Measurement Model Research Articles

CAUSALITIES OF MOTIVATION, SELF-RELATED BELIEFS, AND STUDENT ENGAGEMENT TOWARDS HIGH SCHOOL STUDENTS’ ACHIEVEMENT USING STRUCTURAL EQUATION MODELING

Students’ academic performances is an important factor as it affects students’ chances of higher education and job opportunities. While cognitive factor (e.g., intelligence quotient) plays a major role in students’ performances in academic environment, it does not guarantee students’ academic achievement. Instead, researches shows that non-cognitive factor can affects student performance as well. Three non-cognitive factor that will be researched in this study are motivation, self-related beliefs, and engagement. Using Structural Equation Modeling (SEM) as the method of choice to accommodate the study of the factors. 699 of students’ sample was taken from six public high school in Denpasar to conduct the research. SEM is used for its ability to analyze latent factors using measurement model and its ability to estimate multiple structural relation simultaneously. The model with a Goodness-of-fit (GFI) score of 0.981 confirms that Motivation does significantly directly affects student performances. Self-related beliefs and Engagement on the other hand significantly directly affects Motivation, hence the two constructs significantly indirectly affect student achievement.

The Measurement Model of Family Strengths for Generation Alpha in the Thai Context.

The concept of the Thai family has shifted from the traditional family to families of various forms, such as single-parent families, same-sex families, skipped-generation families, and one-person households, which affects not only mental health, but future relationships of the children. This research aimed to analyze the factors that contribute to family strengths in the context of Generation Alpha and develop a measurement model tailored to a Thai context. The sample consisted of parents and caregivers of Generation Alpha, divided into two sections, with 521 participants for exploratory factor analysis and 508 participants for confirmatory factor analysis. Using stratified random sampling, data were collected via questionnaires distributed across four regions of Thailand. The discrimination index ranged from 0.280-0.896, with a Cronbach's alpha of 0.952. The results revealed a measurement model consisting of five key factors contributing to family strengths. The factor with the highest score was positive relationships, with a factor loading of 0.92. Additional factors included technology and media literacy, parental roles, good attributes, and mental immunity, with a factor loading of 0.80, 0.74, 0.55, and 0.44, respectively. The measurement model of family strengths for Generation Alpha in the Thai context was found to be consistent with the empirical data. These findings suggest that understanding these family strengths can significantly enhance the well-being of both Generation Alpha and their family members.

Striking the Balance: How Workplace Flexibility Enhances Engagement and Performance of Employees working in Public and Private settings

This study investigates the relationships between workplace flexibility, employee engagement, and performance of employees working in public and private settings. The purpose is to understand how flexible work arrangements influence employee engagement and overall performance across various industries. This research was conducted using a descriptive quantitative method. A structured questionnaire was used to collect data from 400 employees across finance, technology, healthcare, and manufacturing industries. The measurement model’s reliability was evaluated using the partial least square (PLS) structural equation modeling (SEM) approach (see table 3). Cronbach alpha (α) and composite reliability (CR) values were used. A prognosis model evaluation shows that all of the variables have SRMR values of 0.082 and NFI values of 0.914, suggesting that the model accurately matches with the experimental data. The Q2 predict value for employee performance (EP) of 0.352 shows excellent predictive relevance with a significant impact size. In comparison, the Q2 predict predictive value for Employee Engagement (EENG) is 0.185, indicating good predictive relevance with a modest impact size. These Q2Predict values are higher than the required minimum threshold of 0.00, indicating that the model is highly predictive for each variable. Furthermore, the Q2 effect sizes for EENG and EPC show that they have a significant impact on the endogenous variable (WPFX). SmartPLS 3.3.9 used the PLS algorithm as well as the bootstrapping approach to evaluate the presented hypothesis. Table 6 shows the beta values that demonstrate the intensity and significance of the positive link between the dependent and independent variables, where employee engagement acts as both a moderator and mediator. By including both mediation and moderation in the same model, you can gain a more comprehensive understanding of how workplace flexibility impacts employee performance, considering the mediating role of employee engagement and the moderating effect of its level. In addition to the above analysis, In addition, demographic factors are tested by Manova.

