Empirical Calculations Research Articles

The effect of interface polarity on the basal dislocations at the GaN/AlN interface.

The unavoidable high-density dislocations in GaN usually hinder the normal operation of GaN-based devices. The current theoretical research studies mainly focus on threading dislocations in the bulk GaN crystal. Here, we alternatively turn our attention to the basal dislocations, which have been directly observed in experiments but have been less studied. The results from the density-functional theory and empirical molecular dynamic calculations indicate that the Al-polar GaN/AlN interface is more conductive to the formation of Shockley partial dislocations, which are the main dislocations formed during the early growth of GaN on AlN and the precursor of the threading edge dislocations. The dislocation density and local geometry in GaN deposited on AlN strongly depend on the temperature. Overall, choosing N-polar AlN as a substrate for GaN growth at 1900 K helps to obtain high-quality GaN with a greater wurtzite structure content and fewer dislocations.

КОЛИЧЕСТВЕННАЯ ОЦЕНКА ГЛЯЦИАЛЬНОЙ СЕЛЕВОЙ ОПАСНОСТИ В ИЛЕ АЛАТАУ

The present publication is devoted to studying the problem of quantitatively assessing glacial debris flow hazards. The study area, which includes river basins in the central part of the northern slope of Ile Alatau, is characterized by increased debris flow activity. Currently existing methods of glacial debris flow hazard assessment are based mainly on qualitative criteria. Despite the fact that such assessments were generally based on the results of field surveys and observations, their outcomes were largely determined by subjective factors. The aim of the study is to address the problem of using quantitative criteria in assessing the outburst hazard of moraine-glacial lakes by creating a new methodology for the quantitative assessment of glacial debris flow hazards based on empirical data. The geodetic and bathymetric measurements of lake basins located on modern moraines and directly at glacier tongues served as a basis for this work, allowing us to identify relationships between lake areas and volumes. This paper presents the results of glacial debris flow hazard assessment, largely obtained for the first time using a computational method, for the most studied debris flow-prone areas of Ile Alatau. For the quantitative assessment of glacial debris flow hazard, the results of direct observations of moraine-glacial lake outbursts were used, allowing us to obtain quantitative characteristics and hydrographs of outburst floods. The relationships between the volumes of outburst floods and the maximum discharge of turbulent mudstone flows at the outlet of debris flow centers (incisions), measured by the instrumental method and using empirical calculation formulas, were also identified. These data were obtained during special surveys of the traces of past debris flows

The impact of rainfall value on the accuracy of the SCS-CN model: selection of model parameters

ABSTRACT The soil conservation service curve number (SCS-CN) model is a widely utilized tool for estimating runoff and relies on two empirical parameters: the CN and the ratio of initial abstraction to maximum potential retention (λ). The determination of the parameters is via the empirical method or calculations based on actual data. However, few studies address the effect of rainfall on parameter selection, and collecting runoff data for model analysis is challenging. This study, taking the Nemor River Basin in Northeast China as the research region, investigates how the combination of CN and λ impacts the model in different rainfall conditions. Using runoff plots and reanalysis product data, the study reveals that: (1) the calculated methods outperformed the empirical method, increasing the Nash efficiency coefficient from 0.34 to 0.65. (2) A higher λ value (0.2 compared to 0.02) reduces runoff and smoothes the runoff curve, which becomes less obvious with increasing CN. (3) The CN values exhibit a non-monotonic relationship with rainfall, initially decreasing before rising, highlighting the need to adjust the CN based on rainfall. Moreover, the SCS-CN model's performance with reanalysis data approximates that with actual data, confirming the viability of reanalysis datasets in this region.

Calibration verification for stochastic agent-based disease spread models.

Accurate disease spread modeling is crucial for identifying the severity of outbreaks and planning effective mitigation efforts. To be reliable when applied to new outbreaks, model calibration techniques must be robust. However, current methods frequently forgo calibration verification (a stand-alone process evaluating the calibration procedure) and instead use overall model validation (a process comparing calibrated model results to data) to check calibration processes, which may conceal errors in calibration. In this work, we develop a stochastic agent-based disease spread model to act as a testing environment as we test two calibration methods using simulation-based calibration, which is a synthetic data calibration verification method. The first calibration method is a Bayesian inference approach using an empirically-constructed likelihood and Markov chain Monte Carlo (MCMC) sampling, while the second method is a likelihood-free approach using approximate Bayesian computation (ABC). Simulation-based calibration suggests that there are challenges with the empirical likelihood calculation used in the first calibration method in this context. These issues are alleviated in the ABC approach. Despite these challenges, we note that the first calibration method performs well in a synthetic data model validation test similar to those common in disease spread modeling literature. We conclude that stand-alone calibration verification using synthetic data may benefit epidemiological researchers in identifying model calibration challenges that may be difficult to identify with other commonly used model validation techniques.

