Chemical Composition Of Glass Research Articles

Prediction of Glass Chemical Composition and Type Identification Based on Machine Learning Algorithms

Ancient glass artifacts were susceptible to weathering from the environment, causing changes in their chemical composition, which pose significant obstacles to the identification of glass products. Analyzing the chemical composition of ancient glass has been beneficial for evaluating their weathering status and proposing measures to reduce glass weathering. The objective of this study was to explore the optimal machine learning algorithm for glass type classification based on chemical composition. A set of glass artifact data including color, emblazonry, weathering, and chemical composition was employed and various methods including logistic regression and machine learning techniques were used. The results indicated that a significant correlation (p < 0.05) could only observed between surface weathering and the glass types (high-potassium and lead–barium). Based on the random forest and logistic regression models, the primary chemical components that signify glass types and weathering status were determined using PbO, K2O, BaO, SiO2, Al2O3, and P2O5. The random forest model presented a superior ability to identify glass types and weathering status, with a global accuracy of 96.3%. This study demonstrates the great potential of machine learning for glass chemical component estimation and glass type and weathering status identification, providing technical guidance for the appraisal of ancient glass artifacts.

Short-range order and chemical compositions of glasses along the basaltic-rhyolite sub-alkaline join by Raman and FTIR spectroscopies

Six sub-alkaline glasses with compositions progressively shifting from the tholeiitic basalt (B100) end-member to the rhyolite (R100) end-member were investigated and analysed in the same frequency domain by both Raman and FTIR. This approach highlights spectroscopic similarities and differences such as positions, widths and intensities of Raman and FTIR bands, which may also exhibit significant overlapping. Both the Raman and FTIR spectra show several peaks grouped in three main vibrational windows: 200–650 (low frequency region, named F-I in this study), 650–850 (intermediate region, F-II) and 850–1250 cm−1 (high-frequency region, F-III). In line with previous investigations, the F-I interval can be ascribed to vibrational modes involving rings of tetrahedrally coordinated cations linked by bridging oxygens. It can be fitted with three components, whereas F-II involves the motion of Si atoms within its oxygen cage and is adequately represented by two components. Finally, F-III contains different T-O (T = tetrahedrally coordinated network-forming cation) stretching bands that can be tied to the overall degree of polymerization of the glass and are fitted with four components. In some glasses, the three and the two components within F-I and F-II are identifiable in both Raman and FTIR spectra; in cases of strong peak overlap, these peaks can be complementary to one another towards our interpretation of the molecular arrangement(s) in these glasses. Indeed, the positions of the four components in F-III are first constrained in the Raman spectra, which are more identifiable, then further refined using available Raman spectra for corresponding chemically simple silicate systems. The nine fitted components can reproduce the Raman and FTIR spectra extremely well.As a function of the amount of SiO2, the positions and intensities of the three low frequency components progressively shift in both Raman and FTIR. Similarly, the two bands in the F-II intermediate region exhibit a monotonic shifting of their positions. Indeed, the components at high frequency display less significant shifting of their positions as a function of SiO2, while their intensities change more markedly in the Raman spectrum compared to those for FTIR. The vibrational components measured in this study provide a referenced dataset of assignations of the most abundant natural volcanic glasses. Therefore, it provides a diagnostic tool based on the cross-validation of Raman and FTIR spectra to quickly identify the glass chemistry, offering the possibility to expand the applicability of remote investigations.

Study on the ancient glass composition based on the multivariate statistical analysis method

The Silk Road was a channel of cultural exchange between China and the West, and glass was the precious material evidence of early trade. The study of the relationship between the weathering of ancient glass products and glass types and chemical composition contributes to the preservation of ancient cultural relics and promotes the further study of the history of the Silk Road. Based on this, this paper analyzed the surface weathering of cultural relics, and the type, ornamentation, color, and chemical composition of cultural relics, and it was finally found that there is a significant correlation between glass type and surface weathering. Then, discriminant analysis and systematic cluster analysis were applied to establish the classification function for the glass type variables and the chemical composition variables. Finally, gray correlation analysis was used to explore the correlation between each chemical component, and found that SiO2-Al2O3 was the most associated with each other in lead-barium glass, and K2O-CaO was the highest in high potassium glass.