A Two-Step Q-Matrix Estimation Method.

Cognitive Diagnosis Models in educational measurement are restricted latent class models that describe ability in a knowledge domain as a composite of latent skills an examinee may have mastered or failed. Different combinations of skills define distinct latent proficiency classes to which examinees are assigned based on test performance. Items of cognitively diagnostic assessments are characterized by skill profiles specifying which skills are required for a correct item response. The item-skill profiles of a test form its Q-matrix. The validity of cognitive diagnosis depends crucially on the correct specification of the Q-matrix. Typically, Q-matrices are determined by curricular experts. However, expert judgment is fallible. Data-driven estimation methods have been developed with the promise of greater accuracy in identifying the Q-matrix of a test. Yet, many of the extant methods encounter computational feasibility issues either in the form of excessive amounts of CPU times or inadmissible estimates. In this article, a two-step algorithm for estimating the Q-matrix is proposed that can be used with any cognitive diagnosis model. Simulations showed that the new method outperformed extant estimation algorithms and was computationally more efficient. It was also applied to Tatsuoka's famous fraction-subtraction data. The paper concludes with a discussion of theoretical and practical implications of the findings.

Measurement and Analysis of Surface Temperature Gradients on Magnetron Sputtered Thin Film Growth Studied Using NiCr/NiSi Thin Film Thermocouples.

In the general analysis of thin-film growth processes, it is often assumed that the temperature of the film growth surface is the same as the temperature of the film growth substrate. However, a temperature gradient exists between the film growth surface and film growth substrate. Using the growth surface of TiO2 thin films as an example, the temperature gradient of the film growth surface is tested and analyzed. A NiCr/NiSi thin-film thermocouple is fabricated using the direct-current pulse magnetron sputtering method. A three-layer NiCr/NiSi thin-film thermocouple temperature measurement system is established to measure the temperature gradient of the film growth surface. The growth surface temperature and substrate temperature of the TiO2 thin films are measured. For a sputtering power density of 0.83W cm- 2, the temperature difference between the first and second layers is 104.79°C, while the temperature difference between the second and third layers is 39.92°C. A standard K-type thermocouple is used to measure the substrate temperature, which is recorded to be 132.05°C, consistent with common measurements of substrate temperature. The heat conduction on the film growth surface in the vacuum chamber is examined and a model for the temperature measurement device during film growth is constructed.

MCMC stopping rules in latent variable modelling.

Bayesian analysis relies heavily on the Markov chain Monte Carlo (MCMC) algorithm to obtain random samples from posterior distributions. In this study, we compare the performance of MCMC stopping rules and provide a guideline for determining the termination point of the MCMC algorithm in latent variable models. In simulation studies, we examine the performance of four different MCMC stopping rules: potential scale reduction factor (PSRF), fixed-width stopping rule, Geweke's diagnostic, and effective sample size. Specifically, we evaluate these stopping rules in the context of the DINA model and the bifactor item response theory model, two commonly used latent variable models in educational and psychological measurement. Our simulation study findings suggest that single-chain approaches outperform multiple-chain approaches in terms of item parameter accuracy. However, when it comes to person parameter estimates, the effect of stopping rules diminishes. We caution against relying solely on the univariate PSRF, which is the most popular method, as it may terminate the algorithm prematurely and produce biased item parameter estimates if the cut-off value is not chosen carefully. Our research offers guidance to practitioners on choosing suitable stopping rules to improve the precision of the MCMC algorithm in models involving latent variables.