Pseudoisotherm: A Revised Concept of Desorption Phenomena of Coalbed Gas Reservoirs Integrating Isotherm, Material Balance, and Pressure for Optimized Field Development

Summary Gas in the coal is found mainly in the form of adsorbed methane on the surface of micropores. Methane desorbs and diffuses from micropores to cleat systems; then, the cleat network delivers the gas to the wellbore. The Langmuir isotherm empirically explains this phenomenon, a plot between pressure and adsorbed gas content (GC). The isotherm is generated in a laboratory, indicating how a particular reservoir will desorb the gas from the coal surface with a reduction in pressure throughout the production stage from startup to abandonment. This study aims to validate the laboratory-derived isotherm after a few years of field production, impacting the revised development plan for infill wells. Assuming that the initially established isotherm is correct, the difference between the initial and current adsorbed gas volume should match closely with the produced gas volume. However, production analysis after 5 years indicates that the calculated produced gas volume is significantly higher than the observed, thus invalidating the fundamental material balance equation. This triggers the question: What is wrong with using the initially established adsorption isotherm in recovery calculations of depleted coal reservoirs, and how can it be corrected? This necessitates revision/review of isotherms to match current pressure and GC/gas in place (GIP = area × current GC × thickness × density). As explained before, for coalbed methane (CBM) reservoirs, GC, cumulative gas production, and current reservoir pressure are direct measurements and require a solid logic to change the parameters. On the other hand, a Langmuir isotherm is a laboratory-derived isotherm from a representative sample of coal with inherent uncertainties from sampling to measurement. Hence, the isotherm shape is modified based on production data analysis and associated material balance results. Considering all these empirical calculations, the laboratory isotherm is revised to satisfy both initial and current reservoir conditions. This solution is validated with hard field data in the form of new core holes drilled after 3 years of production. The pressure and GC measured in those core holes were plotted, which matched the pseudoisotherm, validating the new concept. Instead of using the laboratory-derived core-scale isotherm throughout field life, it is modified with respect to production data analysis and material balance to make it a field-scale isotherm. In the coalbed gas or CBM industry, a new name is coined as “pseudoisotherm” to differentiate it from laboratory-derived isotherm. This method can be used in other reservoirs to optimize field development through infill wells during the production stage and unlock the full potential of coal-gas reservoirs.

Hybrid diagnosis-related groups-The challenge

The introduction of hybrid diagnosis-related groups (DRG) presents new challenges for healthcare providers and health insurances. The same applied in 2023 to the institute designated by the Federal Ministry of Health (BMG) to extract medical procedures and calculate remuneration levels for the first hybrid DRGs. Aresponsible calculation methodology and arealistic data basis are required as the result of the calculation can lead to controversy, even to a splitting among specialist groups and constructs. There is also the threat of mismanagement with subsequent supply problems. In this context, aloss of quality can occur due to the use of simple surgical procedures that are less complex and not expensive with respect to material costs and are economical but not state of the art and thus directly worsen the medical care of patients in the statutory health insurance (GKV). Furthermore, it is already becoming apparent that procedures that are uneconomical due to the miscalculation are partially no longer being comprehensively rendered by healthcare providers due to adjustment of the service portfolio. An appropriate compensation of procedures is only possible based on a remuneration that adequately covers the costs. In this respect, this article is not intended to be understood as a"solution to the problem of the internal distribution of the remuneration in hybrid DRGs" but more to offer suggestions for solutions for the required further development of the hybrid DRG compensation level calculation to prevent a threat to the treatment of GKV patients due to mismanagement. As required in §115fof the SozialgesetzbuchV (SGBV), the recalculation of an economic remuneration must be carried out urgently and promptly using an empirical calculation basis and methodology and this must be regularly adapted.

