Rational Design and Evaluation of Novel Polymerization Initiators Based On Amine-Peroxide Redox Reactions

Radical polymerization accounts for 45% of all manufactured polymer, producing approximately 150 million tons of materials annually. These polymer products are ubiquitous in modern society and need continuous development of new radical initiators in order to meet the ever-changing demands for materials. In this dissertation, I focus on the design and evaluation of novel radical initiators to address persistent problems in the polymer field by discovering new mechanistic details about radical generation mechanisms that led to the improvement of various existing applications and enablement of new future applications. In my doctoral studies, I first examined the mechanism of a well-known polymerization method that utilizes amine reductant and peroxide oxidant. Despite its extensive use since the 1950s, the initiation mechanism of amine-peroxide redox polymerizations (APRP) has been poorly understood and therefore, advances in this polymerization method have been largely incremental and empirically driven. Through a combination of computational modeling and experimental analysis, I elucidated the APRP mechanisms including its rate-determining step and derived a kinetic model that utilizes the computational calculation to predict experimental polymerization rates. This new mechanistic understanding was then applied to computationally design new amine reductant initiators with faster initiation kinetics, leading to the discovery of an initiator system that was experimentally proven to outperform current state-of-the-art amines by ~20-fold, making iii it the most efficient amine redox initiator to date for use in amine-peroxide redox polymerization processes. I then evaluated structural variations in the peroxide oxidants within the framework of APRP using my previously developed kinetic model. By improving radical and anion stabilization with increased p-electron conjugation and increasing the electrophilicity of the peroxy bond with electron-withdrawing groups, I computationally designed several new peroxides and predicted that they would exhibit improved initiation rates when compared to the commonly used benzoyl peroxide. I then developed a redox-based 3D printing process with a custom direct writing printer that used an APRP redox pair optimized using my computational modeling to exploit its extremely high reactivity. Beyond redox initiation, I also developed a unique photoinitiator based on amine-peroxide redox initiation that enables continued polymerization after irradiation is ceased, known as dark curing. The photoactivation of this initiator creates both initiating radicals as well as the amine compounds that can react with a peroxide over an extended period. This dark curing photoinitiator achieved a remarkable 25-60% additional conversion after exposure and slightly improved mechanical properties in comparison to conventional continuous photocuring, which can contribute to the homogeneity of polymers by raising conversions in initially under-cured regions. Using laboratory experiments and computational studies, I then clarified the origin of the high initiation efficiency and showed that this photoinitiator may be the most-photon-efficient photoinitiator to date. Lastly, I expanded dark-curing photoinitiation to absorption in the visible range with a new chromophore through a series of computational predictions, including the study of positiondependent effects of substituents, electronic transitions, and energetics. A target compound was iv synthesized and optically examined to demonstrate strong visible light absorption. I then demonstrated extensive dark-curing with a high quantum yield. All of the new initiators that I developed in my doctoral studies have unprecedented capabilities that enable innovation in polymer synthesis. For instance, bone cement adhesive polymers can be more rapidly fabricated with lower cytotoxicity from my redox initiators with higher efficiency. On the other hand, my photoinitiators can allow rapid coating of curved surfaces present in automotive or airplanes without a large oven needed for thermal curing.&nbsp;</p

INSTA-BEEER: Explicit Error Estimation and Refinement for Fast and Accurate Unseen Object Instance Segmentation

Efficient and accurate segmentation of unseen objects is crucial for robotic manipulation. However, it remains challenging due to over- or under-segmentation. Although existing refinement methods can enhance the segmentation quality, they fix only minor boundary errors or are not sufficiently fast. In this work, we propose INSTAnce Boundary Explicit Error Estimation and Refinement (INSTA-BEEER), a novel refinement model that allows for adding and deleting instances and sharpening boundaries. Leveraging an error-estimation-then-refinement scheme, the model first estimates the pixel-wise boundary explicit errors: true positive, true negative, false positive, and false negative pixels of the instance boundary in the initial segmentation. It then refines the initial segmentation using these error estimates as guidance. Experiments show that the proposed model significantly enhances segmentation, achieving state-of-the-art performance. Furthermore, with a fast runtime (less than 0.1 s), the model consistently improves performance across various initial segmentation methods, making it highly suitable for practical robotic applications.Comment: 8 pages, 5 figure

Right sizes of nano- and microstructures for high-performance and rigid bulk thermoelectrics

