Thermogravimetric Analysis Data Research Articles

Chemical Potential Analysis as an Alternative to the van't Hoff Method: Hypothetical Limits of Solar Thermochemical Hydrogen.

The van't Hoff method is a standard approach for determining reaction enthalpies and entropies, e.g., in the thermochemical reduction of oxides, which is an important process for solar thermochemical fuels and numerous other applications. However, by analyzing the oxygen partial pressure pO2, e.g., as measured by thermogravimetric analysis (TGA), this method convolutes the properties of the probe gas with the solid-state properties of the examined oxides, which define their suitability for specific applications. The "chemical potential method" is here proposed as an alternative. Using the oxygen chemical potential ΔμO instead of pO2 for the analysis, this method does not only decouple gas-phase and solid-state contributions but also affords a simple and transparent approach to extracting the temperature dependence of the reduction enthalpy and entropy, which carries important information about the defect mechanism. For demonstration of the approach, this work considers three model systems; (1) a generic oxide with noninteracting, charge-neutral oxygen vacancy defects, (2) Sr0.86Ce0.14MnO3(1-δ) alloys with interacting vacancies, and (3) a model for charged vacancy formation in CeO2, which reproduces the extensive experimental TGA data available in the literature. The reduction behavior of these model systems obtained from the chemical potential method is correlated with simulated results for the thermochemical water splitting cycle, highlighting the exceptional behavior of CeO2, which originates from defect ionization. The theoretical performance limits for solar thermochemical hydrogen within the charged defect mechanism are assessed by considering hypothetical materials described by a variation of the CeO2 model parameters within a plausible range.

Crystal structures, electron spin resonance, and thermogravimetric analysis of three mixed-valence copper cyanide polymers.

The crystal structures of three mixed-valence copper cyanide alkanolamine polymers are presented, together with thermogravimetric analysis (TGA) and electron spin resonance (ESR) data. In all three structures, a CuII moiety on a crystallographic center of symmetry is coordinated by two alkanolamines and links two CuICN chains via cyanide bridging groups to form diperiodic sheets. The sheets are linked together by cuprophilic CuI-CuI interactions to form a three-dimensional network. In poly[bis(μ-3-aminopropanolato)tetra-μ-cyanido-dicopper(I)dicopper(II)], [Cu4(CN)4(C3H8NO)2]n, 1, propanolamine bases have lost their hydroxyl H atoms and coordinate as chelates to two CuII atoms to form a dimeric CuII moiety bridged by the O atoms of the bases with CuII atoms in square-planar coordination. The ESR spectrum is very broad, indicating exchange between the two CuII centers. In poly[bis(2-aminopropanol)tetra-μ-cyanido-dicopper(I)copper(II)], [Cu3(CN)4(C3H9NO)2]n, 2, and poly[bis(2-aminoethanol)tetra-μ-cyanido-dicopper(I)copper(II)], [Cu3(CN)4(CH7NO)2]n, 3, a single CuII atom links the CuICN chains together via CN bridges. The chelating alkanolamines are not ionized, and the OH groups form rather long bonds in the axial positions of the octahedrally coordinated CuII atoms. The coordination geometries of CuII in 2 and 3 are almost identical, except that the Cu-O distances are longer in 2 than in 3, which may explain their somewhat different ESR spectra. Thermal decomposition in 2 and 3, but not in 1, begins with the loss of HCN(g), and this can be correlated with the presence of OH protons on the ligands in 2 and 3, which are not present in 1.

The Influence of Mo Content and Annealing on the Oxidation Behavior of Arc‐Melted Cr–xMo–8Si Alloys

