Screening Model Research Articles

The Effect of Conflict Management Strategies on Job Satisfaction: A Study in the Accounting Sector

The aim of this study is to examine the effect of conflict management on job satisfaction for the accounting sectorand to examine the effect between the conflict management strategies used by employees and their job satisfaction. Also, demographic characteristics are to contribute to the related literature by examining the effect of job satisfaction, conflict management, and conflict management strategies. In this study, survey research method and relational screening model were used. Then, e-survey was applied to 451 accounting professionals through convenience sampling among approximately 200.000 accounting sector employees operating in Turkey and 403 questionnaires were analysed. The factor loadings of the scales were examined, followed by reliability and normal distribution analyses of the relevant scales and then parametric tests were performed. Pearson correlation analysis and regression analysis were performed to examine the relationship between constructive, adaptive, coercive, avoidant and compromising subscales of job satisfaction scale and conflict management strategy scale. According to the results, conflict management has a positive effect on job satisfaction. Also, constructive, accommodative, compromise, avoidant and coercive conflict management strategies were also found to have a positive effect on job satisfaction. When demographic characteristics were analysed, it was found that there was no significant difference between gender, marital status, age, education level variables and job satisfaction, conflict management, adaptive, constructive, coercive, avoidant and accommodative conflict management strategies. It was observed that there was a significant difference between the professional experience variable and compatible conflict management strategy, also between the occupation variable and coercive conflict management strategy.

Anatomically and Physiologically Accurate Engineered Neurovascular Unit and Blood-Brain Barrier Model Using Microvessels Isolated from Postmortem Human Brain Tissue.

Brain vasculature is a complex and heterogeneous physiological structure that serves specialized roles in maintaining brain health and homeostasis. There is substantial interest in developing representative human models of the brain vasculature for drug screening and disease modeling applications. Many contemporary strategies have focused on culturing neurovascular cell types in hydrogels and microdevices, but it remains challenging to achieve anatomically relevant vascular structures that have physiologically similar function to their in vivo counterparts. Here, we present a strategy for isolating microvessels from cryopreserved human cortical tissue and culturing these vessels in a biomimetic gelatin-based hydrogel contained in a microfluidic device. We provide histological evidence of arteriole and capillary architectures within hydrogels, as well as anastomosis to the hydrogel edges allowing lumen perfusion. In capillaries, we demonstrate restricted diffusion of a 10 kDa dextran, indicating intact passive blood-brain barrier function. We anticipate this bona fide human brain vasculature-on-a-chip will be useful for various biotechnology applications.

Working towards health: A model of cervical cancer screening and treatment for factory employees in Haiti.

In Haiti, cervical cancer continues to cause high levels of mortality and morbidity due to lack of resources and political unrest. Haitian women employed in factories are especially vulnerable because they are unable to take time away from work to access health resources. We aimed to describe a low-cost intervention which successfully addressed this need. We present a retrospective review of data gathered through a public-private partnership, in which women working in garment factories near Port-au-Prince, Haiti, were offered health education, clinical breast exam, and free human papillomavirus (HPV) self-swab testing at their place of employment. Women testing positive for HPV were subsequently tested using visual inspection with acetic acid (VIA) to inform treatment referrals, and treated with mobile thermocoagulation in factory infirmaries. Factory-employed healthcare workers were trained on cancer screening, including VIA and clinical breast exam. A total of 6843 out of 6983 (98%) female factory employees attended free reproductive health education sessions, and 4005 out of 4153 eligible women (97%) were screened using HPV self-swab testing; 5176 women received a clinical breast exam. Of the women screened for HPV, 1001 (25%) tested positive and 905 (90%) of HPV-positive women received VIA testing and thermocoagulation. The intervention had a total cost of US$76 000, over half of which was spent on an HPV testing machine. Innovative approaches to the prevention of cervical cancer are especially necessary in very low-resource, politically unstable environments like Haiti. Self-swab and screen-and-treat programs in the workplace were acceptable to employees and factory owners. This low-cost model was reached vulnerable women through a public-private partnership, and tracked them through screening and treatment. It could be implemented elsewhere or extended to include other health services.