Comparison of discharge characteristics of Pelton injector estimated by empirical relation, numerical simulation and experiment

Abstract It is difficult to define a general relationship for discharge characteristics of a Pelton injector due to high degree of variability in geometry of injector amongst different Pelton turbine designs. This study has been aimed to determine discharge characteristics of injector of Pelton rig at Waterpower Laboratory, NTNU. Three different approaches have been undertaken, viz. calculation based on empirical relations, numerical simulations, and experimental validation. For empirical calculation, the injector has been simplified into series of flow geometries with equivalent cross-sectional area and axial length for multiple stroke to constant aperture diameter ratio (s/D 0). Discharge coefficient has been calculated for a constant pressure difference between inlet and atmospheric outlet for each case of s/D 0, based on head loss estimated using empirical resistance coefficient (ζ - values) available in engineering handbooks. Discharge has been determined through numerical simulations for same s/D 0 values. Results from numerical and empirical calculations are compared and correction factor (CF) has been determined. It is found that the CF is almost constant, at value of 0.85, for all values of s/D 0. The discharge coefficient calculated by numerical simulation is around 0.02 lower than the values obtained by experimental observation.

Comparative Analysis of Heat Transfer Coefficient Using Experimental Data and Empirical Expressions

This study aims to determine the heat transfer coefficient by comparing results obtained from both experimental methods and empirical expressions. A controlled experiment involving the flow of water through a polyethylene hose was conducted, and the heat transfer coefficient was calculated based on inlet and outlet temperatures. These findings were then compared with values derived from empirical correlations, specifically the Sieder-Tate equation, to assess accuracy and reliability. The experimental setup maintained consistent boundary conditions, including water inlet temperature, airflow rate, and hose orientation. The results indicated that the experimentally determined heat transfer coefficient was 48.30 W/(m²·K), while the empirical calculation yielded a value of 53.44 W/(m²·K). The slight discrepancy between these values highlights minor experimental errors and assumptions inherent in empirical models. Overall, the close agreement between the experimental and empirical values validates the use of empirical correlations for predicting heat transfer coefficients in similar configurations. This study provides valuable insights for optimizing heat transfer processes in various industrial and engineering applications, emphasizing the importance of experimental validation. Future work could explore extended parameter ranges, advanced measurement techniques, and numerical simulations to further enhance the accuracy and applicability of the findings.

Machine learning prediction method for the interface bond strength between fiber reinforced polymer bars and concrete based on multi-feature driven analysis

In severe service conditions, corrosion of reinforcement poses a significant challenge for concrete structures. Fiber reinforced polymer (FRP) materials, characterized by their low weight, high strength, and superior corrosion resistance, are effective in enhancing the durability of these structures. The interfacial bond performance between FRP bars and concrete is crucial for their synergistic action and is pivotal for the design and safety evaluation of FRP-reinforced concrete structures. This paper conducts a comprehensive analysis of factors including the material and mechanical properties of FRP bars, concrete mechanical properties, and bond length. Utilizing an exhaustive search strategy, it identifies the optimal characteristic parameters for assessing the bond strength between FRP bars and concrete. Subsequently, these parameters are integrated with four machine learning (ML) algorithms—DT, SVM, RF and XGB—to train the nonlinear mapping relationship between influencing factors and bond strength, proposing a unified and interpretable prediction method for the FRP bar-concrete interface bond strength. The model's predictive accuracy is then validated using a test dataset, and a comparative analysis is performed among the predictive models generated by the four ML algorithms, as well as against empirical calculation formulas. The findings indicate that the ML models offer superior predictive performance and accuracy over empirical formulas, with the XGB algorithm demonstrating the highest overall accuracy and best performance. Moreover, to address the black-box nature of ML algorithms, the SHAP method is employed to enhance the interpretability of the bond strength prediction process. The newly developed hybrid ML model holds promise as a novel approach for accurately assessing the bond strength at the FRP bar-concrete interface.