Moderating Effect Of Profitability In Disclosure Of Corporate Social Responsibility, Leverage, And Firm Size: Effect On Share Price

This research aims to examine the effect of Corporate Social Responsibility (CSR), leverage, and firm size on share prices in companies listed on the LQ45 index of the Indonesia Stock Exchange (IDX), with profitability as a moderating variable. This research uses a quantitative approach with a sample size of 20 companies over 7 years, namely 140 research samples obtained using a purposive sampling technique. This research uses secondary data collected through documentation method. The data analysis technique used is Partial Least Square Structural Equation Modeling (PLS-SEM) using SmartPLS 3.0 software. The test stages using PLS-SEM are divided into 3 stages, namely measurement model analysis (outer model), structural model analysis (inner model), and hypothesis testing. The results of the analysis in this study indicate that CSR disclosure, leverage, firm size and profitability influence the share prices of LQ45 index companies. Apart from that, profitability is unable to moderate the influence of CSR and leverage on the share prices of LQ45 index companies. However, profitability is able to moderate the influence of firm size on the share prices of LQ45 index companies.

A unified EM framework for estimation and inference of normal ogive item response models.

Normal ogive (NO) models have contributed substantially to the advancement of item response theory (IRT) and have become popular educational and psychological measurement models. However, estimating NO models remains computationally challenging. The purpose of this paper is to propose an efficient and reliable computational method for fitting NO models. Specifically, we introduce a novel and unified expectation-maximization (EM) algorithm for estimating NO models, including two-parameter, three-parameter, and four-parameter NO models. A key improvement in our EM algorithm lies in augmenting the NO model to be a complete data model within the exponential family, thereby substantially streamlining the implementation of the EM iteration and avoiding the numerical optimization computation in the M-step. Additionally, we propose a two-step expectation procedure for implementing the E-step, which reduces the dimensionality of the integration and effectively enables numerical integration. Moreover, we develop a computing procedure for estimating the standard errors (SEs) of the estimated parameters. Simulation results demonstrate the superior performance of our algorithm in terms of its recovery accuracy, robustness, and computational efficiency. To further validate our methods, we apply them to real data from the Programme for International Student Assessment (PISA). The results affirm the reliability of the parameter estimates obtained using our method.

QUALIMETRIC MODEL OF A HIGH-TECH INFORMATION AND MEASURING SYSTEM FOR THE PREPARATION AND CONDUCT OF TESTS

The issue of the report concerns the selection of information and testing equipment or a system that will be used for planning and conducting tests with minimal error and high efficiency. The complexity of consideration as a whole, when choosing the components of the information and measurement system and determining the qualitative measurement model determines the need to divide it into several stages and consider them separately. The stages of building a qualitative model of a high-tech information and measurement system for preparing and conducting tests are divided into: modeling stage; design stage; stage of implementation; interpretation stage. At the stage of determining the limitations, it should be taken into account in which division the tests are carried out, namely: by purpose; by the level of testing; by stages of product development; by testing finished products; according to the conditions and venue; by the duration of the tests; by type of action; by the result of the action; according to the characteristics of the object. The relevance of the work is the consideration of the stages of building a qualitative model of the information and measurement system in the difficult conditions of the military and political situation in Ukraine. The purpose of the work is to develop the technological aspects of qualitative modeling, as well as the stages of qualitative model examination. Additional results of the work can be – to obtain initial data (parameters); – to break down important indicators (parameters) according to their importance using an expert method; – to assess state and foreign requirements for the system for further work; – to assess and choose a direction based on the price indicator (factor) and duration of work or other indicators; – provide recommendations for making a decision regarding the construction of a qualitative model of the properties of the information and measurement system;–allow to improve (apply) the process of conducting tests of high-tech weapons and military equipment; – allow to use qualitative model databases to reduce preparation time to conducting tests of high-tech weapons and military equipment;–will allow to expand the directions for finding new features and providing recommendations in the further process of tests. The article builds a qualitative model (hereafter KM) of the formation (creation) of KM stages of the information and measurement system (hereafter IMS) at the preparatory stage of OVT tests. Stages of KM examination have been developed based on the current purpose of the tests, the required quantity and quality of recorded and measured indicators, the complexity of measurements and their analysis. Formulated KM efficiency indicators are as follows: minimal error; cost of equipment; equipment resource; others. The stages of selection of IBS components have been developed and the KM of assessment at the preparatory stage of tests has been determined. A model of quantitative quality measurement has been developed, taking into account possible errors and the provided, structural, technological aspects and stages of qualitative modeling. The most expedient tool for a systematic approach to decision-making during the construction of the IBS CM has been determined. The results of a similar review of literary sources of domestic and foreign scientists are presented. However, the insufficient development of the scientific basis for creating KM efficiency indicators is noted. The normative and real vectors of the creation of the CM are determined, which are laid down as important indicators of the effectiveness of the CM, by changing which it is possible to change the direction of the selection of the creation of the CM of the IAS.