The influence of Mo (10–40 at%) additions to Cr–8 at%Si is systematically studied by analyzing arc‐melted alloys in the as‐cast and annealed state. The oxidation resistance is tested by thermogravimetric analysis (TGA) at 1200 °C in air for up to 100 h. Samples are characterized by X‐ray diffraction, scanning electron microscopy, and wavelength‐dispersive X‐ray spectroscopy. The studied Cr–xMo–8Si alloy series shows a beneficial effect of Mo on the corrosion behavior. For all of the investigated Mo contents, no nitridation is observed and the oxide scale adhesion is improved. An increased amount of intermetallic A15 phase leads to the formation of a SiO2 scale beneath the Cr2O3 layer during high‐temperature exposure. The internal SiO2 layer inhibits further internal oxidation of the A15 phase. Uniform size and distribution of A15, benefits the formation of a duplex oxide scale. Cr–25Mo–8 at%Si, shows the most promising oxidation behavior. The TGA data follows the paralinear law, with both the growth rate, kp, and volatilization rate, kv, of Cr2O3 being reduced by adding 10–25 at% Mo. A binary Cr/Mo–Si phase diagram is generated in order to determine accurate annealing conditions and estimate the stability range of the metastable σ phase.

Evaluation of reaction rate of thermogravimetric analysis data using periodic sinc function interpolation

AbstractThe periodic sinc function interpolation offers a compelling solution to address the issue of noise in the analysis of thermogravimetric analysis (TGA) data, thereby enhancing the outcomes of differential techniques such as the Friedman isoconversional method. In this study, we introduce a novel approach that leverages the periodic sinc function interpolation to directly obtain smooth reaction rates from TGA data, eliminating the reliance on numerical differentiation methods. The efficacy of this method has been confirmed through its application to noisy experimental data derived from the thermal decomposition of various polymers, showcasing its robustness. Readers are provided with the corresponding code for Gnu Octave, serving as a free alternative to MATLAB. Additionally, the activation energies calculated from the experimental data using both the Friedman method and periodic sinc function interpolation closely align with those determined by the integral Vyazovkin method, emphasizing the validity and reliability of this new approach.

Novel approach for optimizing mechanical and damping performance of MABS composites reinforced with basalt fibers: An experimental study

The purpose of this research is to explore how basalt fibers, when compounded in specific proportions, impact the mechanical and damping attributes of methyl methacrylate acrylonitrile butadiene styrene (MABS). The fabrication process involved compounding basalt fibers in a twin-screw extruder at four distinct weight percentages: 5%, 10%, 15%, and 20%, with an MABS matrix. This study uniquely employs a comprehensive suite of characterization techniques including dynamic mechanical analysis (DMA), thermogravimetric analysis (TGA), X-ray microcomputed tomography (micro-CT), scanning electron microscopy (SEM), tensile tests, and density measurements to evaluate the composite's performance. The research significantly reveals that the integration of basalt fibers enhances the damping characteristics of MABS composites, as confirmed by DMA. Additionally, micro-CT scans provide unprecedented insights into the uniform distribution of basalt fibers within the MABS matrix, thereby elucidating the underlying mechanisms for the observed improvements. TGA data further bolsters the composite's thermal resilience, revealing its aptitude for high-temperature applications. Our findings establish a novel correlation between the basalt fiber weight percentage and the damping properties, revealing a non-monotonic relationship. This study thus not only augments the understanding of MABS based composites but also opens new avenues for the exploitation of basalt fibers in advanced composite materials, particularly in terms of their damping capabilities.

Study on enhancing waste PVC management through predictive Machine Learning analysis of TGA and its economic benefits

The onset of COVID-19 has led to a sudden surge of existing medical plastic wastes, majority of them made up of PVCs. Pyrolysis, a globally adopted waste management alternative, can be a crucial aid to handle the bulk plastic wastes which makes the identification of thermal behaviour of materials important to aid in the scale-up of pyrolytic processes. The study presents a novel approach of utilising regression-based algorithms- Support Vector Regression (SVR), Random Forest Regression (RFR) and K-Nearest Neighbour algorithms (KNN), to predict the weight loss % at a heating rate of 20 K/min for 6 different PVC based medical plastic wastes. The correlation between the factors affecting Thermogravimetric Analysis (TGA) have been identified using heat maps, the Machine Learning (ML) models were trained on this TGA data and the model efficiencies were identified using performance metrics of Mean Squared Error (MSE), Mean Absolute Error (MAE) and R2. Several kinetic parameters like activation energy and reaction order were estimated using Coats - Redfern method followed by a cost analysis. The results showed that experimental and predicted weight loss were in good agreement with a regression R2 value > 0.97 for all the materials except the composite outer cover. Coats - Redfern method was successful in the estimation of kinetic parameters and the economic analysis indicated that the utilisation of ML in TGA analysis can have significant financial benefits for the concerned industry.