(GaAs/InAs)–GaAsSb digital alloy type-II superlattice for extended short-wavelength infrared detection

GaInAs–GaAsSb type-II superlattices (T2SLs) on an InP substrate are promising candidates for an optical absorption layer in the extended short-wavelength region (2–3 μm), offering more flexibility in designing a cutoff wavelength compared to strained GaInAs bulk material. However, T2SL-based photodetectors inherently suffer from lower quantum efficiency (QE) due to the reduced overlap of the wavefunctions of the conduction and valence bands in the optical matrix element of the T2SL. To improve QE, a (GaAs/InAs)–GaAsSb digital alloy T2SL, which replaces the GaInAs random alloy layer in the GaInAs–GaAsSb T2SL with a GaAs/InAs digital alloy, has been proposed recently by an empirical tight-binding calculation. This paper presents a demonstration of a fabricated photodetector using the (GaAs/InAs)–GaAsSb digital alloy grown on an InP substrate by molecular beam epitaxy and shows that the average QE in the wavelength region of 2.3–2.6 μm is approximately 1.6 times higher than that of a conventional GaInAs–GaAsSb T2SL photodetector. Furthermore, the dark-current density of the digital alloy photodetector is lower than that of the GaInAs–GaAsSb T2SL photodetector despite having a longer cutoff wavelength.

In-situ preheating: Novel insights into thermal history and microstructure of directed energy deposition-arc of hot-work tool steels

Directed Energy Deposition-Arc (DED-Arc/M) represents a promising approach to the production of complex and medium-sized parts, eliminating the need for specific tooling and the minimization of resource use. However, the inherent thermal conditions of DED-Arc/M, characterized by high cooling rates and successive reheating cycles, result in microstructures distinctly different from those observed in traditional cast materials. Nevertheless, the influence of thermal history on DED-Arc/M processed hot-work tool steels remains an insufficiently addressed topic in previous studies. Therefore, this work systematically investigates the effects of heat input and accumulation through assessing the thermal history and resulting microstructural changes during DED-Arc/M processing of the hot-work tool steel AISI H13 (X40CrMoV5-1). Accordingly, layer-wise heat input results in heat accumulation, leading to high interpass temperatures, significantly impacting the solidification and, thus, the microsegregation as well as the overall chemical homogeneity. By employing an innovative statistical approach that incorporates quantitative EDS data, the distribution of chemical elements was investigated. Furthermore, a novel computational procedure through the utilization of empirical and thermodynamic calculations was used to identify microstructural effects on the thermodynamic stability of retained austenite. This research offers vital insights into the microstructural dynamics of tool steels processed through DED-Arc/M, underlining the pivotal role of precise thermal management in tailoring material microstructures and, consequently, influencing the properties for unlocking the full potential of additive manufacturing for industrial applications.

Low temperature combustion and ash deposition characteristics of palm oil and forest replanting waste

This study evaluation and analysis the low-temperature combustion characteristics and ash deposition of palm oil waste, including palm kernel shell (PKS) and empty fruit bunch (EFB), as well as forestry waste in the form of rubber wood (RBW) and replanting waste (RPW). The experiments were conducted using a drop tube furnace at 900 ᵒC, which is considered a low temperature compared to the operating temperature of pulverized coal. The thermogravimetric method (TGA) was applied to understand the combustion properties of the biomass, while the ash deposition tendency was assessed through empirical calculations, combustion experiments, and direct observation of the ash produced. The results showed that RBW had the best combustion performance compared to the other samples. Although RPW has the highest ash fusion temperature value among all samples, with a value above 1200 ᵒC, the low-melting-point mineral phase such as fairchildite, produced at low-temperature combustion at 24.74%, increases the risk of ash sticking to metal surfaces. PKS and EFB produce very high albite mineral content of more than 60%, which has a low melting point and could potentially cause ash deposition problems. On the other hand, RBW, with a dominant content of SiO2 (21.79%), Al2O3 (20.24%), and MgO (8.03%), produces minerals dominated by quartz at up to 78% and magnesioferrite at more than 5%, both of which have a high melting point that tends to produce harmless ash. These findings provide insight into the potential of utilizing biomass in low-temperature combustion processes, which is a way of reducing waste and lowering greenhouse gas emissions.

Influence of Composition on Mechanical Properties and Sound Speed of AlAs1−xPx for Various Pressures

The mechanical properties of AlAs1−xPx alloy under the influence of composition have been determined. The sound speed of AlAs1−xPx has been determined at various compositions. The studied properties were obtained with the effect of composition at various pressures. The predicted values were consistent with the available experimental results. The investigation used empirical method calculations based on empirical pseudo potential theory (EPM) with the virtual crystal approximation (VCA) to broaden the applications of ternary alloys and better investigate their potential as novel materials. AlAs1−xPx is a material that shows promise for usage in multi-junction solar cell designs because its properties can potentially be adjusted. The mechanical stability of AlAs1−xPx alloys was obtained according to these conditions C11 + 2C12 > 0, C44 > 0, and C11-C12 > 0. The obtained anisotropy factor was not equal to 1, indicating the presence of elastic anisotropy in the studied alloy in the applied composition range. The AlAs1−xPx alloy is a ductile material throughout the entire composition range at various pressures due to the Bu/Cs values exceeding 1.75.