Advanced unconditional signal processing model for cross-section contour reconstruction using multi-channel measurements

In mechanical engineering the standard method to assess the geometric tolerances of rotational parts is by analysing parts’ rotational motion in relation to the measuring system. Traditional tolerance compliance measurement models are conditional signal processing models that require pre-defined contour parameters of measured sections. However, due to part diversity and complexity, pre-determining and pre-setting the section parameters before each measurement process is very time-consuming and, in some cases, unachievable. To address this challenge, this paper proposes an advanced unconditional signal processing model, which uses multi-channel measurements for cross-section contour reconstruction that operates without predefined section parameters. This model can handle multi-point measurement signals and accurately estimate the contour shape and engineering center coordinates of measured sections through circumferential Fourier expansion and an approximated signal-contour transform matrix. An efficient iterative algorithm then reconstructs the contour and precisely locates the engineering center. The computational accuracy and robustness of the proposed model have been confirmed through rigorous theoretical analysis and comprehensive experimental validation. Due to its inherent unconditional advantage, the proposed signal processing model can achieve intelligent monitoring of rotational parts throughout their entire life, which not only adapts and remains stable across a wide variety of cross-section types but also significantly improves measurement efficiency, ensuring precise and accurate results.

Exploring the impact of generative AI technologies on education: Academic expert perspectives, trends, and implications for sustainable development goals

This study provides empirical data on the impact of generative AI in education, with special emphasis on sustainable development goals (SDGs). By conducting a thorough analysis of the relationship between generative AI technologies and educational outcomes, this research fills a critical gap in the literature. The insights offered are valuable for policymakers seeking to leverage new educational technologies to support sustainable development. Using Smart-PLS4, five hypotheses derived from the research questions were tested based on data collected from an E-Questionnaire distributed to academic faculty members and education managers. Of the 311 valid responses, the measurement model assessment confirmed the validity and reliability of the data, while the structural model assessment validated the hypotheses. The study’s findings reveal that New Approaches to Learning Outcome Assessment (NALOA) significantly contribute to achieving SDGs, with a path coefficient of 0.477 (p < 0.001). Similarly, the Use of Generative AI Technologies (UGAIT) has a notable positive impact on SDGs, with a value of 0.221 (p < 0.001). A Paradigm Shift in Education and Educational Process Organization (PSEPQ) also demonstrates a significant, though smaller, effect on SDGs with a coefficient of 0.142 (p = 0.008). However, the Opportunities and Risks of Generative AI in Education (ORGIE) study did not find statistically significant evidence of an impact on SDGs (p = 0.390). These findings highlight the potential opportunities and challenges of using generative AI technologies in education and underscore their key role in advancing sustainable development goals. The study also offers a strategic roadmap for educational institutions, particularly in Oman to harness AI technology in support of sustainable development objectives.