Clinical strategy study on prenatal screening and diagnostic model for Down syndrome

Exploring efficient and easily implementable prenatal screening strategies aims at birth defect prevention and control. However, there have been limited economic evaluations of non-invasive prenatal screening (NIPS) strategies in China. Furthermore, these studies were predominantly confined to local or geographically proximate provinces and lacked universality and representativeness. This study assesses the health economics of current prenatal screening strategies and NIPS as first-line screening programs, analyzing their efficacy to determine an optimal strategy. From the perspective of health economics, cost-effectiveness, cost-benefit, and single-factor sensitivity were conducted for five different screening strategies using a decision tree model. Among pregnant women aged < 35 years who underwent only one screening for foetal Down syndrome (DS), the detection rate, false positive rate and positive predictive value of NIPS for foetuses with DS were superior to those of the other four serological screening methods. Although applying NIPS as first-line screening method yields the highest efficacy and benefits, it currently lacks cost-effectiveness when compared to serological screening and sequential NIPS screening strategies.

Elementomics of 32 elements in cord serum depicts the risk of orofacial clefts: A case-control study in Shanxi, China

Maternal exposure to various metallic and non-metallic elements has been linked to the occurrence of orofacial clefts (OFCs), yet there remains a dearth of comprehensive research on the potential ramifications of simultaneous exposure to multiple elements. In this study, we investigated the individual and combined effects of element exposure on OFCs in a cohort of 168 pregnant women (49 cases and 119 controls) in the Shanxi province of northern China from 2010 to 2015. Cord serum samples were obtained from all participants to analyze the levels of 32 elements using inductively coupled plasma-mass spectrometry. The study examined the independent correlation between element concentrations and OFCs using two machine screening models, Boruta and Least Absolute Shrinkage and Selection Operator. Bayesian kernel machine regression (BKMR) was utilized to determine the combined effects of key exposure elements on OFCs and to clarify the interaction between exposed elements through the generalized additive model (GAM). The screening models identified lead (Pb), tin (Sn), iron (Fe), and cesium (Cs) as the most significant risk factors for OFC development in offspring. In the BKMR model, the probability of OFCs increased with higher overall levels of these risk elements, with Pb emerging as the primary contributor to the combined effect of the mixture. The findings of the GAM indicated that the combined exposure to Pb and Sn had a synergistic effect on the risk of developing OFCs. Analysis of elemental exposure in umbilical cord serum suggested that Pb exposure may have detrimental effects on OFC development in offspring, which may be further intensified by a synergistic interaction between Sn and Pb in the occurrence of OFCs.

Dose-Dependent Induction of Differential Seizure Phenotypes by Pilocarpine in Rats: Considerations for Translational Potential.

Background and Objectives: Pilocarpine is used in experimental studies for testing antiepileptic drugs, but further characterization of this model is essential for its usage in testing novel drugs. The aim of our study was to study the behavioral and EEG characteristics of acute seizures caused by different doses of pilocarpine in rats. Materials and Methods: Male Wistar rats were treated with a single intraperitoneal dose of 100 mg/kg (P100), 200 mg/kg (P200), or 300 mg/kg (P300) of pilocarpine, and epileptiform behavior and EEG changes followed within 4 h. Results: The intensity and the duration of seizures were significantly higher in P300 vs. the P200 and P100 groups, with status epilepticus dominating in P300 and self-limiting tonic-clonic seizures in the P200 group. The seizure grade was significantly higher in P200 vs. the P100 group only during the first hour after pilocarpine application. The latency of seizures was significantly shorter in P300 and P200 compared with P100 group. Conclusions: Pilocarpine (200 mg/kg) can be used as a suitable model for the initial screening of potential anti-seizure medications, while at a dose of 300 mg/kg, it can be used for study of the mechanisms of epileptogenesis.

