Identifying the descriptors that control catalytic performance is key to design catalysts with high activity and stability.As indirect descriptors related to catalytic activity,modulating the Pt–Pt interatomic distance and d-band center can effectively enhance the ORR activity.It is well recognized that the Pt–Pt interatomic distance is strongly correlated with the surface strain.Herein,PtMnM/C ternary intermetallics are constructed through partially replacing Mn in PtMn/C binary intermetallics with the other transition metal(M=Fe,Co,Ni,and Cu).The 3d-transition metal doping induces surface strain,and the ORR performance of PtMnM/C exhibits volcano relationship relative to both the Pt–Pt interatomic distance and d-band center.The PtMnCo/C with optimum strain exhibits the highest mass activity(1.06 A mgPt^(−1))at 0.9 V,which is 2.6 and 4.6 times higher than that of PtMn/C and commercial Pt/C catalysts,respectively.In addition,PtMnCo/C shows good durability with only 10 mV half-wave potential decay after 50,000 potential cycles.
A ternary early-strengthening agent consisting of calcium formate+triethanolamine+lithium sulfate was compounded with quercetin to shorten the setting time of cementitious materials while ensuring their early strength.The optimum ratio of the three early-strengthening agents was determined as 0.5%calcium formate+0.04%triethanolamine+0.4%lithium sulfate by response surface methodology.The effects of the ternary early-strengthening agent composed of calcium formate+triethanolamine(TEA)+lithium sulfate on cementitious pore sealing materials under the synergistic effect of quercetin were studied by means of the performance tests of compressive strength,fluidity,and setting time,and the microstructural characterizations of X-ray powder diffractometer(XRD),thermogravimetry(TG-DSC)and scanning electron microscopy(SEM).The study shows that the synergistic effect of ternary early-strengthening agent and quercetin forms a multi-performance composite admixture for cementitious materials.The best performance was obtained with the compounding scheme of 0.5%calcium formate+0.04%triethanolamine+0.4%lithium sulfate ternary early-strengthening agent and 0.05%quercetin.The compressive strength of 1,3,7,and 28 d are 94.8%,39.8%,42%,and 28%higher than those of the blank group,respectively.The initial time and final setting time are 41 and 57 minutes,respectively.According to the microscopic analysis,the network and fibrous C-S-H gels generated by ternary early-strengthening agents are attached to the surface promoted by quercetin,which forms skeleton support while thickening and solidifying the cement slurry,which enhances the early compressive strength of the cement-based materials.
Organic solar cells(OSCs) hold great potential as a photovoltaic technology for practical applications.However, the traditional experimental trial-and-error method for designing and engineering OSCs can be complex, expensive, and time-consuming. Machine learning(ML) techniques enable the proficient extraction of information from datasets, allowing the development of realistic models that are capable of predicting the efficacy of materials with commendable accuracy. The PM6 donor has great potential for high-performance OSCs. However, it is crucial for the rational design of a ternary blend to accurately forecast the power conversion efficiency(PCE) of ternary OSCs(TOSCs) based on a PM6 donor.Accordingly, we collected the device parameters of PM6-based TOSCs and evaluated the feature importance of their molecule descriptors to develop predictive models. In this study, we used five different ML algorithms for analysis and prediction. For the analysis, the classification and regression tree provided different rules, heuristics, and patterns from the heterogeneous dataset. The random forest algorithm outperforms other prediction ML algorithms in predicting the output performance of PM6-based TOSCs. Finally, we validated the ML outcomes by fabricating PM6-based TOSCs. Our study presents a rapid strategy for assessing a high PCE while elucidating the substantial influence of diverse descriptors.
Kiran A.NirmalTukaram D.DongaleSantosh S.SutarAtul C.KhotTae Geun Kim
Modeling the boundary layer flow of ternary hybrid nanofluids is important for understanding and optimizing their thermal performance,particularly in applications where enhanced heat transfer and fluid dynamics are essential.This study numerically investigates the boundary layer flow of alumina-copper-silver/water nanofluid over a permeable stretching/shrinking sheet,incorporating both first and second-order velocity slip.The mathematical model is solved in MATLAB facilitated by the bvp4c function that employs the finite difference scheme and Lobatto IIIa formula.The solver successfully generates dual solutions for the model,and further analysis is conducted to assess their stability.The findings reported that only one of the solutions is stable.For the shrinking sheet case,increasing the first-order velocity slip delays boundary layer separation and enhances heat transfer,while,when the sheet is stretched,the second-order velocity slip accelerates separation and improves heat transfer.Boundary layer separation is most likely to occur when the sheet is shrinking;however,this can be controlled by adjusting the velocity slip with the inclusion of boundary layer suction.
Nur Syahirah WahidNor Ain Azeany Mohd NasirNorihan Md ArifinIoan Pop
The unique long-range disordered atomic arrangement inherent in amorphous materials endows them with a range of superior properties,rendering them highly promising for applications in catalysis,medicine,and battery technology,among other fields.Since not all materials can be synthesized into an amorphous structure,the composition design of amorphous materials holds significant importance.Machine learning offers a valuable alternative to traditional“trial-anderror”methods by predicting properties through experimental data,thus providing efficient guidance in material design.In this study,we develop a machine learning workflow to predict the critical casting diameter,glass transition temperature,and Young's modulus for 45 ternary reported amorphous alloy systems.The predicted results have been organized into a database,enabling direct retrieval of predicted values based on compositional information.Furthermore,the applications of high glass forming ability region screening for specified system,multi-property target system screening and high glass forming ability region search through iteration are also demonstrated.By utilizing machine learning predictions,researchers can effectively narrow the experimental scope and expedite the exploration of compositions.
