scholarly journals Partial Polymer Blend for Fused Filament Fabrication with High Thermal Stability

Polymers ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 3353
Author(s):  
Muhammad Harris ◽  
Johan Potgieter ◽  
Hammad Mohsin ◽  
Jim Qun Chen ◽  
Sudip Ray ◽  
...  
Keyword(s):  
Thermal Degradation ◽  
Polymer Blend ◽  
Large Scale ◽  
The Novel ◽  
Fused Filament Fabrication ◽  
Scanning Calorimetry ◽  
Factorial Anova ◽  
Novel Approach ◽  
Chemical Grafting ◽  
High Structural Stability

The materials for large scale fused filament fabrication (FFF) are not yet designed to resist thermal degradation. This research presents a novel polymer blend of polylactic acid with polypropylene for FFF, purposefully designed with minimum feasible chemical grafting and overwhelming physical interlocking to sustain thermal degradation. Multi-level general full factorial ANOVA is performed for the analysis of thermal effects. The statistical results are further investigated and validated using different thermo-chemical and visual techniques. For example, Fourier transform infrared spectroscopy (FTIR) analyzes the effects of blending and degradation on intermolecular interactions. Differential scanning calorimetry (DSC) investigates the nature of blending (grafting or interlocking) and effects of degradation on thermal properties. Thermogravimetric analysis (TGA) validates the extent of chemical grafting and physical interlocking detected in FTIR and DSC. Scanning electron microscopy (SEM) is used to analyze the morphology and phase separation. The novel approach of overwhelmed physical interlocking and minimum chemical grafting for manufacturing 3D printing blends results in high structural stability (mechanical and intermolecular) against thermal degradation as compared to neat PLA.

Lead recovery from spent lead acid battery paste by hydrometallurgical conversion and thermal degradation

2019 ◽  
Vol 38 (3) ◽  
pp. 263-270
Author(s):  
Wenke Liu ◽  
Qingwei Qin ◽  
Dengqi Li ◽  
Guangqiang Li ◽  
Yinjie Cen ◽  
...  
Keyword(s):  
Thermal Degradation ◽  
Lead Oxide ◽  
Lead Dioxide ◽  
Scanning Calorimetry ◽  
Lead Carbonate ◽  
Lead Acid Batteries ◽  
Novel Approach ◽  
Lead Recovery ◽  
Main Component ◽  
Lead Acid

Spent lead paste is the main component in lead-acid batteries reaching end of life. It contains about 55% lead sulphate and 35% lead dioxide, as well as minor amounts of lead oxide. It is necessary to recycle spent lead paste with minimal pollution and low energy consumption instead of the conventional smelting method. In this study, a novel approach involving hydrometallurgical desulphurisation and thermal degradation is developed to recover lead as PbO products from spent lead acid batteries. First, the desulphurisation effects and phase compositions of products with different transforming agents were compared, and the optimum conditions using (NH4)2CO3 as a transforming agent were determined. And then, the thermal degradation processes of both precursors lead carbonate and lead dioxide were investigated to prepare α-PbO, Pb3O4, and β-PbO products in argon and air atmospheres, respectively. Both the desulphurisation precursors and the calcination products were characterised by thermogravimetry and differential scanning calorimetry, X-ray diffraction, and scanning electron microscopy. The results showed that the lead oxide products were prepared, including α-PbO at 450°C in argon, Pb3O4 and β-PbO at 480°C and 620°C in air, respectively.

scholarly journals The novel modeling approach for the study of thermal degradation of PMMA/nanooxide systems

2019 ◽  
Vol 38 (1) ◽  
pp. 95
Author(s):  
Mirjana Jovicic ◽  
Oskar Bera ◽  
Katalin Meszaros Szecsenyi ◽  
Predrag Kojic ◽  
Jaroslava Budinski-Simendic ◽  
...  
Keyword(s):  
Thermal Degradation ◽  
Degradation Kinetics ◽  
The Novel ◽  
Heating Rates ◽  
Scanning Calorimetry ◽  
Total Mass Loss ◽  
Thermal Changes ◽  
Silica Alumina ◽  
Individual Contributions ◽  
Kinetics Of