A novel integrated soft sensing model for online cement clinker quality monitoring based on fuzzy fine-grained classification

The content of free calcium oxide (f-CaO) in cement clinker is an important index for cement quality. For the problems of multiple working conditions and unbalanced distribution of sample labels in cement clinker production, a soft sensing method of cement clinker f-CaO content based on fuzzy fine-grained classification (FF) is proposed. First, a divide-and-conquer strategy is used to divide the samples into high, medium, and low subsets according to cement clinker f-CaO and extract the fine-grained features under diverse types of multiple production conditions. Second, fuzzy classification based on the membership function is used in the FF model to solve the uncertainty of the sample categories. To ensure the rationality of the classification, the fuzzy membership rule is combined with a convolutional neural network to implement the fuzzy classification method. Finally, different feature extraction methods are proposed to be selected according to the data size of various categories of samples. After experimental validation, the evaluation metrics of RMSE decreased by 4.5 % and R2increased by 17.8 % for the direct classification model compared to the single model. The RMSE of the fuzzy classification model over the direct classification model was again reduced by 2.34 % and R2was again improved by 7.24 %, showing the effectiveness of the proposed soft measurement model.

Development and validation of an instrument to evaluate the perspective of using the electronic health record in a hospital setting

BackgroundEvaluating healthcare information systems, such as the Electronic Health Records (EHR), is both challenging and essential, especially in resource-limited countries. This study aims to psychometrically develop and validate an instrument (questionnaire) to assess the factors influencing the successful adoption of the EHR system by healthcare professionals in Moroccan university hospitals.MethodsThe questionnaire validation process occurred in two main stages. Initially, data collected from a pilot sample of 164 participants underwent analysis using exploratory factor analysis (EFA) to evaluate the validity and reliability of the retained factor structure. Subsequently, the validity of the overall measurement model was confirmed using confirmatory factor analysis (CFA) in a sample of 368 healthcare professionals.ResultsThe structure of the modified HOT-fit model, comprising seven constructs (System Quality, Information Quality, Information technology Service Quality, User Satisfaction, Organization, Environment, and Clinical Performance), was confirmed through confirmatory factor analysis. Absolute, incremental, and parsimonious fit indices all indicated an appropriate level of acceptability, affirming the robustness of the measurement model. Additionally, the instrument demonstrated adequate reliability and convergent validity, with composite reliability values ranging from 0.75 to 0.89 and average variance extracted (AVE) values ranging from 0.51 to 0.63. Furthermore, the square roots of AVE values exceeded the correlations between different pairs of constructs, and the heterotrait-monotrait ratio of correlations (HTMT) was below 0.85, confirming suitable discriminant validity.ConclusionsThe resulting instrument, due to its rigorous development and validation process, can serve as a reliable and valid tool for assessing the success of information technologies in similar contexts.

Exploring Determinants of Sustained Participation in New Sports: The Impact of YouTube Engagement and Educator Support.

This study explored the determinants of sustained participation in new sports by applying the Theory of Planned Behavior (TPB) and examining the moderating role of social support, as well as the influence of media interest. This present study aimed to contribute to the promotion and effective adoption of new sports by providing valuable data for new-sports educators to teach these activities. A survey was conducted among 313 university students enrolled in new-sports classes across five universities in Korea, utilizing a convenience sampling method. Using SPSS 26.0 and AMOS 26.0, we conducted frequency, correlation, and reliability analyses, followed by confirmatory factor analysis (CFA) to validate the measurement model and structural equation modeling (SEM) to examine the relationships between media interest, attitudes, subjective norms, perceived behavioral control (PBC), and behavioral intentions. The software Jamovi version 2.3.21 was employed to analyze the moderating effects of social support from educators on these relationships. The findings reveal that media interest significantly influenced the attitudes, which, in turn, along with subjective norms and PBC, affected the behavioral intentions. Moreover, the social support from educators moderated the effects of attitudes, subjective norms, and PBC on the behavioral intentions. This study concluded that leveraging media interest and enhancing social support can effectively promote engagement and sustained participation in new sports. These insights can offer practical strategies for stakeholders aiming to increase participation rates in new-sports activities.

Measuring the mediating role of quality education for ensuring employability skills: An analysis of higher education student perception in Bangladesh.