High temperature synthesis, surface characterization, and photoluminescence property of boron carbon nitride

The present study demonstrates the synthesis of boron carbon nitride (BCN) by molten salt method which utilizes melamine (source of carbon and nitrogen), boric acid (source of boron) and potassium chloride as the salt. The synthesis is performed at 915 °C for 4 h under N2-atmosphere. The synthesized material is further acid-treated. The physicochemical characterization data for as-synthesized and acid-treated materials confirm successful synthesis of BCN. SEM and AFM data display that synthesized BCN pre and post acid-treatment exhibit sheet-type morphology. Thermogravimetric analysis data confirm that the acid-treated material is thermally stable up to 800 °C, with only 2 % mass-loss is observed till 800 °C. Water contact angle measurement study demonstrates that acid-treatment significantly improves the surface hydrophilicity. Additionally, the low photoluminescence peak intensity of the acid-treated material indicates low electron-hole pair recombination rate. The synthesized material may be useful for the photocatalytic degradation of potentially toxic chemicals from water.

Crystal structure of the sodium salt of mesotrione: a triketone herbicide

The crystal structure of the sodium salt of mesotrione, namely, catena-poly[[sodium-μ3-2-[(4-methanesulfonyl-2-nitrophenyl)carbonyl]-3-oxocyclohex-1-en-1-olato] ethanol monosolvate], {[Na(C14H12NO7S)]C2H5OH} n , is described. The X-ray structural analysis results reveal that the coordination sphere is established by two chelating O atoms, the O atom of the coordinated ethanol molecule, and an O atom from the methylsulfonyl group of a neighboring molecule. Simultaneously, an O atom of the cyclohexane fragment serves as a bridge to a neighboring sodium ion, forming a flat Na–O–Na–O quadrangle, thereby forming a mono-periodic polymer. The structure displays O—H...O hydrogen bonds and C—H...O short contacts. Thermogravimetric analysis (TGA) data indicate that the sodium salt of mesotrione decomposes in four stages.

Machine Learning Backpropagation Prediction and Analysis of the Thermal Degradation of Poly (Vinyl Alcohol).

Thermogravimetric analysis (TGA) is crucial for describing polymer materials' thermal behavior as a result of temperature changes. While available TGA data substantiated in the literature significantly focus attention on TGA performed at higher heating rates, this study focuses on the machine learning backpropagation analysis of the thermal degradation of poly (vinyl alcohol), or PVA, at low heating rates, typically 2, 5 and 10 K/min, at temperatures between 25 and 600 °C. Initial TGA analysis showed that a consistent increase in heating rate resulted in an increase in degradation temperature as the resulting thermograms shifted toward a temperature maxima. At degradation temperatures between 205 and 405 °C, significant depths in the characterization of weight losses were reached, which may be attributed to the decomposition and loss of material content. Artificial neural network backpropagation of machine learning algorithms were used for developing mathematical descriptions of the percentage weight loss (output) by these PVA materials as a function of the heating rate (input 1) and degradation temperature (input 2) used in TGA analysis. For all low heating rates, modelling predictions were observably correlated with experiments with a 99.2% correlation coefficient and were used to interpolate TGA data at 3.5 and 7.5 K/min, indicating trends strongly supported by experimental TGA data as well as literature research. Thus, this approach could provide a useful tool for predicting the thermograms of PVA materials at low heating rates and contribute to the development of more advanced PVA/polymer materials for home and industrial applications.

Application of the orthogonal collocation method in evaluating the rate of reactions using thermogravimetric analysis data

The orthogonal collocation method offers a solution to the challenge of noise in thermogravimetric analysis data, improving upon the differential methods like the Friedman isoconversional method. This new technique requires only a few data points for interpolation to recreate conversion-time or conversion-temperature curves, from which a smooth reaction rate curve can be derived by differentiating polynomials. Remarkably, this method demands fewer than 20 data points for a precise estimation of reaction rates in single step reactions. The method's validity is supported by both simulated and actual reactions. Users can implement this technique using provided Gnu Octave (a free alternative to MATLAB) codes. Furthermore, the activation energy results of experimental data obtained from the Freidman and orthogonal collocation methods were in good agreement with those evaluated from the integral Vyazovkin and Ortega methods.