PMMA (poly(methyl methacrylate)) nanocomposites differing in their nature, size, and surface area were prepared containing one volume percent of silica, alumina or titania. These samples and pure PMMA were prepared in order to analyze how the presence of nanooxides affects the thermal stability and degradation kinetics of the materials. A detailed study of thermal degradation and thermal changes was performed by Simultaneous Thermogravimetry and Differential Scanning Calorimetry (SDT). The proposed mathematical model, including all three heating rates in one minimizing function, well fitted all TGA data obtained with a very high coefficient of correlation. This enabled an assessment of four decomposition steps of the PMMA samples and a calculation of their activation energies and individual contributions to total mass loss. The addition of the largest nanoparticles (titania) caused the highest activation energy for each DTG stage of the PMMA/nanooxide systems. The enhancement of head-to-head H–H bonding strength was achieved by addition of alumina and titania. The influence of the size and nature of nanoparticles on the glass transition temperature of prepared PMMA systems was also determined.

scholarly journals New experimental diagnostics in combustion of forest fuels: Microscale appreciation for a Macroscale approach

2018 ◽  
Author(s):  
Dominique Cancellieri ◽  
Valérie Leroy-Cancellieri ◽  
Xavier Silvani ◽  
Frédéric Morandini
Keyword(s):  
Thermal Degradation ◽  
Large Scale ◽  
Field Scale ◽  
Scanning Calorimetry ◽  
Natural Fire ◽  
Fire Propagation ◽  
New Device ◽  
Forest Fuels ◽  
Kinetics Of ◽  
Experimental Diagnostics

Abstract. In modelling the wildfire behaviour, a good knowledge of the mechanisms and the kinetic parameters controlling the thermal decomposition of forest fuel is of great importance. Lab-scale experimental diagnostics as Thermogravimetric Analysis (TGA), Differential Scanning Calorimetry (DSC), Cone Calorimeter (CC) or Fire Propagation Apparatus (FPA) led to valuable results for modelling the thermal degradation of vegetal fuels and allowed several upgrades of pyrolysis models. But, these works remain beyond large-scale conditions of a wildland or forest fire. In an effort to elaborate the kinetic models under realistic natural fire conditions, a mass-loss device specifically designed for the field scale has been developed. The paper presents primary results gained using this new device, during large-scale experiments of controlled fires. The experimental data collected at the field scale lead to a new insight about thermal degradation processes of natural fuel, when compared to the kinetic laws established in TGA. These new results, provide a global description of the kinetics of degradation of Mediterranean forest fuels.

scholarly journals School-level inequality measurement based categorical data: a novel approach applied to PISA

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Lucas Sempé
Keyword(s):  
Categorical Data ◽  
Large Scale ◽  
Predictive Performance ◽  
School Level ◽  
The Novel ◽  
Linear Regression Models ◽  
Gini Coefficients ◽  
Country Level ◽  
Irt Models ◽  
Novel Approach

AbstractThis paper introduces a new method to measure school-level inequality based on Item Response Theory (IRT) models. Categorical data collected by large-scale assessments poses diverse methodological challenges hinder measuring inequality due to data truncation and asymmetric intervals between categories. I use family possessions data from PISA 2015 to exemplify the process of computing the measurement and develop a set of country-level mixed-effects linear regression models comparing the predictive performance of the novel inequality measure with school-level Gini coefficients. I find school-level inequality is negatively associated with learning outcomes across many non-European countries.

scholarly journals Acrylonitrile Butadiene Styrene and Polypropylene Blend with Enhanced Thermal and Mechanical Properties for Fused Filament Fabrication

Materials ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4167 ◽  
Author(s):  
Muhammad Harris ◽  
Johan Potgieter ◽  
Sudip Ray ◽  
Richard Archer ◽  
Khalid Mahmood Arif
Keyword(s):  
Thermal Stability ◽  
Mechanical Stability ◽  
Hydrogen Abstraction ◽  
Acrylonitrile Butadiene Styrene ◽  
Fused Filament Fabrication ◽  
Scanning Calorimetry ◽  
Bed Temperature ◽  
Chemical Grafting ◽  
Significant Chemical ◽  
Butadiene Styrene

Acrylonitrile butadiene styrene (ABS) is the oldest fused filament fabrication (FFF) material that shows low stability to thermal aging due to hydrogen abstraction of the butadiene monomer. A novel blend of ABS, polypropylene (PP), and polyethylene graft maleic anhydride (PE-g-MAH) is presented for FFF. ANOVA was used to analyze the effects of three variables (bed temperature, printing temperature, and aging interval) on tensile properties of the specimens made on a custom-built pellet printer. The compression and flexure properties were also investigated for the highest thermal combinations. The blend showed high thermal stability with enhanced strength despite three days of aging, as well as high bed and printing temperatures. Fourier-transform infrared spectroscopy (FTIR) provided significant chemical interactions. Differential scanning calorimetry (DSC) confirmed the thermal stability with enhanced enthalpy of glass transition and melting. Thermogravimetric analysis (TGA) also revealed high temperatures for onset and 50% mass degradation. Signs of chemical grafting and physical interlocking in scanning electron microscopy (SEM) also explained the thermo-mechanical stability of the blend.