The present study employs a quantitative approach to measure the student perception of the direct and mediating effect of quality education for ensuring employability skills in higher education students of Bangladesh. The study was conducted on 154 undergraduate and graduate students at a Private University in Bangladesh, through a cross-sectional survey using a structured data questionnaire. The multilevel measurement and structural model, which was based on the constructs of quality education, employability skills of students, course structure, institutional policy, and physical aspects, was analysed using Partial Least Squares modelling with SmartPLS 4. The aim was to identify the employability skills that are present among higher education students in Bangladesh through student perception and explore the mediating role of quality education in shaping these skills. The findings suggest a significant gap between the skills taught in private universities, and the industry requirements of Bangladesh, which highlights the urgency for administrators and policy-makers to act fast. physical aspects have a positive influence on quality education and employability skills, but course structure and policy show less direct impact. Moreover, quality education is a crucial mediator for only the factors that match a direct effect. This proves that higher education students in Bangladesh may not have acquired the technical knowledge required by the industry. However, the present study was conducted on undergraduate and graduate students at a single private university, thus acknowledging the need to diversify the population sample in future studies for enhanced generalizability. The implications of these results extend to educational policymakers, institutions, and stakeholders, therefore emphasizing the need for curriculum enhancement, industry-academia collaboration, and policy reforms.

Effect of FinTech Adoption, Green Finance and Green Innovation on Sustainability Performance of Nepalese Financial Institutions

This study is aimed to investigate the impact of FinTech adoption on the sustainability performance of financial institutions of Nepal focusing the mediating role of green finance and green innovation. Employing a descriptive and causal comparative research design, the research systematically describes the characteristics of FinTech adoption among customers of financial institutions of the selected area and explores potential cause-and-effect relationships between FinTech adoption and sustainability performance. Data were collected from 180 respondents through a structured questionnaire distributed via an online survey platform, utilizing a 5-point Likert scale. Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM) were used to test the measurement model and examine the relationships between the constructs. The study confirmed that FinTech adoption positively impacts sustainability performance and green finance, although it negatively influences green innovation. Mediation analysis revealed that neither green finance nor green innovation significantly mediate the relationship between FinTech adoption and sustainability performance. The findings underscore the necessity for financial institutions to integrate both technological advancements and sustainable practices comprehensively. This study contributes to the academic literature by providing empirical evidence from Nepal and offers practical insights for policymakers and financial institutions aiming to enhance sustainability through fintech. By addressing critical research gaps, this study advances our understanding of how FinTech can be effectively aligned with sustainable financial practices in Nepal, ensuring long-term environmental, social, and economic benefits.

Methodological Validation of the Cooperative Social Responsibility Measurement Instrument

This research is part of the project "Corporate Social Responsibility in Ecuadorian savings and credit cooperatives. A multi-case analysis and proposal for the development of a cooperative education program" and examines the synergy between cooperative principles, the Sustainable Development Goals (SDGs) and the dimensions proposed for a Cooperative Social Responsibility (CSR) measurement model in Ecuador. It explores how these dimensions converge to promote ethical, sustainable and socially responsible practices, aligned with current regulatory compliance. CSR has become increasingly important in cooperatives, becoming a fundamental practice that encompasses cooperative governance, economic, social, environmental, ethical and technological dimensions. This study describes the methodological process for the validation of a CSR instrument in cooperatives, reviewed and validated by experts from Spain, Ecuador, Guatemala, Argentina and Cuba, using the Delphi method to reach a consensus on the relevance and clarity of the questionnaire items. This process ensures that the instrument is suitable for assessing CSR practices in credit unions, providing a reliable tool for the continuous improvement of their operations.

Assessing Jealousy: Factor Analyses, Measurement Invariance, Nomological Validity, and Longitudinal APIM Analyses of the Multidimensional Jealousy Scale.