A Study of Multiple Comparison Tests with Imprecise and Uncertain Information

Objectives: This study aims to demonstrate the application of multiple comparison tests in situations involving imprecise, incomplete, and uncertain observations. Such data arises in many practical situations. Methods: Five post hoc multiple comparison tests viz. Fisher's LSD test, Tukey's HSD, Bonferroni ( ), Scheffe, and Hochberg ( ) method is utilized within a one-way design framework, with a significance level of 5%. Simulation studies are applied to the empirical level and power calculation. Findings: The study shows that Tukey's HSD ( ) shows the highest power in the determinant part, not the indeterminant part, compared to other tests. For, e.g., For (5, 5, 5, 5) with location parameter (0, .5, 1, 0), the power interval shows [.3953, .3584] where [ ]. Overall, and shows a prominent result and are preferable compared to other tests. From application, NANOVA provided the P-value is [<.001, <.001], i.e., tests are statistically significant. The P-value of the tests for pairs [1, 1] and [1, 1] accepted (greater than .05) means the average daily ICU occupancy of corona patients of more than 55 years and between 35 and 55 age groups are not equal. Similarly, other tests also show the same. Novelty: Neutrosophic Multiple comparisons are still developing, and computer programs are still unavailable to support them. We have applied a simulation study to check the performance of the tests. Keywords: Multiple comparisons, NANOVA, Neutrosophic Statistics, Indeterminacy, Covid-19

Data-Driven Interpretable Machine Learning Prediction Method for the Bond Strength of Near-Surface-Mounted FRP-Concrete

The Near-Surface-Mounted (NSM) technique for Fiber-Reinforced Polymer (FRP) strengthening is widely applied in the seismic retrofitting of concrete structures. The key aspect of the NSM technique lies in the adhesive performance between the FRP, adhesive layer, and concrete. In order to accurately predict the bond strength of embedded reinforced NSM FRP–concrete, this study constructs the relationship between the influencing factors of bonding performance and bond strength based on four machine learning (ML) algorithms: Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGB). A unified and interpretable prediction method for FRP–concrete interface bond strength based on SHAP values and ML algorithms is proposed. The results indicate that the ML models exhibit good predictive performance, with the R2 of the test set ranging from 0.8190 to 0.9621, showing higher accuracy than empirical calculation formulas. Among them, the RF algorithm demonstrates the highest overall accuracy and optimal performance. Additionally, the SHAP (Shapley additional explanations) method quantitatively confirms that the width of the FRP strip has the most significant impact on bond strength. The newly developed hybrid ML model has the potential to become a new choice for accurately assessing the bond strength of NSM FRP strengthening technology.

Structural performance of precast concrete sandwich panels with bolt-steel plate connection subjected to axial loading

A new precast concrete sandwich panel with glass fiber-reinforced polymer connectors was proposed, utilizing bolt-steel plate connections. A compressive experiment was carried out to investigate the performance. The main damage was concrete crushing in the bolt connection region, at the panel bottom, and above the joint. The connector type, solid concrete rib, slenderness ratio, and thickness affected the failure mode and bearing capacity. The use of GFRP connectors and solid concrete ribs enhanced the ultimate bearing capacity and stiffness of the panels while reducing the lateral deflection. Higher slenderness ratios leading to reduced axial bearing capacity and increased lateral deflection. Finally, the results of the existing empirical calculation formulae were compared with experimental ones, and they were overly conservative. A new formula for predicting the bearing capacity was proposed, and the difference percentages between the proposed formula and experimental results were −0.4 %–2.8 %, which was the most accurate prediction formula.