Polylactide-Based Nonisocyanate Polyurethanes: Preparation, Properties Evaluation and Structure Analysis.

This study investigated the successful synthesis and characterization of nonisocyanate polyurethanes (NIPUs) based on polylactide. The NIPUs were synthesized by a condensation reaction of oligomers with hard segments (HSs) and synthesized carbamate-modified polylactic acid containing flexible segments (FSs). The oligomers with HSs were prepared from phenolsulfonic acid (PSA) or a mixture of PSA and hydroxynaphthalenesulfonic acid (HNSA), urea and formaldehyde. The mixing of oligomeric compounds with different amounts of formaldehyde was carried out at room temperature. Obtained NIPU samples with different hard segment content were tested for their mechanical and thermal properties. The tensile strength (TS) of all NIPU samples increased with an increasing amount of HSs, attaining the maximum value at an HS:FS ratio of 1:3. Samples prepared from PSA and HNSA showed higher tensile strength (TS) without significant change in elongation at break compared to the samples based only on PSA. Thermogravimetric analysis data indicated an absence of weight loss for all samples below 100 °C, which can be considered a safe temperature for using NIPU materials. Maximum degradation temperatures reached up to 385 °C. Fourier transform infrared spectroscopy results confirmed the existence of expected specific groups as well as the chemical structure of the prepared polyurethanes. DSC analysis showed the existence of two characteristic phase transitions attributed to the melting and crystallization of hard segments in the NIPU samples.

Thermal degradation evaluation of polyethylene terephthalate microplastics: Insights from kinetics and machine learning algorithms using non-isoconversional TGA data

The thermal stability of polyethylene terephthalate (PET) microplastics (MPs) is crucial in diverse applications, necessitating a comprehensive understanding of their behavior under high temperatures. This study explores the impact of chemical modifications and aging on the thermal degradation of different PET MPs, including pristine (Pr-PET), acid-modified (Mod-PET), and oxidatively aged (Ag-PET), using kinetics, thermodynamics, and machine learning algorithms using non-isoconversional thermogravimetric analysis (TGA) data. The calculated activation energy (E) and pre-exponential factor (A) for Coats-Redfern model showed Pr-PET with highest E value of 21.09 and 15.24 kJ/mol for zero-order and first order kinetic model, indicating its increased energy requirement for chemical reactions, while Mod-PET and Ag-PET demonstrated improved thermal resistance with lower E values. Thermodynamic data from kinetic parameters showed positive values of Gibbs free energy (ΔG°), enthalpy (ΔH°) and negative values of entropy (ΔS°) (−0.17. kJ/mol·K to −0.20 kJ/mol·K) indicated non-spontaneous degradation process, necessitated energy input, and reduced disorder with decreased reactivity. For accurate predictions of thermal behavior, various machine learning algorithms, including Support Vector Machine (SVM), Decision Tree Regressor (DTM), Random Forest Regressor (RFM), and Artificial Neural Network (ANN), were utilized. The RFM consistently outperformed other models, exhibiting the lowest error metrics and providing reliable predictions of weight loss and derivative thermogravimetric (DTG) for different PET MPs with a predicted-R2 of 0.999. This study sheds light on the thermal stability of PET MPs, considering chemical modifications and aging. The integration of these methods offers a powerful approach to understanding and predicting PET MPs under thermal conditions.