Covalent Atomic Bridges Enable Unidirectional Enhancement of Electronic Transport in Aligned Carbon Nanotubes

2019 ◽  
Author(s):  
Mingguang Chen ◽  
Wangxiang Li ◽  
Anshuman Kumar ◽  
Guanghui Li ◽  
Mikhail Itkis ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Electrical Transport ◽  
Large Scale ◽  
The Novel ◽  
Electrical Measurements ◽  
Metal Atoms ◽  
Aligned Carbon Nanotubes ◽  
Transport Channels ◽  
Walled Carbon Nanotubes ◽  
Bulk Structures

<p>Interconnecting the surfaces of nanomaterials without compromising their outstanding mechanical, thermal, and electronic properties is critical in the design of advanced bulk structures that still preserve the novel properties of their nanoscale constituents. As such, bridging the p-conjugated carbon surfaces of single-walled carbon nanotubes (SWNTs) has special implications in next-generation electronics. This study presents a rational path towards improvement of the electrical transport in aligned semiconducting SWNT films by deposition of metal atoms. The formation of conducting Cr-mediated pathways between the parallel SWNTs increases the transverse (intertube) conductance, while having negligible effect on the parallel (intratube) transport. In contrast, doping with Li has a predominant effect on the intratube electrical transport of aligned SWNT films. Large-scale first-principles calculations of electrical transport on aligned SWNTs show good agreement with the experimental electrical measurements and provide insight into the changes that different metal atoms exert on the density of states near the Fermi level of the SWNTs and the formation of transport channels. </p>

Green synthesis, characterization of silver sulfide nanoparticles and antibacterial activity evaluation

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Antibacterial Activity ◽  
Large Scale ◽  
Research Work ◽  
Silver Sulfide ◽  
Morphological Properties ◽  
Effective Diameter ◽  
Novel Approach ◽  
Transmission Electron ◽  
Green Route ◽  
Silver Sulfide Nanoparticles

This research work presents a facile and green route for synthesis silver sulfide (Ag2SNPs) nanoparticles from silver nitrate (AgNO3) and sodium sulfide nonahydrate (Na2S.9H2O) in the presence of rosemary leaves aqueous extract at ambient temperature (27 oC). Structural and morphological properties of Ag2SNPs nanoparticles were analyzed by X-ray diffraction (XRD) and transmission electron microscopy (TEM). The surface Plasmon resonance for Ag2SNPs was obtained around 355 nm. Ag2SNPs was spherical in shape with an effective diameter size of 14 nm. Our novel approach represents a promising and effective method to large scale synthesis of eco-friendly antibacterial activity silver sulfide nanoparticles.

Exploring normative perceptions and misperceptions of private, environmentally impactful behaviours

2020 ◽  
Author(s):  
Elaine Gallagher ◽  
Bas Verplanken ◽  
Ian Walker
Keyword(s):  
Social Norms ◽  
Behaviour Change ◽  
Social Contexts ◽  
The Novel ◽  
Normative Influence ◽  
Change Mechanism ◽  
Novel Approach ◽  
Classical Studies ◽  
Referent Group

Social norms have been shown to be an effective behaviour change mechanism across diverse behaviours, demonstrated from classical studies to more recent behaviour change research. Much of this research has focused on environmentally impactful actions. Social norms are typically utilised for behaviour change in social contexts, which facilitates the important element of the behaviour being visible to the referent group. This ensures that behaviours can be learned through observation and that deviations from the acceptable behaviour can be easily sanctioned or approved by the referent group. There has been little focus on how effective social norms are in private or non-social contexts, despite a multitude of environmentally impactful behaviours occurring in the home, for example. The current study took the novel approach to explore if private behaviours are important in the context of normative influence, and if the lack of a referent groups results in inaccurate normative perceptions and misguided behaviours. Findings demonstrated variance in normative perceptions of private behaviours, and that these misperceptions may influence behaviour. These behaviours are deemed to be more environmentally harmful, and respondents are less comfortable with these behaviours being visible to others, than non-private behaviours. The research reveals the importance of focusing on private behaviours, which have been largely overlooked in the normative influence literature.

scholarly journals Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Ariel Rokem ◽  
Kendrick Kay
Keyword(s):  
Ridge Regression ◽  
Large Scale ◽  
Imaging Data ◽  
Regularization Technique ◽  
Large Scale Data ◽  
Novel Approach ◽  
Manual Exploration ◽  
L2 Norm ◽  
Software Implementations ◽  
Brain Imaging Data

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

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