Broadband 1.53 μm Emission and McCumber Analysis of Er3+ Doped Alkali Oxyfluorophosphate Glass for Fiber Optic Communication Material

Er3+ doped alkali oxyfluorophosphate glasses were prepared by the melted quenching method and characterized their optical and photoluminescence properties. Firstly, the chemical compositions of glasses were varied by changing alkali fluoride (LiF and NaF), whereas the concentration of Er3+ was fixed at 1.00 mol%. The optical absorption pattern and intensity were resemblance. Broadband 1.53 μm emission was one prominent peak of Er3+ in glass and the highest emission intensity was obtained for NaF glass under the stimulating at 521 nm. Secondly, the NaF glass was prepared by variation of Er3+ concentration (0.1, 0.5, 1.0, 2.0, and 4.0 mol%). The emission intensity of glasses increases with an increase in Er2O3 concentration up to 2.00 mol% and decreases for higher Er2O3 concentration, because of the concentration quenching effect. Broadband emission was observed that it covers three telecommunication windows: S, C, and L bands. The absorption and emission cross-section of 2.00 mol% of Er3+ doped alkali oxyfluorophosphate were found to be 1.3895 x 10−20 cm2 and 1.7248 x 1020 cm−2 , respectively by using the McCumber theory. Both values led to the estimate of the internal gain coefficient for 4I13/2→4I15/2 emission transition. The gain coefficient is positive when P is higher than 40%. All results point out that Er3+ doped alkali oxyfluorophosphate glass could be useful for fiber optic communication material.

Effects of incorporating silica glass powder as a supplementary cementitious material on selected properties of portland cement mortar

The use of waste glass powder as a partial replacement for cement is an emerging practice for sustainable cement-based construction. Several studies have demonstrated that finely ground-powdered glass could be an interesting supplementary cementitious material (SCM). However, the performance of such material is influenced by the chemical composition of glass and its pozzolanic reactivity. In this context, the goal of this study is to investigate the use of silica-glass powder (SGP), which is characterized by a high content of silica (>96%), as a partial replacement of Portland cement in mortar. In this research work, mortars containing 7.5%, 10, 12.5, 15 and 20% SGP in replacement of an amount of cement, with three different water-to-binder ratios of 0.55, 0.60 and 0.65 are studied. Selected properties have been investigated at both fresh and hardened stages, which include workability, air content, flexural and compressive strengths estimated by destructive and non-destructive methods, water absorption by capillarity, permeability to chlorides and chloride ions diffusion. The experimental results confirm that incorporating SGP improves the mechanical strength of the mortar, notably in the long-term, and can considerably enhance its durability properties, especially by a decrease in sorptivity of the mortar and by an increase of its resistance to chloride ions diffusion. Correlations between selected physical and mechanical properties and certain durability indices are also established and discussed in this study.

Prediction of Chemical Composition Ratio of Ancient Glass before Weathering Based on LASSO Regression

Ancient glass is highly susceptible to weathering by the environment in which it is buried. During the weathering process, the internal elements of the glass undergo a large exchange with the environmental elements. This phenomenon leads to a change in the proportion of its composition. By analysing the chemical composition of ancient glass before and after weathering, the relationship between the changes can be identified, which means the original chemical composition can be predicted from the chemical composition of the glass after weathering. This is of great significance to the exploration and study of ancient artefacts. This paper first uses Spearman and Kendall correlation coefficient to identify the most influential factor on the weathering condition of glass from ornamentation, type and color, which eventually points to the glass type. This finding is secondly verified using a chi-square test. So the glass is divided into two types: high-potassium and lead-barium. By using the box plot to compare the chemical composition of high-potassium and lead-barium glass before and after weathering, it is found that there is a certain linear relationship between the chemical composition of glass before weathering and after weathering.LASSO regression was eventually applied to find the correspondence between the chemical composition of glass before and after weathering, thus enabling the prediction of the chemical composition of the glass before weathering.

Prediction of chemical composition of cultural relics glass based on moving average algorithm of least square method

In order to explore the chemical composition of ancient cultural relics glass and understand the structure of ancient cultural relics glass, according to the basic information of glass cultural relics, the characteristics of glass are quantified, and the chi-square independence test is carried out on whether weathering and the properties of glass. Based on the cross table and chi-square test, it can be concluded that high potassium glass is generally not easy to weathering, while lead barium glass is more prone to weathering. A total of four categories were established to effectively reflect the statistical law of the chemical composition content of glass with different properties, and the principal component factors of each category were explored to obtain the chemical composition of each category. The scatter line diagram of the chemical composition of the unweathered sampling points was drawn. Based on the least square method, the ideal trend value of each chemical composition was calculated by using the moving average algorithm, and the prediction of various chemical compositions was completed. The chemical composition of the glass can be successfully predicted by the model constructed in this study. This method has certain technical support and theoretical support for the study of ancient glass, and provides some reference.