A randomised controlled implementation study integrating patient self-screening with a remote central monitoring system to screen community-dwellers aged 75 years and older for atrial fibrillation.

Diagnosis of atrial fibrillation (AF) provides opportunities to reduce stroke risk. This study aimed to compare AF diagnosis rates, participant satisfaction and feasibility of an electrocardiogram (ECG) self-screening virtual care system with usual care. This randomised controlled implementation study involving community-dwelling people aged ≥75 years was conducted from May 2021 to June 2023. Participants were given a handheld single-lead ECG device and trained to self-record ECGs once daily on weekdays for 12 months. The control group received usual care with their general practitioners in the first 6 months and participated in the subsequent 6 months. AF diagnosis and participant satisfaction were assessed at 6 months. 200 participants (mean age 79.0±3.4 years; 54.0% female; 72.5% urban). AF was diagnosed in 10/97 (10.3%) intervention participants and 2/100 (2.0%) in the control group (Odds Ratio 5.6, 95% CI 1.4-37.3, p=0.03). In the intervention, 80% of AF cases were diagnosed within 3 months. 91/93 (97.9%) intervention participants and 55/93 (59.1%) control-waitlisted participants (p<0.001) were satisfied with AF screening. Of the expected 20 days per month, the overall monthly median number of days participants self-recorded ECGs was 20 (interquartile range 17-22). Participants were confident using the device (93%), reported it was easy to use (98%) and found screening efficient (96%). Patient-led AF self-screening using single-lead ECG devices with a remote central monitoring system was feasible, acceptable, and effective in diagnosing AF among older people. This screening model could be adapted for implementation, interfacing with integrated care models within existing health systems.

Exploring the Role of HR Analytics in Enhancing Talent Acquisition Strategies

Hiring and selecting the right candidate is a critical business process in today’s cutthroat competition. HR Analytics, also known as Human Resource Analytics, provides unique insights into talent acquisition and helps organisations to improve their strategies based on fact. This research paper aims to establish the use of the HR Analytics in improving the talent acquisition practices since this will help in identifying the different candidates sourcing, screening, selection, and onboarding processes. In doing so, the work employs the study of different techniques of HR Analytics including but not limited to the predictive analytics, data mining, and talent market analysis with the main view of understanding how different techniques can be used to give the organizations competitive advantage in talent management and attraction. The research will focus on a few real-world solutions such as the use of HR Analytics for social media talent acquisition, the use of prediction models for screening and the use of data analytics for selection. Also, the paper will discuss the problems and drawbacks that may be encountered while adopting HR Analytics in recruiting. In it, we will highlight the data quality issue, the privacy issue, and the need that HR professionals must have skills and knowledge to use the HR Analytics tools. Lastly, this research aims at contributing to the existing body of knowledge that seeks to help organizations that aim at increasing on their Strategic Human Resources Management Talent Acquisition approach based on the design and use of HR Analytics. Day by day organizations are becoming more rational and by using this technique organizations can arrive to better decisions about employer branding and, consequently, about attracting and retaining the best talent in today’s job market.

Colorimetric screening model for identification of menaquinone-7 (MK-7) producing strains.

The online version contains supplementary material available at 10.1007/s13205-024-04097-1.

Investigation of Secondary School Students' Visual Perception and Attitudes towards Graphic Design

The relationship between visual perceptions and graphic design of secondary school students is of great importance in terms of the development of their artistic skills. This link is directly related to how students interpret the visual world and how they express these interpretations through graphic design. The aim of this study is to examine the relationships between visual perception and attitudes of secondary school students towards graphic design in terms of some variables. The research conducted with the comparative relational screening model was conducted on 168 students studying at a private secondary school in Türkiye. The research data were collected using the ‘Visual Perception Test’ and the ‘Attitude Scale Towards Graphic Design’. The data were analyzed using ‘Descriptive Statistics’, ‘Independent Samples t test’ and ‘Multiple Regression Analysis’ techniques. According to the research findings, the visual perceptions of the participating students were found to be above the average level, while their attitudes towards graphic design were found to be at a high level. Students’ visual perceptions and attitudes towards graphic design differ according to the gender variable. Finally, visual perception significantly predicts students’ attitudes towards graphic design.