In this paper, we systematically investigate three different routes of synthesizing 2% Na-doped PbTe after melting the elements: (i) quenching followed by hot-pressing (QH), (ii) annealing followed by hot-pressing, and (iii) quenching and annealing followed by hot-pressing. We found that the thermoelectric figure of merit, zT, strongly depends on the synthesis condition and that its value can be enhanced to similar to 2.0 at 773 K by optimizing the size distribution of the nanostructures in the material. Based on our theoretical analysis on both electron and thermal transport, this zT enhancement is attributed to the reduction of both the lattice and electronic thermal conductivities; the smallest sizes (2 similar to 6 nm) of nanostructures in the QH sample are responsible for effectively scattering the wide range of phonon wavelengths to minimize the lattice thermal conductivity to similar to 0.5 W/m K. The reduced electronic thermal conductivity associated with the suppressed electrical conductivity by nanostructures also helped reduce the total thermal conductivity. In addition to the high zT of the QH sample, the mechanical hardness is higher than the other samples by a factor of around 2 due to the smaller grain sizes. Overall, this paper suggests a guideline on how to achieve high zT and mechanical strength of a thermoelectric material by controlling nano-and microstructures of the material

Computational and Experimental Evaluation of Peroxide Oxidants for Amine-Peroxide Redox Polymerization

Amine–peroxide redox polymerization (APRP) is the prevalent method for producing radical-based polymers in the many industrial and medical applications where light or heat activation is impractical. We recently developed a detailed description of the APRP initiation process through a combined computational and experimental effort to show that APRP proceeds through SN2 attack by the amine on the peroxide, followed by the rate-determining homolysis of the resulting intermediate. Using this new mechanistic understanding, a variety of peroxides were computationally predicted to initiate APRP with fast kinetics. In particular, the rate of APRP initiation can be improved by radical and anion stabilization through increased π-electron conjugation or by increasing the electrophilicity of the peroxy bond through the addition of electron-withdrawing groups. On the other hand, the addition of electron-donating groups lowered the initiation rate. These design principles enabled the computational prediction of several new peroxides that exhibited improved initiation rates over the commonly used benzoyl peroxide. For example, the addition of nitro groups (NO₂) to the para positions of benzoyl peroxide resulted in a theoretical radical generation rate of 1.9 × 10⁻⁹ s⁻¹, which is ∼150 times faster than the 1.3 × 10⁻¹¹ s⁻¹ radical generation rate observed with unsubstituted benzoyl peroxide. These accelerated kinetics enabled the development of a redox-based direct-writing process that exploited the extremely rapid reactivity of an optimized redox pair with a custom inkjet printer, capable of printing custom shapes from polymerizing resins without heat or light. Furthermore, the application of more rapid APRP kinetics could enable the acceleration of existing industrial processes, make new industrial manufacturing methods possible, and improve APRP compatibility with biomedical applications through reduced initiator concentrations that still produce rapid polymerization rates

Online Gaming Traffic Generator for Reproducing Gamer Behavior

International audienceIn this paper, we proposed an online gaming traffic generator reflecting user behavior patterns. We analyzed the packet size and inter departure time distributions of a popular FPS game (Left4Dead) and MMORPG (World of Warcraft) for regenerating gaming traffic. The proposed traffic generator generates an inter departure time and gaming packetbased on analytical model of the gamer behaviors, then transmits the packet according to the inter departure time. Packet generation results show that generated packets of World of Warcraft is much different with analytical model, unlike Left4Dead. It is caused by Nagle algorithm and Delayed Acknowledgments of TCP. Thus, we disabled the Nagle algorithm in the proposed traffic generator. The generation results show that the revised proposed traffic generator guarantees goodness of fit in the generated traffic distribution

The Development of Transparent Photovoltaics

Transparent photovoltaics (TPVs), which combine visible transparency and solar energy conversion, are being developed for applications in which conventional opaque solar cells are unlikely to be feasible, such as windows of buildings or vehicles. In this paper, we review recent progress in TPVs along with strategies that enable the transparency of conventional photovoltaics, including thin-film technology, selective light-transmission technology, and luminescent solar concentrator technology. From fundamental research to commercialization of the TPV, three main perspectives should be considered: (1) high-power conversion efficiency at the same average visible transmittance; (2) aesthetic factors, which should not detract from applications such as buildings and vehicles; and (3) feasibility for real-world applications, including modularization and stability evaluation. We present the distinct analysis criteria for these main perspectives and discuss their importance. We also discuss possible research directions for the commercialization of TPVs

Modeling the Fate and Transport of Malathion in the Pagsanjan-Lumban Basin, Philippines