A logistic regression-based model for classification and evaluation of chemical composition of silicate glass products

Glass is one of the earliest human-made materials, and it has played an irreplaceable role in the development of human civilization. In ancient China, glass was an important commodity on the Silk Road, and it became a valuable witness to early trade and cultural exchanges between China and the West. Due to manufacturing processes, ancient glass is easily affected by burial environments and undergoes weathering. During the weathering process, there is a significant exchange of elements between the glass and the environment, causing changes in the proportions of the glass's chemical composition, which can affect the correct determination of the glass type. In this paper, we studied multiple sets of weathered and unweathered silicate glasses, extracted data on the chemical composition, color, and patterns of the sampling points, and first found a significant difference between the six chemical composition elements in the unweathered parts for the two silicate glass categories. Then, we introduced logistic coefficients and established a chemical composition evaluation model based on logistic regression to obtain the classification patterns of the two glass types. To further subdivide the glass into subcategories, we selected the top five chemical composition contents with the highest standard deviation for clustering under each of the two glass types and divided them into three subcategories. We obtained specific partitioning methods and results and analyzed the rationality of the results.

Research on glass products classification based on machine learning

In order to analyze the classification rules of ancient Chinese high-potassium glass and lead-barium glass and select the appropriate chemical composition for each category to divide them into subcategories, this paper models the type and chemical composition of glass by random forest algorithm, divides the data by 3:7 and uses 3-fold cross-validation, and uses Accuracy, Recall, Precision and F1-score to evaluate the model after training, and the results are optimal. The strongly correlated chemical components under each glass category were obtained by using the K-Means clustering algorithm, and the clustering results were visualized by using unsupervised learning, and the subclass division results were better when k was taken 2 after visualizing different cluster data. Finally, the results of subclassification are visualized and the rationality and sensitivity analysis of the method are carried out, and it is found that the method is accurate.

Known Glass Compositions in Iron Age Europe—Current Synthesis and Emerging Questions

Ancient glass has been extensively studied from a technological and raw material provenance perspective since the middle of the 20th century. With the rising applications of analytical techniques in the field of Heritage Studies, the last two decades saw an exponential increase in publications on ancient and historical glass technology from around the globe. Given the amount of works on glass chemical composition, it is surprising to note that the long-held production model for Iron Age glasses found in Europe has only recently been challenged by the publication of uncharacteristic glass compositions. Traditionally, LBA glass industries based on plant-ash fluxes/HMG (Egypt and the Levant) and mixed-alkali fluxes/LMHK (Italy) are thought to be supplanted by natron-fluxed/LMG production operating in Egypt and the Levant since around the 9th century BCE. Recently, however, arguments have been put forth for a more diversified network of glassmaking traditions, including small-scale autonomous European workshops. This article reviews the current state of research into Iron Age (1st millennium BCE) glasses in Europe by examining the available published data on glass compositions to critically assess some practical and theoretical issues stemming from this heterogenous field of research. Key questions are addressed, and future lanes of research are proposed.

Composition analysis and identification of ancient glass products based on gray correlation

The study of ancient glass, as an important branch of scientific and technological archaeology, is an important physical material for exploring the economic, technological and cultural exchanges between China and foreign countries on the Silk Road. It is of great significance for the study of the development of ancient societies and cultural exchanges between China and foreign countries. This paper focuses on the composition analysis and identification of ancient glass products, based on data mining and software analysis, giving the weathering pattern of glass surface, predicting the chemical composition content before weathering, as well as cluster analysis and correlation analysis of different categories of glass and their chemical composition. We also concluded that the composition of silica, potassium oxide and barium oxide in high potassium glass artifacts changed to a great extent before and after weathering, while the composition of silica and lead oxide in lead-barium glass artifacts changed to a great extent, and finally, we constructed a multiple linear regression model for prediction by the data of each chemical composition at the weathering point, and conducted F-test on the model to prove that the prediction models of silica, potassium oxide, barium oxide and lead oxide had a good performance. The F-test of the model proved the reasonableness of the prediction models for silica, potassium oxide, barium oxide, and lead oxide. It can be concluded that the glass type has an influence on the chemical composition of glass, so the glass can be divided into two categories of glass: high potassium and lead-barium, and then the chemical composition of these two categories of glass were analyzed separately by gray correlation analysis to obtain the top five correlation ranking of the chemical composition of each category of glass, and then the differences in the chemical composition of these two categories of glass were compared.