Empirical calculation of K-shell fluorescence cross sections for elements in the atomic range 16≤Z≤92 by photon effects ranging from 5.46 to 123.6 keV (Three-dimensional formulae)

Abstract The main objective of this study is to obtain three-dimensional empirical K-shell X-ray fluorescence cross-section (σKα,σKβ and σKtot) values for a wide range of elements 16≤Z≤92 for photons with energies from 5.46 keV to 123.6 keV, using more than 3300 experimental data values published between 1985 and 2023 by numerous researchers. These data values are fitted using an interpolation method including a three-dimensional function against atomic number Z and excitation energy E, resulting in a three-dimensional plot to estimate empirically a three-dimensional set of Kα, Kβ and Ktot X-ray fluorescence cross sections. The results of this empirical calculation are compared, for selected elements, with other empirical and experimental values reported in the literature, and a reasonable agreement is observed.

Multiphysics Simulation of a Vertical Roller Mill in Matlab®/Simulink®

The development of innovative products increasingly requires interdisciplinary collaboration among the fields of mechanical engineering, electrical/electronic engineering, and software. The growing complexity of products, driven by increased automation, poses significant challenges. Many equipment manufacturers optimise their development processes using software tools. By leveraging simulations and virtual models, product development time can be reduced. Additionally, the number of costly test bench experiments can be significantly minimised. Furthermore, numerical simulation is gaining importance in the field of predictive maintenance.To fully harness the potential of simulation, it is crucial to accumulate experience across various products and processes and validate them using real-world physical test benches. In this contribution, a vertical roller mill (VRM) is modelled across domains. Subsequently, selected simulation analyses are conducted and validated against measurement data from the physical demonstrator and analytical or empirical calculation models. Additionally, the suitability of the virtual prototype for model-based virtual commissioning is examined. The multi-physical model was developed using Matlab®/Simulink®, including the toolbox Simscape and Stateflow.

RMCProfile7: reverse Monte Carlo for multiphase systems.

This work introduces a completely rewritten version of the program RMCProfile (version 7), big-box, reverse Monte Carlo modelling software for analysis of total scattering data. The major new feature of RMCProfile7 is the ability to refine multiple phases simultaneously, which is relevant for many current research areas such as energy materials, catalysis and engineering. Other new features include improved support for molecular potentials and rigid-body refinements, as well as multiple different data sets. An empirical resolution correction and calculation of the pair distribution function as a back-Fourier transform are now also available. RMCProfile7 is freely available for download at https://rmcprofile.ornl.gov/.

Tapinarof and its structure-activity relationship for redox chemistry and phototoxicity on human skin keratinocytes

Tapinarof (3,5-dihydroxy-4-isopropylstilbene) is a therapeutic agent used in the treatment of psoriasis (VTAMA®). In this study, we examined the redox behaviour, (photo)stability, (photo)toxicity and (bio)transformation of tapinarof in the context of a structure-activity relationship study. Selected derivatives of the structurally related tapinarof were investigated, namely resveratrol, pterostilbene, pinosylvin and its methyl ether. Tapinarof undergoes electrochemical oxidation in a neutral aqueous medium at a potential of around +0.5 V (vs. Ag|AgCl|3M KCl). The anodic reaction of this substance is a proton-dependent irreversible and adsorption-driven process. The pKa value of tapinarof corresponds to 9.19 or 9.93, based on empirical and QM calculation approach, respectively. The oxidation potentials of tapinarof and its analogues correlate well with their HOMO (highest occupied molecular orbital) energy level. The ability to scavenge the DPPH radical decreased in the order trolox ≥ resveratrol > pterostilbene > tapinarof > pinosylvin ≫ pinosylvin methyl ether. It was also confirmed that tapinarof, being a moderate electron donor, is able to scavenge the ABTS radical and inhibit lipid peroxidation. The 4′-OH group plays a pivotal role in antioxidant action of stilbenols. During the stability studies, it was shown that tapinarof is subject to spontaneous degradation under aqueous conditions, and its degradation is accelerated at elevated temperatures and after exposure to UVA (315–399 nm) radiation. In aqueous media at pH 7.4, we observed an ∼50 % degradation of tapinarof after 48 h at laboratory temperature. The main UVA photodegradation processes include dihydroxylation and hydration. In conclusion, the phototoxic effect of tapinarof on a human keratinocytes cell line (HaCaT) was evaluated. Tapinarof exhibited a clear phototoxic effect, similar to phototoxic standard chlorpromazine. The IC50 values of the cytotoxicity and phototoxic effects of tapinarof correspond to 27.6 and 3.7 μM, respectively. The main HaCaT biotransformation products of tapinarof are sulfates and glucuronides.