The Multidimensional Jealousy Scale is the standard instrument to assess cognitive, emotional, and behavioral jealousy. We examined competing factor models and external correlations with broad and narrow traits. Across two studies, we analyzed four samples (Ntotal = 2,117). Confirmatory factor analysis supported the measurement model of three correlated factors in comparison to unidimensional, second-order, and bifactor models. Thus, speaking against the use of a total score. Furthermore, we found measurement invariance between romantic partners. We extended the Multidimensional Jealousy Scale (MJS)' nomological net to personality pathology and replicated prior findings of associations with broad and narrow traits. Study 2 examined longitudinal data (5- to 9-month lag) from couples. Actor-Partner Interdependence Model analyses showed that the MJS predicts facets of relationship satisfaction in actors and partners. We discuss potential avenues for revising the MJS (e.g., heteronormative item wordings).

Assessing the psychological pathways of esports events spectators: an application of service quality and its antecedents and consequences

ABSTRACT Research question This research develops and tests a comprehensive structural model to explain the psychological pathway behind esports spectator behaviours, linking perceived service quality to its antecedents (i.e. extrinsic motivation and intrinsic motivation) and consequences (i.e. satisfaction and revisit intention). Research methods Two rounds of data collection were completed for Phase One (N = 485) and Phase Two (N = 217). The measurement items were borrowed from a range of pre-existing scales of traditional sport or esports. Phase One was designed to assess the psychometric properties of the measurement model. Phase Two was purported to test the pathways among the constructs as hypothesised in the structural model. Results and findings In Phase One, the model fit indices showed good model fit, reliability and construct validity. In Phase Two, the SEM model fitness indices showed a marginally acceptable model fit. The path analysis supported 15 out of 19 hypotheses; no significant relationships were found between relatedness and intrinsic motivation, autonomy and extrinsic motivation, intrinsic motivation and physical environment, and intrinsic motivation and interaction. Overall, the structural model showed a moderate to substantial predictive power. Implications The study provides empirical evidence for the proposed relationships among the constructs and expands the realm of the current theories to the context of esports, especially the application of the self-determination theory and the new esports service quality model. It also provides a more comprehensive picture for esports practitioners to understand their spectators and balance their effort in managing elements which promote future attendance.

Dy3+-doped niobate phosphor based on charge transfer band red-shift effect: Luminescence thermal stability and temperature sensing performance

The thermal quenching behavior seriously limits the application prospect of phosphors, so it is urgent to improve the thermal quenching behavior of phosphors. It is expected that the red-shift of charge transfer band (CTB) caused by temperature can be used to improve the luminescent stability of phosphor. Therefore, we designed a family of LuNbO4:x%Dy3 (x = 0.25, 1, 2, 5) phosphors based on the characteristic of the red-shift of CTB edge, and investigated the relationship between the red-shift of the CTB edge and the luminescence thermal stability of the phosphor, further broadening the application of this phosphor in temperature sensing. The effects of three different excitation positions, CTB (260 nm), CTB edge red-shift (288 nm) and Dy3+ 4f-4f transition (354 nm), on the luminescence thermal stability of phosphors were also investigated. It was found that only excitation in the red-shift range of CTB edge showed complete antithermal quenching behavior. The LuNbO4:2 %Dy3+ phosphor showed complete antithermal quenching under the excitation of CTB edge, and the comprehensive emission intensity at 573 K is 741 % of that at 298 K. According to the above characteristics, a dual-mode optical thermometry based on fluorescence intensity ratio (FIR) and International Commission on Illumination (CIE) chromaticity coordinates is proposed, in which the maximum relative temperature sensitivity of LuNbO4:x%Dy3+ is 5.04 % K−1 (298 K, FIR) and 3.14 % K−1 (573 K, CIE). Interestingly, when constructing CIE temperature measurement model, we found that LuNbO4:0.25 %Dy3+ phosphor at 260 nm excitation position has obvious color change at each temperature stage, showing temperature visualization characteristics. At 354 nm excitation position, it shows constant white light emission. The excellent optical temperature measurement performance and temperature visualization characteristics of the phosphor shows that it has excellent application potential in optical sensing.