Phosphorous containing poly(amidoamine-organosilicon) (P@PAMAMOSi) flame retardants: Synthesis and sol-gel application on cotton fabrics

A novel hyperbranched phosphorous containing poly(amidoamine-organosilicon) (P@PAMAMOSi) was successfully synthesized and the structure was characterized by fourier transform infrared spectra (FT-IR) and NMR. The cotton fabrics were wrapped with P@PAMAMOSi by sol-gel method. The P@PAMAMOSi modified cotton fabrics were proven by FT-IR and scanning electron microscopy (SEM). The thermal stability and combustion properties of the blank cotton and treated cotton fabrics were explored by thermogravimetric analysis (TGA), limiting oxygen index (LOI), vertical flammability, and cone calorimetry. The data of TGA indicated that modified cotton fabrics have exquisite thermal stability, when the temperature reached 700 °C, the char residues of A30 treated cotton, B30 treated cotton, and C30 treated cotton were 29.5, 34.5, and 28.8 %, respectively; which is better than the 12.7 % of blank cotton. The results were verified by the LOI and combustion tests of cotton fabrics. The flame-retardant mechanism of treated cotton fabrics was analyzed by the FT-IR, X-ray photoelectron spectrometry (XPS), SEM, and energy dispersive X-ray spectroscopy (EDS), the treated cotton fabrics showed condensation phase and gas phase flame retardant mechanism. In addition, treated cotton fabrics showed desirable physical properties and mechanical properties.

Characterization of pyrolysis behavior for rigid polyurethane foam wastes with different densities through TGA-FTIR-DSC-MCC and Hill Climbing Optimization methods

Pyrolysis technology is one of the most effective thermal treatment methods recycling rigid polyurethane foam (RPUF) waste. To optimize pyrolysis recovery efficiency, quantifying the pyrolysis reaction kinetics and the heat required for the thermal treatment process is critical. This work focuses on establishing a systematic methodology to quantify pyrolysis reaction kinetics and thermodynamics for RPUF waste. RPUF waste with two different densities (RPUFρ100 and RPUFρ45) were investigated using thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) under anaerobic atmosphere to measure mass and heat flow information. The obtained data were inversely analyzed using a numerical framework known as ThermaKin2Ds. Pyrolysis reaction mechanisms with four and three consecutive first-order reactions were developed to reproduce the TGA data for RPUFρ100 and RPUFρ45, respectively. The Hill Climbing Optimization Algorithm was employed to adjust the reaction kinetics for an improved agreement between the ThermaKin2Ds-reproduced and experimental data. Based on the heat flow data from DSC tests, the specific heat capacity of each condensed-phase component was determined by using the inverse modeling method. Additionally, the micro-scale combustion calorimeter was used to generate the heat release data that served as the target data for inversely determining the heats of complete combustion, hc, of each pyrolyzate. The established pyrolysis reaction models was further validated by predicting the TGA results collected under different external heating conditions. Subsequently, the generated combustion-inhibiting gases (CO2) and combustion-promoting gases (olefins, alkanes, carboxylic acids, etc.) were characterized through TGA coupled with Fourier-transform infrared spectroscopy (TG-FTIR). This allowed us to elucidate the gas-phase combustion phenomena observed in MCC experiments. This work provides a core set of parameters for optimizing the thermal treatment conditions of RPUF waste.

Investigating supplementary cementitious materials' effects on stabilized aggregate performance, behaviour, and design aspects

Supplementary Cementitious Materials (SCMs) have been widely accepted in the concrete industry due to their varied advantages. However, the same reflects little in the case of Cementitious Stabilized Aggregates (CSA). The present work focuses on the potential and practical application of SCMs in CSA based on laboratory-based growth and performance analysis followed by their design aspects and economic advantages. Fly ash and Crushed Granulated Blast Furnace Steel Slag (GBFS) were chosen at 30% and 50% by weight of cement as partial replacement to cementitious material to prepare a cementitious stabilized mix of aggregates. Mechanical and microstructural properties were then assessed through critical tests like Unconfined Compression Strength (UCS), Flexural Strength (FS), Flexural Modulus (FM) and Thermogravimetric Analysis (TGA) over subsequent curing periods. Results from these tests revealed improved performance on including SCMs in significant proportion when compared to cement mix of aggregates. Power-law relationships proved optimal for comprehending strength and stiffness growth in CSA. Mixes with SCMs exhibited higher growth coefficients and growth rates for UCS, FS and FM. TGA data showed higher bound water loss %, indicating increased hydration for SCMs mixes. Composite pavement design revealed a 5 mm increase in the bituminous layer for cement-fly ash mix, while it remained unchanged for cement and cement-GBFS mix. Economic analysis demonstrated SCM cost savings of 6% to 16% in the CSA layer in mixes with SCMs.

