Sara RozasLorena ZamoraCristina BenitoMert Atilhan...
99-114页
查看更多>>摘要：Herein,the synthesis and characterization methods of natural deep eutectic solvents based on monoterpenoids have been presented.Low viscous fluids with suitable physicochemical properties are produced.The materials are non-toxic,biodegradable,and cost-effective.Thus,they can be used to develop sustainable solvents for various processes and can find their applications in various fields.A theoretical study based on quantum chemistry and classical molecular dynamics is used for the nanoscopic characterization of structure,dynamics,and hydrogen bonding.The reported results help analyze the properties of this new family of solvents.The required information for developing structure-property relationships for proper solvent design to form a sustainable chemistry framework is obtained.
查看更多>>摘要：A novel Ni doped carbon quantum dots(Ni-CQDs)fluorescence probe was synthesized by facile electrolysis of monoatomic Ni dispersed porous carbon(Ni-N-C).The obtained Ni-CQDs showed a high quantum yield of 6.3％with the strongest excitation and emission peaks of 360 nm and 460 nm,and maintained over 90％of the maximum fluorescence intensity in a wide pH range of 3-12.The metal ions detectability of Ni-CQDs was enhanced by Ni doping and functional groups modification,and the rapid and selective detection of Fe3+and Cu2+ions was achieved with Ni-CQDs through dynamic and static quenching mechanism,respectively.On one hand,the energy band gap of Ni-CQDs was regulated by Ni doping,so that excited electrons in Ni-CQDs were able to transfer to Fe3+easily.On the other hand,the abundant functional groups promoted the generation of static quenching complexation between Cu2+and Ni-CQDs.In metal ions detection,the linear quantitation range of Fe3+and Cu2+were 100-1000 μM(R2=0.9955)and 300-900 μM(R2=0.9978),respectively.The limits of detection(LOD)were calculated as 10.17 and 7.88 μM,respectively.Moreover,the fluorescence quenched by Cu2+could be recovered by EDTA2-due to the destruction of the static quenching complexation.In this way,Ni-CQDs showed the ability to identify the two metal ions to a certain degree under the condition of Fe3+and Cu2+coexistent.This work paves the way of facile multiple metal ion detection with high sensitivity.
查看更多>>摘要：Gasification is a sustainable approach for biomass waste treatment with simultaneous combustible H2-syngas production.However,this thermochemical process was quite complicated with multi-phase products generated.The product distribution and composition also highly depend on the feedstock information and gasification condition.At present,it is still challenging to fully understand and optimize this process.In this context,four data-driven machine learning(ML)methods were applied to model the biomass waste gasification process for product prediction and process interpretation and optimization.The results indicated that the Gradient Boosting Regression(GBR)model showed good performance for predicting three-phase products and syngas compositions with test R2 of 0.82-0.96.The GBR model-based interpretation suggested that both feed and gasification con-dition(including the contents of feedstock ash,carbon,nitrogen,oxygen,and gasification temperature)were important factors influencing the distribution of char,tar,and syngas.Furthermore,it was found that a feedstock with higher carbon(＞48％),lower nitrogen(＜0.5％),and ash(l％-5％)contents under a temperature over 800℃could achieve a higher yield of H2-rich syngas.It was shown that the optimal conditions suggested by the model could achieve an output containing 60％-62％syngas and achieve an H2 yield of 44.34 mol/kg.These valuable insights provided from the model-based interpretation could aid the understanding and optimization of biomass gasification to guide the production of H2-rich syngas.
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