Flexible, polymer-based nanocomposite dosimeters have attracted growing interest for radiation monitoring in diagnostic radiology because of their mechanical conformability and potential for low-cost, large-area fabrication. While silicone-based detectors loaded with heavier metal oxides have previously been explored, the dosimetric behavior of room-temperature-vulcanizing (RTV-2) silicone elastomer reinforced with copper oxide nanoparticles — which differs from these systems in both filler chemistry and cure mechanism — has not been systematically characterized. In this work, a flexible silicone/copper oxide nanocomposite was fabricated and evaluated, for the first time, as a current-mode X-ray dosimeter. Copper oxide nanoparticles were synthesized by a simple precipitation method and incorporated into the silicone matrix at weight fractions ranging from 0 to 36.4 wt percent. Structural and chemical characterization confirmed the formation of phase-pure, monoclinic copper oxide nanoparticles with an average crystallite size of 27–29 nm, dispersed within the amorphous silicone network as submicron aggregates (mean Feret diameter 253 ± 86 nm within the composite, from 121 measured particles). Under 60 kV diagnostic X-ray irradiation, the nanocomposite exhibited a stable, nearly linear photocurrent response over a dose-rate range of 2.8–12 mGy/min. Among the compositions evaluated, the sample containing approximately 27.6 wt percent copper oxide showed the best overall dosimetric performance, with a sensitivity of 0.102 ± 0.014 nA/(mGy/min) and a noise-limited detection threshold of 0.25 mGy/min; given that the composition-dependent variation in photocurrent (∼11%) is comparable to the measurement uncertainty, this composition is best regarded as a favorable rather than a definitively optimized formulation. The enhanced sensitivity relative to the unfilled matrix is attributed primarily to the higher photon-interaction probability associated with the increased effective atomic number introduced by the copper oxide phase. The devices showed good short-term repeatability, with a relative standard deviation of 1.01% over repeated irradiation cycles, and a signal-to-noise ratio exceeding 90 at an applied bias of 400 V. These results indicate that silicone/copper oxide nanocomposites constitute a cost-effective, mechanically flexible platform for low-energy X-ray dosimetry, with performance comparable to other polymer–metal oxide systems reported in the literature.
آلودگی صوتی یکی از چالشهای مهم محیط زیست است که میتواند سلامت انسان و پایداری اکوسیستم را تهدید کند. استفاده از نانوساختارهای کربنی بهدلیل استحکام مکانیکی بالا و ویژگیهای منحصربهفرد هندسی، رویکردی نوین برای جذب صوت در مقیاس نانو متر بهشمار میآید. در این پژوهش، انتشار و جذب امواج صوتی در محیط گازی آرگون با بهرهگیری از شبیهسازی دینامیک مولکولی بررسی شد. مدل ابتدا در غیاب جاذب اعتبارسنجی گردید و سپس اثر قطر، تعداد، آرایش و فرکانس بر پارامترهای آکوستیکی شامل ضریب میرایی، عدد موج و ضریب جذب تحلیل شد. نتایج نشان داد افزایش قطر نانولولهها سطح تماس مؤثر را گسترش داده و افزایش تعداد آنها با کاهش مسیر آزاد مؤثر مولکولها، کارایی جذب را بهبود میدهد. آرایش مثلثی با محدودسازی فضا و افزایش برخوردهای گاز– دیواره، بالاترین ضریب جذب را ایجاد کرد. همچنین بررسی وابستگی فرکانس نشان داد میان فرکانس، ضریب میرایی و ضریب جذب همبستگی مستقیم برقرار است که تطابق آن با مطالعات پیشین، اعتبار شبیهسازی حاضر را تأیید میکند. محدودیت اصلی مطالعه، تمرکز بر محیط گازی آرگون و استفاده از نانولولههای تکجداره با ابعاد محدود است. با این وجود، یافتهها بر اهمیت بهینهسازی هندسه و پارامترهای ساختاری نانوجاذبها برای طراحی سامانههای کارآمد کنترل نویز در فرکانسهای بالا تأکید دارند.
Different studies have shown that metal–organic framework (MOF) materials are effective self-sacrificing templates for the fabrication of transition metal sulfides (TMSs) due to their high porosity, excellent tunability for components and structure, and abundant active sites. MOFs have been known as effective “sacrificial templates” for the synthesis and development of various TMS compounds. In this work, a self-sacrificial template strategy is used to synthesize CoMOF@CoS2 nanosheet arrays, derived from the CoMOF, which acts as a self-sacrificing template resembling rose-decorated scaffolds. These obtained nanosheet arrays not only preserve the effective structure of the CoMOF but also improve the conductivity due to the formation of cobalt sulfide. Cobalt sulfides have attracted widespread attention due to their variable redox valence states, electrical conductivity that is twice that of their metal oxide counterparts, and controllable structure and dimensions. The produced CoMOF@CoS2/CF exhibits a higher specific capacitance value of 1032.45 F g−1 in comparison with CoMOF/CF with a specific capacitance value of 368.501 F g−1 at a similar scan rate of 0.003 V s−1 in an electrolyte solution of 6.0 M KOH. The enhanced electrochemical performance is attributed to excellent fast discharge/charge performance and enhanced conductivity. The constructed asymmetric supercapacitor device indicates energy density (E) and power density (P) values of 1.47 Wh kg−1 and 4.01 kW kg−1, respectively. Also, 85.25
Four supervised regression-based machine learning (ML) models, including random forest (RF), support vector regression (SVR), light gradient boosting regression (LGBR), and extreme gradient boosting regression (XGBR), were employed for predicting catalytic oxidative desulfurization (ODS) of liquid fossil fuels using ionic liquids (ILs) as catalysts and H2O2 as the oxidant. The models were developed using 1383 experimental data points derived from ODS utilizing 31 various ILs. Six input variables, including process temperature in the range of 25-100 degrees C, reaction time in the range of 5-360 min, oxidant-to-sulfur (O/S) molar ratio in the range of 2-100, IL type, feedstock type, and extractant type, and one output variable of sulfur removal (%) were considered. Among these models, XGBR indicated the best performance, achieving the lowest root-mean-square error (RMSE = 2.72), mean absolute error (MAE = 1.63), and mean absolute percentage error (MAPE = 4%), along with the highest determination coefficient (R 2= 0.99).
This study examines the impact of artificial intelligence (AI) on the critical thinking (CT) abilities of language instructors in higher education. To this end, 10 university language instructors participated in semi-structured interviews, which were analyzed thematically. Having adapted the CT framework, the data were discussed in four key domains: clarification, advanced clarification, basis of inference, and inference. Participants highlighted the benefits of AI for concept clarification and a subtle understanding of information. Furthermore, they pinpointed the potential of AI to facilitate advanced clarification and a deeper analysis of underlying assumptions. However, regarding the basis of inference, the reliance on AI is reduced, suggesting the need for a practical integration of AI in educational practices. In conclusion, this research highlights the complex perspectives of instructors and underscores the pivotal role of AI in CT in higher education contexts, while emphasizing the need for further research on its implications to inform AI-integrated pedagogical approaches.