Exploring the power of data-driven models for groundwater system conceptualization: a case study of the Grazer Feld Aquifer, Austria

AbstractDeveloping a reliable conceptual model is crucial for analyzing groundwater systems. An essential part of the aquifer conceptualization is the identification of the hydrological stresses that control the hydraulic head fluctuations. By effectively capturing and understanding these stresses, the propagation of potential errors and uncertainties through subsequent modeling steps can be minimized. This study aims to test data-driven models as screening models for conceptualizing a groundwater system. The case study is applied to the Grazer Feld Aquifer in southeast Austria. Time series models are applied to: (1) identify the stresses likely influencing the observed head fluctuations and their spatial variability; (2) identify locations where a lack of understanding of head fluctuations exists; and (3) discuss the limitations and opportunities associated with data-driven models to support system conceptualization. Time series models were created for 144 monitoring wells where sufficient head observations were available during the calibration period (2005–2015). A total of 576 models were developed, incorporating the combinations of stresses: recharge, river level, and a step trend. Following the model selection process, each model was categorized based on its performance and divided into four groups. At 88 sites, recharge and river level variations were identified as the primary controlling stresses influencing head fluctuations. The inclusion of the step trend was found to be necessary at five sites to accurately simulate heads due to dam construction. The application of data-driven models in this study enhanced the identification of key aquifer stresses, facilitating a more informed understanding of the groundwater system.

An effective screening model for subjective cognitive decline in community-dwelling older adults based on gait analysis and eye tracking.

To evaluate the effectiveness of multimodal features based on gait analysis and eye tracking for elderly people screening with subjective cognitive decline in the community. In the study, 412 cognitively normal older adults aged over 65 years were included. Among them, 230 individuals were diagnosed with non-subjective cognitive decline and 182 with subjective cognitive decline. All participants underwent assessments using three screening tools: the traditional SCD9 scale, gait analysis, and eye tracking. The gait analysis involved three tasks: the single task, the counting backwards dual task, and the naming animals dual task. Eye tracking included six paradigms: smooth pursuit, median fixation, lateral fixation, overlap saccade, gap saccade, and anti-saccade tasks. Using the XGBoost machine learning algorithm, several models were developed based on gait analysis and eye tracking to classify subjective cognitive decline. A total of 161 gait and eye-tracking features were measured. 22 parameters, including 9 gait and 13 eye-tracking features, showed significant differences between the two groups (p < 0.05). The top three eye-tracking paradigms were anti-saccade, gap saccade, and median fixation, with AUCs of 0.911, 0.904, and 0.891, respectively. The gait analysis features had an AUC of 0.862, indicating better discriminatory efficacy compared to the SCD9 scale, which had an AUC of 0.762. The model based on single and dual task gait, anti-saccade, gap saccade, and median fixation achieved the best efficacy in SCD screening (AUC = 0.969). The gait analysis, eye-tracking multimodal assessment tool is an objective and accurate screening method that showed better detection of subjective cognitive decline. This finding provides another option for early identification of subjective cognitive decline in the community.

Natural Products from Marine-Derived Fungi with Anti-Inflammatory Activity.

Inflammation is considered as one of the most primary protective innate immunity responses, closely related to the body's defense mechanism for responding to chemical, biological infections, or physical injuries. Furthermore, prolonged inflammation is undesirable, playing an important role in the development of various diseases, such as heart disease, diabetes, Alzheimer's disease, atherosclerosis, rheumatoid arthritis, and even certain cancers. Marine-derived fungi represent promising sources of structurally novel bioactive natural products, and have been a focus of research for the development of anti-inflammatory drugs. This review covers secondary metabolites with anti-inflammatory activities from marine-derived fungi, over the period spanning August 2018 to July 2024. A total of 285 anti-inflammatory metabolites, including 156 novel compounds and 11 with novel skeleton structures, are described. Their structures are categorized into five categories: terpenoids, polyketides, nitrogen-containing compounds, steroids, and other classes. The biological targets, as well as the in vitro and in vivo screening models, were surveyed and statistically summarized. This paper aims to offer valuable insights to researchers in the exploration of natural products and the discovery of anti-inflammatory drugs.