Chemical compositions prediction of glass artifacts based on logistic regression and linear weighting

Chemical composition analysis is of great significance for studying the category, origin and production age of the ancient glass. In order to consider the influence of weathering on chemical composition, this paper proposes a prediction method of glass chemical composition based on logistic regression and linear weighting. First, the chi-square test shows that whether the glass surface is differentiated is related to the type of glass. Secondly, the chemical composition of glass relics is divided into two categories by binary logistic regression: significantly affected by weathering and not significantly affected by weathering. Then, for the chemical components not significantly affected by weathering, combined with descriptive statistical results, the mean value was used as the chemical composition content before weathering. For the chemical components significantly affected by weathering, the linear weighting method was used to predict the content of chemical components before weathering. The final results show that this method can better reflect the influence of weathering factors on the chemical composition of glass, which is of great significance for the prediction of the glass composition.

Classification and identification of glass based on principal component and systematic cluster analysis

The composition of glass relics in different regions is not completely the same. Meanwhile, due to the large amount of internal elements exchanged in the process of glass burial and weathering, in order to determine and find a method to classify and identify glass relics, principal component analysis and systematic clustering are used to select the appropriate indexes for clustering by selecting the composition indexes of existing glass samples. The types of unknown glass artifacts are determined by classification from the types of known glass artifacts. Finally, after multiple cluster analysis, two unknown types of glass relics were identified as high potassium and lead barium. After the identification, the glass chemical composition data were fine-tuned up and down, and it was found that the identification type remained unchanged and the identification effect was stable and accurate.

Analysis and Identification of the Composition of Ancient Glass Objects based on Statistical Research and Machine Learning Algorithms

In order to promote the study of ancient glass artifacts, this paper integrates the weathering principle and the nature of chemical elements in glass, and analyzes the color and chemical composition of glass using statistical and machine learning methods. First, the classification and regression tree featuring Gini index is applied to explore the classification criteria of high potassium glass and lead barium glass, and the accuracy of the model is tested by 10-fold cross-validation. Secondly, subclasses were classified for the collected sample data. In this paper, Two Step Cluster Algorithm is used to divide the high potassium glass into four subclasses and the lead barium glass into five subclasses based on the Euclidean distance between the samples. Finally, the Spearman’s rank correlation coefficients between chemical components are computed separately based on the proportion of each chemical component in glass to explore the correlation between them.

Composition analysis and identification of ancient glass products

Ancient glass chemical composition is diverse, and because of the environmental impact of weathering, the proportion of its composition will change, affecting the judgment of the glass category. Therefore, according to the relevant data, this paper queries a large number of literature, through reasonable statistical analysis to find the classification basis, and studies all kinds of glass relics before and after weathering, in order to play an important role in the composition analysis and identification of ancient glass products.To define the standard of correlation, so as to better explain the correlation degree of the respective chemical composition of the two types of glass; then, to compare the difference between the two types of glass in the above correlation relationship. The comparison model of individual differences was then established, and the gap between the two individual correlation coefficient was calculated from the absolute value distance, and compared with the defined evaluation criteria, which concluded that the glass of the two categories had significant individual differences in each pair of content of chemical composition. For example, both potassium oxide and calcium oxide are chemical components, with the absolute value distance of 1.319, and the difference level is very significant.

Application of Support Vector Machine Algorithm Incorporating Slime Mould Algorithm Strategy in Ancient Glass Classification

Glass products are important evidence of early East–West cultural exchanges. Ancient glass in China mostly consisted of lead glass, and potassium glass is widely believed to be imported abroad. In order to figure out the origin of glass artefacts, it is crucial to define the type of glass products accurately. In contemporary research on the chemical composition of ancient glass products, potassium glass is separated from lead glass primarily by the weight ratio of oxides or the proportion of lead-containing compounds. This approach can be excessively subjective and prone to mistakes while calculating the mass fraction of compounds containing potassium. So, it is better to find out the link between the proportion of glass’s chemical composition and its classifications during the weathering process of the glass products, to develop an effective classification model using machine learning techniques. In this research, we suggest employing the slime mould approach to optimise the parameters of a support vector machine and examine a 69-group glass chemical composition dataset. In addition, the results of the proposed algorithm are compared to those of commonly used classification models: decision trees (DT), random forests (RF), support vector machines (SVM), and support vector machines optimised by genetic algorithms (GA-SVM). The results of this research indicated that the support vector machine method with the sticky slime mould algorithm strategy is the most effective. On the training set, 100% accuracy was attained, while on the test set, 97.50% accuracy was attained in this research. The research results demonstrate that the support vector machine algorithm combining the slime mould algorithm strategy is capable of providing a trustworthy classification reference for future glass artefacts.