NIR-absorbing TCBD and cyclohexa-2,5-diene-1,4-diylidene-expanded TCBD bridged derivatives of 1,1′-bis(N,N-dimethylaniline)-substituted ferrocene

Symmetrical and unsymmetrical donor-acceptor (D–A) based chromophores were designed and synthesized by the Pd–catalyzed Sonogashira cross–coupling reaction of 1,1′-diiodoferrocene with (4-N,N-dimethylaminophenyl)ethyne followed by [2+2] cycloaddition-retroelectrocyclization reaction with tetracyanoethylene (TCNE) or 7,7,8,8-tetracyanoquinodimethane (TCNQ). Ferrocene was used as a central donor unit and 4-(N,N-dimethylamino)phenyl group as the end-capping donor. Herein, we report the cycloaddition-retrocyclization reaction of TCNE/TCNQ groups on the disubstituted ferrocene containing more than one triple bond to get disubstituted tetracyanobutadiene (TCBD) and cyclohexa-2,5-diene-1,4-diylidene-expanded TCBD products. The effect of the conjugation length and different π–acceptor linkers were investigated by photophysical, redox and computational studies. The redox study reveals multiple oxidation waves corresponding to donor moieties (ferrocene and 4-(N,N-dimethylamino)phenyl group) and reduction waves corresponding to acceptor units (TCBD / cyclohexa-2,5- diene-1,4-diylidene-expanded TCBD). The thermogravimetric analysis data shows that the compounds exhibit high thermal stability. The computational studies reveal HOMO-LUMO gaps consistent with the experimentally obtained data.

The effect of uncertainty in calibration data on burning rate predictions

Comprehensive pyrolysis models describe solid phase reaction rates with respect to the material temperature and the concentrations of components. In theory, this aspect of comprehensive pyrolysis models allows for true predictive capabilities for pyrolysis models and, more generally, fire models. To utilize these capabilities, the reaction kinetics must be defined for the material of interest and there is a lack of publicly available data from which these kinetic parameters may be determined. There is also a lack of guidance for quality and repeatability for thermal analysis experiments, which complicates the use of mean thermal analysis data found in the literature to estimate the thermal degradation kinetics. This study details a numerical investigation on the effect of repeatability of thermogravimetric analysis data on the kinetics calibrated from the data. The uncertainty in the kinetic parameters that corresponded to target repeatability values imposed on synthetic calibration data was determined through a latin hypercube sampling procedure. The generalized polynomial chaos expansions method allowed propagation of the uncertainty of the kinetic parameters through a one-dimensional model of a bench-scale experiment. The relationship between uncertainty in calibration data and uncertainty in bench-scale model predictions was linear.

Liquid phase methanol synthesis by CO2 hydrogenation over Cu-Zn/Z catalysts: Influence of Cd promotion

BackgroundThe current scenario of high CO2 emissions, which is causing global warming and climate change, one of the greatest challenges faced by the global community. One of the most promising ways to recycle CO2 and thus mitigate its emission into the natural environment is the catalytic conversion of CO2 into valuable products such as methanol, hydrocarbons, dimethyl ether (DME), formaldehyde, syngas, alcohols and urea. MethodZeolite supported Cu-Zn bimetallic (Cu-Zn/Z) catalysts were synthesized by co-precipitation method. The synthesized catalysts were doped with different concentration of Cd to investigate the promoting role of Cd. To investigate the physicochemical profile of the calcined Cu-Zn/Z nano-catalysts, various analytical techniques were used. Significant findingsThermal stability of zeolite support was carried out using Thermogravimetric analysis (TGA). TGA data exhibited higher thermal stability of the zeolite support. XRD findings revealed highly dispersed Cd promoted Cu-Zn oxides on the surface of the zeolite support. Field Emission Scanning Electron Microscopy (FESEM) was used to investigate morphology, which indicated the nano size of synthesized catalysts with uniform distribution of metal oxides. Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES) detected bulk composition of each metal. BET surface studies revealed the mesoporous nature of Cd promoted Cu-Zn/Z catalysts. X-ray Photoelectron Spectroscopy (XPS) revealed the surface chemistry of Cd promoted Cu-Zn/Z catalysts. Activity data showed the active profile of Cd promoted Cu-Zn/Z catalysts for CO2 hydrogenation to methanol by accelerating the methanol synthesis rate from 73 to 157 g.meth/kg.cat.h. Structure-activity studies identified metal-support interactions and Cu stability as the main factors governing the three-phase methanol synthesis rate from CO2 hydrogenation. The importance of Cd promotion was further demonstrated by the fact that activity profile revealed better performance of the current catalysts with recently reported data in the literature.