Reliability of AlphaFold2 Models in Virtual Drug Screening: A Focus on Selected Class A GPCRs.

Protein three-dimensional (3D) structure prediction is one of the most challenging issues in the field of computational biochemistry, which has overwhelmed scientists for almost half a century. A significant breakthrough in structural biology has been established by developing the artificial intelligence (AI) system AlphaFold2 (AF2). The AF2 system provides a state-of-the-art prediction of protein structures from nearly all known protein sequences with high accuracy. This study examined the reliability of AF2 models compared to the experimental structures in drug discovery, focusing on one of the most common protein drug-targeted classes known as G protein-coupled receptors (GPCRs) class A. A total of 32 representative protein targets were selected, including experimental structures of X-ray crystallographic and Cryo-EM structures and their corresponding AF2 models. The quality of AF2 models was assessed using different structure validation tools, including the pLDDT score, RMSD value, MolProbity score, percentage of Ramachandran favored, QMEAN Z-score, and QMEANDisCo Global. The molecular docking was performed using the Genetic Optimization for Ligand Docking (GOLD) software. The AF2 models' reliability in virtual drug screening was determined by their ability to predict the ligand binding poses closest to the native binding pose by assessing the Root Mean Square Deviation (RMSD) metric and docking scoring function. The quality of the docking and scoring function was evaluated using the enrichment factor (EF). Furthermore, the capability of using AF2 models in molecular docking to identify hits with key protein-ligand interactions was analyzed. The posing power results showed that the AF2 models successfully predicted ligand binding poses (RMSD < 2 Å). However, they exhibited lower screening power, with average EF values of 2.24, 2.42, and 1.82 for X-ray, Cryo-EM, and AF2 structures, respectively. Moreover, our study revealed that molecular docking using AF2 models can identify competitive inhibitors. In conclusion, this study found that AF2 models provided docking results comparable to experimental structures, particularly for certain GPCR targets, and could potentially significantly impact drug discovery.

The Mediating Effect of Rumination in the Relationship between Secondary Traumatic Stress and Self-Compassion in Psychosocial Support Providers

The aim of this study is to determine whether rumination has a mediating role in the relationship between secondary traumatic stress and self-compassion levels of professionals who provide psychosocial assistance to survivors after the earthquake in Turkey on 6 February 2023. The research was organized in accordance with the relational screening model. Within the scope of Turkey Disaster Response Plan, 153 professionals including social workers, psychologists and psychological counselors who provide psychosocial assistance to earthquake survivors participated in the study. The research data were collected through an online survey link using the Secondary Traumatic Stress Scale, Self-Compassion Scale, Ruminative Reaction Scale and Personal Information Form. In the analysis of the data; descriptive statistics, Pearson correlation analysis and PROCESS Macro (Model 4) were used in the mediation test. According to the results of the study, the direct (β = −0.204) and indirect (β = −0.175) effects of self-compassion on secondary traumatic stress are statistically significant and negative. Therefore, it was determined that rumination played a significant mediating role in this interaction. Accordingly, it is understood that increasing self-compassion and regulating rumination reactions are important in reducing the secondary traumatic stress levels of professionals providing psychosocial assistance. There is a need for more research into the factors that influence the secondary traumatic stress of mental health workers who intervene with traumatized people.