Controlled-release fertilizers from phosphate glass-matrix: A new ecological approach to match nutrients release with plants demand

Physico-chemical properties, structural characterization, and dissolution behaviors of four phosphate glasses modified by incorporating zinc, boron, and copper, acting as eco-friendly fertilizers with controlled release of macro and micronutrients for wheat plants, were investigated. The elaborated glasses were characterized by differential thermal analysis, density measurements, X-ray diffraction, FTIR spectroscopy, and Raman spectroscopy. The dissolution behaviors were investigated using weight and pH measurements. The ionic concentration of leachate solutions was determined using ICP-OES. Results proved that the glass's chemical composition and lattice structure play the mean role in controlling the release of nutrients from the glass. The seemingly different properties between the elaborated glasses depend on the strength's bonds of glass lattice-formers and modifiers as well as their ionic field strength of the incorporated elements. An agronomic valorization was carried out to evaluate the efficiency of these agriglasses on wheat crops. It revealed a positive impact on wheat growth, yield, photosynthetic parameters, and grain mineral content with improvement values ranging from 4 to 89% compared to conventional fertilizers. These results pave the way to applying these fertilizers in large-scale experimentation to confirm their potentiality in crop production and as eco-friendly fertilizers.

Molecular-Composition Analysis of Glass Chemical Composition Based on Time-Series and Clustering Methods

The weathering of ancient glass relics has long been a concerned. Therefore, a systematic and more comprehensive mathematical model with which to correctly judge the category of ancient glass products whose chemical composition changes due to weathering should be established. This paper systematically analyzes and studies the changes in the composition of ancient glass products as a result of weathering of. We first analyze the surface weathering of glass relics and its correlation with three properties and establish a multivariable time-series model to predict the chemical-composition content before weathering. Next, we use one-way analysis of variance for subclassification and, finally, we use a principal component analysis of the rationality, and change the significance level to determine its sensitivity, for the reasonable prediction of the chemical-composition content and classification to provide a theoretical basis for improving the model. This allows the model to provide reference values, which can be used in the protection of cultural relics, historical research, and other fields.

A study of the problem of compositional analysis of ancient glassware based on grey models

In ancient times, glass was a valuable object of trade between China and the West, with potassium glass and lead-barium glass being common. In order to classify and identify ancient glass objects, this paper establishes a relevant classification model based on the chemical composition of glass, which is of some significance for the classification and analysis of glass. In this paper, the frequency distribution of each component is plotted as non-standard normal distribution, and then Spearman correlation coefficient is selected to describe its correlation, and it is found that SiO₂ has a more active relationship with other components, so it is chosen as the reference column for grey correlation analysis, and it is concluded that SiO₂ is closely related to the flux component of its glass type.

Reinforcing civil infrastructure with waste glass-enhanced concrete: A comprehensive review of properties, performance and applications

Millions of tons of waste are produced globally each year, the bulk of which cannot be recycled. Furthermore, recycling waste also consumes energy and harms the environment. Additionally, suburban garbage disposal and accumulation pose a serious threat to the environment. Using waste products during the production of concrete allows for the removal of waste as well as the addition of advantageous properties to concrete. Due to their substantial amounts of released greenhouse gases, substantial consumption of energy, and heavy use of the environment's resources, the cement and glass sectors face a variety of challenges. Because waste glass is non-biodegradable, the current practise of discarding of it in landfills fails to provide an environmentally suitable disposal for the waste glass. Waste glass's chemical composition and pozzolanic properties promote its use in the mortar and concrete companies as an ecologically acceptable choice for the glass and concrete sectors. This study examines various findings brought to light by several research groups and offers fresh viewpoints based on an examination of underlying mechanisms and various glass trash types. The fresh characteristics, durability, compressive strength, electrical, thermal and microstructure of concrete are among the contents that have been evaluated. This review is projected to increase information of consuming recycled glass in concrete production, recognize areas in need of further study, and examine how glass qualities affect the durability and performance of concrete and cement products.