Exposure to highly toxic pesticides could potentially cause cancer and disrupt the development of vital systems. Monitoring activities were performed to assess the level of contamination; however, these were costly, laborious, and short-term leading to insufficient monitoring data. However, the performance of the existing Soil and Water Assessment Tool (SWAT model) can be restricted by its two-phase partitioning approach, which is inadequate when it comes to simulating pesticides with limited dataset. This study developed a modified SWAT pesticide model to address these challenges. The modified model considered the three-phase partitioning model that classifies the pesticide into three forms: dissolved, particle-bound, and dissolved organic carbon (DOC)-associated pesticide. The addition of DOC-associated pesticide particles increases the scope of the pesticide model by also considering the adherence of pesticides to the organic carbon in the soil. The modified SWAT and original SWAT pesticide model was applied to the Pagsanjan-Lumban (PL) basin, a highly agricultural region. Malathion was chosen as the target pesticide since it is commonly used in the basin. The pesticide models simulated the fate and transport of malathion in the PL basin and showed the temporal pattern of selected subbasins. The sensitivity analyses revealed that application efficiency and settling velocity were the most sensitive parameters for the original and modified SWAT model, respectively. Degradation of particulate-phase malathion were also significant to both models. The rate of determination (R2) and Nash-Sutcliffe efficiency (NSE) values showed that the modified model (R2 = 0.52; NSE = 0.36) gave a slightly better performance compared to the original (R2 = 0.39; NSE = 0.18). Results from this study will be able to aid the government and private agriculture sectors to have an in-depth understanding in managing pesticide usage in agricultural watersheds

Effects of NaOH Activation on Adsorptive Removal of Herbicides by Biochars Prepared from Ground Coffee Residues

In this study, the adsorption of herbicides using ground coffee residue biochars without (GCRB) and with NaOH activation (GCRB-N) was compared to provide deeper insights into their adsorption behaviors and mechanisms. The physicochemical characteristics of GCRB and GCRB-N were analyzed using Brunauer-Emmett-Teller surface area, Fourier transform infrared spectroscopy, scanning electron microscopy, and X-ray diffraction and the effects of pH, temperature, ionic strength, and humic acids on the adsorption of herbicides were identified. Moreover, the adsorption kinetics and isotherms were studied. The specific surface area and total pore volume of GCRB-N (405.33 m(2)/g and 0.293 cm(3)/g) were greater than those of GCRB (3.83 m(2)/g and 0.014 cm(3)/g). The GCBR-N could more effectively remove the herbicides (Q(e,exp) of Alachlor = 122.71 mu mol/g, Q(e,exp) of Diuron = 166.42 mu mol/g, and Q(e,exp) of Simazine = 99.16 mu mol/g) than GCRB (Q(e,exp) of Alachlor = 11.74 mu mol/g, Q(e,exp) of Diuron = 9.95 mu mol/g, and Q(e,exp) of Simazine = 6.53 mu mol/g). These results suggested that chemical activation with NaOH might be a promising option to make the GCRB more practical and effective for removing herbicides in the aqueous solutions

Application of airborne hyperspectral imagery to retrieve spatiotemporal CDOM distribution using machine learning in a reservoir

Colored dissolved organic matter (CDOM) in inland waters is used as a proxy to estimate dissolved organic carbon (DOC) and may be a key indicator of water quality and nutrient enrichment. CDOM is optically active fraction of DOC so that remote sensing techniques can remotely monitor CDOM with wide spatial coverage. However, to effectively retrieve CDOM using optical algorithms, it may be critical to select the absorption co-efficient at an appropriate wavelength as an output variable and to optimize input reflectance wavelengths. In this study, we constructed a CDOM retrieval model using airborne hyperspectral reflectance data and a machine learning model such as random forest. We evaluated the best combination of input wavelength bands and the CDOM absorption coefficient at various wavelengths. Seven sampling events for airborne hyperspectral imagery and CDOM absorption coefficient data from 350 nm to 440 nm over two years (2016-2017) were used, and the collected data helped train and validate the random forest model in a freshwater reservoir. An absorption co-efficient of 355 nm was selected to best represent the CDOM concentration. The random forest exhibited the best performance for CDOM estimation with an R2 of 0.85, Nash-Sutcliffe efficiency of 0.77, and percent bias of 3.88, by using a combination of three reflectance bands: 475, 497, and 660 nm. The results show that our model can be utilized to construct a CDOM retrieving algorithm and evaluate its spatiotemporal variation across a reservoir