Extraction of phenolic acids and tetramethylpyrazine in Shanxi aged vinegar base on vortex-assisted liquid-liquid microextraction-hydrophobic deep eutectic solvent: COSMO-RS calculations and ANN-GA optimization

In this study, vortex-assisted liquid-liquid microextraction (VA-LLME) based on hydrophobic deep eutectic solvents (HDES) was used to efficiently and sustainably extract five phenolic acids and tetramethylpyrazine (TMP) from Shanxi aged vinegar (SAV). The VA-LLME technique was employed to investigate the extraction mechanism of HDES with the best extraction performance for the target compounds using a conductor-like screening model for real solvents (COSMO-RS). An artificial neural network combined with a genetic algorithm (ANN-GA) was developed to optimize the extraction conditions based on single-factor and response surface methodology, while also analyzing the interactive effects on the phenolic acids and TMP in the extracted solution during the extraction phase. The optimized conditions were determined, and the greenness of the procedure was evaluated using an analytical greenness metric, indicating that this technique can serve as a green alternative for the determination of phenolic acids and TMP in SAV.

Development and validation of a machine learning-based framework for assessing metabolic-associated fatty liver disease risk

BackgroundThe existing predictive models for metabolic-associated fatty liver disease (MAFLD) possess certain limitations that render them unsuitable for extensive population-wide screening. This study is founded upon population health examination data and employs a comparison of eight distinct machine learning (ML) algorithms to construct the optimal screening model for identifying high-risk individuals with MAFLD in China.MethodsWe collected physical examination data from 5,171,392 adults residing in the northwestern region of China, during the year 2021. Feature selection was conducted through the utilization of the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Additionally, class balancing parameters were incorporated into the models, accompanied by hyperparameter tuning, to effectively address the challenges posed by imbalanced datasets. This study encompassed the development of both tree-based ML models (including Classification and Regression Trees, Random Forest, Adaptive Boosting, Light Gradient Boosting Machine, Extreme Gradient Boosting, and Categorical Boosting) and alternative ML models (specifically, k-Nearest Neighbors and Artificial Neural Network) for the purpose of identifying individuals with MAFLD. Furthermore, we visualized the importance scores of each feature on the selected model.ResultsThe average age (standard deviation) of the 5,171,392 participants was 51.12 (15.00) years, with 52.47% of the participants being females. MAFLD was diagnosed by specialized physicians. 20 variables were finally included for analyses after LASSO regression model. Following ten rounds of cross-validation and parameter optimization for each algorithm, the CatBoost algorithm exhibited the best performance, achieving an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.862. The ranking of feature importance indicates that age, BMI, triglyceride, fasting plasma glucose, waist circumference, occupation, high density lipoprotein cholesterol, low density lipoprotein cholesterol, total cholesterol, systolic blood pressure, diastolic blood pressure, ethnicity and cardiovascular diseases are the top 13 crucial factors for MAFLD screening.ConclusionThis study utilized a large-scale, multi-ethnic physical examination data from the northwestern region of China to establish a more accurate and effective MAFLD risk screening model, offering a new perspective for the prediction and prevention of MAFLD.

Streamlining Phenotype Classification and Highlighting Feature Candidates: A Screening Method for Non-Targeted Ion Mobility Spectrometry-Mass Spectrometry (IMS-MS) Data.

Nontargeted analysis (NTA) is increasingly utilized for its ability to identify key molecular features beyond known targets in complex samples. NTA is particularly advantageous in exploratory studies aimed at identifying phenotype-associated features or molecules able to classify various sample types. However, implementing NTA involves extensive data analyses and labor-intensive annotations. To address these limitations, we developed a rapid data screening capability compatible with NTA data collected on a liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) platform that allows for sample classification while highlighting potential features of interest. Specifically, this method aggregates the thousands of IMS-MS spectra collected across the LC space for each sample and collapses the LC dimension, resulting in a single summed IMS-MS spectrum for screening. The summed IMS-MS spectra are then analyzed with a bootstrapped Lasso technique to identify key regions or coordinates for phenotype classification via support vector machines. Molecular annotations are then performed by examining the features present in the selected coordinates, highlighting potential molecular candidates. To demonstrate this summed IMS-MS screening approach, we applied it to clinical plasma lipidomic NTA data and exposomic NTA data from water sites with varying contaminant levels. Distinguishing coordinates were observed in both studies, enabling the evaluation of phenotypic molecular annotations and resulting in screening models capable of classifying samples with up to a 25% increase in accuracy compared to models using annotated data.

