A series of novel low band gap donor-acceptor （D-A） type organic co-polymers （BT-F-TPA, BT-CZ-TPA and BT-SI-TPA） consisting of electron-deficient acceptor blocks both in main chains （M1） and at the pendant （M2...A series of novel low band gap donor-acceptor （D-A） type organic co-polymers （BT-F-TPA, BT-CZ-TPA and BT-SI-TPA） consisting of electron-deficient acceptor blocks both in main chains （M1） and at the pendant （M2） were polymerized with different electron rich donor （M3-M5） blocks, i.e., 9,9-dihexyl-9H-fluorene, N-alkyl-2,7-carbazole, and 2,6-dithinosilole, respectively, via Suzuki method. These polymers exhibited relatively low band gaps （1.65-1.88 eV） and broad absorption ranges （680-740 nm）. Bulk heterojunction （BHJ） solar cells incorporating these polymers as electron donors, blended with [6,6]-phenyl-C61-butyric acid methyl ester （PC61BM） or [6,6]-phenyl-Cvl-butyric acid methyl ester （PC71BM） as electron-acceptors in different weight ratios were fabricated and tested under 100 mW/cm2 of AM 1.5 with white-light illumination. The photovoltaic device containing donor BT-SI-TPA and acceptor PC71BM in 1：2 weight ratio showed the best power conversion efficiency （PCE） value of 1.88%, with open circuit voltage （Voc） = 0.75 V, short circuit current density （Jsc） = 7.60 mA/cm2, and fill factor （FF） = 33.0%.展开更多
This study investigates the mechanical behavior of polyurethane(PU)nanocomposites reinforced with nanodiamonds(NDs)and proposes an integrated optimization-prediction framework that combines the Taguchi method with mac...This study investigates the mechanical behavior of polyurethane(PU)nanocomposites reinforced with nanodiamonds(NDs)and proposes an integrated optimization-prediction framework that combines the Taguchi method with machine learning(ML).The Taguchi design of experiments(DOE),based on an L9 orthogonal array,was applied to investigate the influence of composite type(pure PU,0.1 wt.%ND,0.5 wt.%ND),temperature(145℃-165℃),screw speed(50-70 rpm),and pressure(40-60 bar).The mechanical tests included tensile,hardness,and modulus measurements,performed under varying process parameters.Results showed that the addition of 0.5 wt.%ND substantially improved PU performance,with tensile strength increasing by 117%,Young’s modulus by 10%,and hardness by 21%at optimal conditions of 145℃,70 rpm,and 50 bar.SEM analysis revealed ductile fracture in pure PU and brittle fracture in the optimized PU/ND composite.ANOVA confirmed that composite type was the most influential factor,contributing 70.27%,87.14%,and 74.16%to tensile strength,modulus,and hardness,respectively.Regression modeling demonstrated a deviation of less than 10%between predicted and experimental values,validating the framework.To further strengthen predictive capability,computational modeling and analytical procedureswere employed throughmachine learning frameworks.RandomForest achieved R2/MSE values of 0.95/0.53(tensile),0.95/4.03(modulus),and 0.94/2.44(hardness).XGBoost performed better,with 0.98/0.12,0.98/0.77,and 0.98/0.60,while Gradient Boosting provided the highest accuracy with 0.99/0.03,0.99/0.02,and 0.99/0.01.Residual plots supported these results,showing wide fluctuations for RF and tightly clustered residuals near zero for GB and XGB,highlighting their superior accuracy,precision,and generalization.Overall,the integrated Taguchi-ML framework demonstrates a robust and efficient strategy for optimizing processing parameters and accurately predicting the performance of high-strength PU-ND nanocomposites.展开更多
基金This work wasfinancially supported bythe National Institute of Science and Technology,Berhampur,Odisha,India
文摘A series of novel low band gap donor-acceptor （D-A） type organic co-polymers （BT-F-TPA, BT-CZ-TPA and BT-SI-TPA） consisting of electron-deficient acceptor blocks both in main chains （M1） and at the pendant （M2） were polymerized with different electron rich donor （M3-M5） blocks, i.e., 9,9-dihexyl-9H-fluorene, N-alkyl-2,7-carbazole, and 2,6-dithinosilole, respectively, via Suzuki method. These polymers exhibited relatively low band gaps （1.65-1.88 eV） and broad absorption ranges （680-740 nm）. Bulk heterojunction （BHJ） solar cells incorporating these polymers as electron donors, blended with [6,6]-phenyl-C61-butyric acid methyl ester （PC61BM） or [6,6]-phenyl-Cvl-butyric acid methyl ester （PC71BM） as electron-acceptors in different weight ratios were fabricated and tested under 100 mW/cm2 of AM 1.5 with white-light illumination. The photovoltaic device containing donor BT-SI-TPA and acceptor PC71BM in 1：2 weight ratio showed the best power conversion efficiency （PCE） value of 1.88%, with open circuit voltage （Voc） = 0.75 V, short circuit current density （Jsc） = 7.60 mA/cm2, and fill factor （FF） = 33.0%.
基金supported and funded by theDeanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2503).
文摘This study investigates the mechanical behavior of polyurethane(PU)nanocomposites reinforced with nanodiamonds(NDs)and proposes an integrated optimization-prediction framework that combines the Taguchi method with machine learning(ML).The Taguchi design of experiments(DOE),based on an L9 orthogonal array,was applied to investigate the influence of composite type(pure PU,0.1 wt.%ND,0.5 wt.%ND),temperature(145℃-165℃),screw speed(50-70 rpm),and pressure(40-60 bar).The mechanical tests included tensile,hardness,and modulus measurements,performed under varying process parameters.Results showed that the addition of 0.5 wt.%ND substantially improved PU performance,with tensile strength increasing by 117%,Young’s modulus by 10%,and hardness by 21%at optimal conditions of 145℃,70 rpm,and 50 bar.SEM analysis revealed ductile fracture in pure PU and brittle fracture in the optimized PU/ND composite.ANOVA confirmed that composite type was the most influential factor,contributing 70.27%,87.14%,and 74.16%to tensile strength,modulus,and hardness,respectively.Regression modeling demonstrated a deviation of less than 10%between predicted and experimental values,validating the framework.To further strengthen predictive capability,computational modeling and analytical procedureswere employed throughmachine learning frameworks.RandomForest achieved R2/MSE values of 0.95/0.53(tensile),0.95/4.03(modulus),and 0.94/2.44(hardness).XGBoost performed better,with 0.98/0.12,0.98/0.77,and 0.98/0.60,while Gradient Boosting provided the highest accuracy with 0.99/0.03,0.99/0.02,and 0.99/0.01.Residual plots supported these results,showing wide fluctuations for RF and tightly clustered residuals near zero for GB and XGB,highlighting their superior accuracy,precision,and generalization.Overall,the integrated Taguchi-ML framework demonstrates a robust and efficient strategy for optimizing processing parameters and accurately predicting the performance of high-strength PU-ND nanocomposites.
