating flame retardants into polymeric substances as a means of optimizing its effectiveness while reducing their adverse health effects. This goal will be accomplished through the synergistic combination of an interdisciplinary team of scientists and engineers.
The goal of this project is to utilize molecular simulation methods for studying the components of the AChE-liposome nanobiosensor and how the underlying physiochemical interactions of the components impact its function. This information will then be adapted in order to devise methods that can study the characteristics of a complete nanobiosensor (i.e., at the systems level) from its molecular constituents. These methods can be used to guide the design and optimization of more efficient and effective nanobiosensors, and also to investigate the functionality of these nanobiosensors when they are integrated into materials such as textiles.