Exploring the impact of microfluidic chip structure on the efficacy of three-dimensional tumor microspheres cultivation.

Three-dimensional (3D) tumor microspheres can simulate the interaction and growth dynamics of tumor cells, and have been used as a new in vitro model for drug screening and tumor biology related research. The scaffold-free culture of 3D tumor microspheres on microfluidic chips has many advantages, including low cost, high throughput, convenience and flexibility. However, it is still unclear how various factors, such as chip structure, influence the culture effect of tumor microspheres. The lack of standardized evaluation and characterization of the culture effect hinders the further optimization and development of chip function. This study presents numerical simulations of multiple parts or processes of the proposed 3D culture chips with two different structural parameters based on computational fluid dynamics (CFD) methods. An evaluation system for tumor microspheres was established. The prediction of the CFD simulation was consistent with the culture results of the chips, reflecting the important role of the structural parameters of the microtrap in the formation of uniform tumor microspheres. Furthermore, the velocity of cell suspension also had a significant impact on the retention of tumor cells. Additionally, the drug screening results of tumor microspheres indicated that tumor microspheres exhibit greater drug resistance, which may be attributed to their size. These results offer valuable insights into the factors that influence the characteristics of tumor microspheres. This research provides a reference and direction for the optimal design and functional evaluation of scaffold-free 3D culture chips, and holds promise for promoting the development of novel drug research platforms.

Exploring the impact of heterocyclic bridges for tuning the amplitudes of Nonlinear optical properties under Gas, IEFPCM and COSMO solvent models

Nonlinear optical (NLO) materials are a distinctive class of materials due to their NLO response and wide range of applications. We present a systematic quantum chemical study for designing triphenylamine based derivatives (1Ph to 3PhDTDZ) by modifying central bridge with various size and types of heterocyclic rings. Density functional theory (DFT) method is used to calculate the NLO properties including linear and third-order NLO polarizabilities (α and γ) by using M06-2X functional coupled with the 6-311G** basis set. The calculation results predict that among the designed compounds, 1PhDTDZ has the highest third-order NLO polarizability of 2347.7 x 10−36 esu and lowest transition energy of 1.828 eV. On the other hand, 3PhDTDZ has a greater amplitude (108.5 x 10−24 esu) of static linear polarizability (αiso). We also computed frequency-dependent third-order NLO polarizability values at various laser wavelengths (1500 nm to 1970 nm). This provides valuable information about the complex interaction between light and matter. Dynamic second hyperpolarizability of electro-optic Pockels effect (EOPE effect, (γ (−ω; ω,0,0)) is calculated at 1500 nm laser wavelength with a larger γ-amplitude of 36708.4 x 10−36 esu. The effect of solvent on α and γ has also been the primary focus of this research. An extensive solvation impact has been studied using integral equation formalism Polarizable Continuum Model (IEFPCM) and Conductor-like Screening Model (COSMO). Magnitudes of αiso and 〈γ〉 have shown variation from gas to solvent methods. While among solvents, amplitudes are seen 3.87 % to 11.29 % greater under the effect of COSMO-methanol than PCM-methanol. The Frontier molecular orbital (FMO) studies found a significant decrease in energy band gaps from 5.75 eV to 2.98 eV. Since our systems exhibit remarkable photovoltaic properties, they can be used as dye-sensitized solar cells (DSSCs). Their open circuit voltage (VOC) ranging from 1 eV to 4 eV. Thus, our designed triphenylamine based derivatives with distinct central heterocyclic bridges showcase remarkable efficiency as NLO materials, with additional benefits of exhibiting good photovoltaic properties.