Prediction of Thermogravimetric Data in the Thermal Recycling of e-waste Using Machine Learning Techniques: A Data-driven Approach.

The release of bromine-free hydrocarbons and gases is a major challenge faced in the thermal recycling of e-waste due to the corrosive effects of produced HBr. Metal oxides such as Fe2O3 (hematite) are excellent debrominating agents, and they are copyrolyzed along with tetrabromophenol (TBP), a lesser used brominated flame retardant that is a constituent of printed circuit boards in electronic equipment. The pyrolytic (N2) and oxidative (O2) decomposition of TBP with Fe2O3 has been previously investigated with thermogravimetric analysis (TGA) at four different heating rates of 5, 10, 15, and 20 °C/min, and the mass loss data between room temperature and 800 °C were reported. The objective of our paper is to study the effectiveness of machine learning (ML) techniques to reproduce these TGA data so that the use of the instrument can be eliminated to enhance the potential of online monitoring of copyrolysis in e-waste treatment. This will reduce experimental and human errors as well as improve process time significantly. TGA data are both nonlinear and multidimensional, and hence, nonlinear regression techniques such as random forest (RF) and gradient boosting regression (GBR) showed the highest prediction accuracies of 0.999 and lowest prediction errors among all the ML models employed in this work. The large data sets allowed us to explore three different scenarios of model training and validation, where the number of training samples were varied from 10,000 to 40,000 for both TBP and TBP + hematite samples under N2 (pyrolysis) and O2 (combustion) environments. The novelty of our study is that ML techniques have not been employed for the copyrolysis of these compounds, while the significance is the excellent potential of enhanced online monitoring of e-waste treatment and extension to other characterization techniques such as spectroscopy and chromatography. Lastly, e-waste recycling could greatly benefit from ML applications since it has the potential to reduce total and operational costs and improve overall process time and efficiency, thereby encouraging more treatment plants to adopt these techniques, resulting in reducing the increasing environmental footprint of e-waste.

Evaluating thermogravimetric analysis for the measurement of drug loading in mesoporous silica nanoparticles (MSNs)

In this study, a thermogravimetric analysis (TGA) method for measuring the drug loading in mesoporous silica nanoparticles (MSNs) has been developed and evaluated in comparison with the drug loading quantification by high-performance liquid chromatography (HPLC). Indapamide was loaded into two different types of MSNs, namely Mobile Crystalline Material (MCM-41, pore size = 1.2 nm) and Santa Barbara Amorphous (SBA-15, pore size = 4.1 nm). Physical mixtures of the drug and silica gave a linear correlation between the observed and expected drug content for both TGA and HPLC, which were used for calibration purposes. The limit of detection (LOD) for the TGA method obtained from the physical mixture calibration curve was 0.77 % (w/w) and the r2 value was 0.9936, whereas the HPLC had a LOD of 0.06 % (w/w) and an r2 value of 0.9933. The sensitivity of the TGA method was well established using the drug loading studies, as it can detect the low loading of MCM-41 at 2.2 ± 0.21 % (w/w), compared to 5.1 ± 0.12 % (w/w) with the SBA-15. In all samples applied, the multiple comparison analysis showed an insignificant difference between the two methods (p > 0.05). The TGA data presented good evidence for using this technique as a sensitive, cost-effective, and low-variable quantitative analysis in the drug loading determination of the MSNs. TGA is not a selective method of quantification, but optimising the method using the pure and blank samples of MSNs and drug can significantly improve the sensitivity. This work provides a unique approach to apply TGA as a selective and more favourable method to characterise MSNs to do early formulation developments.