Rhodium clusters: investigation of the reactivity towaeds phosphines and selected d- and p-metals

During my PhD we focused on different research projects concerning the synthesis and characterization of new rhodium carbonyl clusters. More specifically, we studied the reactivity between Rh4(CO)12 and different bidentate phosphines, obtaining seven different species: Rh4(CO)10(dppe), Rh4(CO)8(dppe)2, Rh4(CO)10(dppf), {Rh4(CO)10(dpp-hexane)}2, {Rh4(CO)10(t-dppe)}2, Rh2(CO)2(dppf)2 and Rh4(CO)9(μ2-dppe)(μ1-dppeO). The reactivity of [Rh7(CO)16]3- with [AuCl4]- and Au(Et2S)Cl led to the formation of seven bimetallic clusters, of which four new ones, namely [Rh16Au6(CO)36]6-, [Rh10Au(CO)26]3-, [Rh16Au6(CO)36]4-, [Rh16Au6(CO)36]5-, [Rh22Au3(CO)47]5-, [Rh19Au5(CO)40]4- and [Rh20Au7(CO)45]5-. The reactivity of [Rh16Au6(CO)36]6- and [Rh10Au(CO)26]3- was studied as well. The reactivity of [Rh7(CO)16]3- with AgBF4, AgNO3 and with Pt(Et2S)2Cl2 was investigated, yielding only to the already known [Rh6N(CO)15]-, [PtRh5(CO)15]- and [PtRh4(CO)14]2- compounds. [Rh7(CO)16]3- war reacted with SnCl2·2H2O in acetone obtaining [Rh7Sn4Cl10(CO)14]5-, and [Rh12Sn(CO)23Cl2]4- was reacted with H+ obtaining [Rh18Sn3Cl2(CO)44]4-. Reactivity of [Rh7(CO)16]3- with InCl3 resulted in the isolation of [Rh12In(CO)28]3- and [Rh11In3(CO)25Cl2]3-, already known in our research lab, and the new [HRh11In(CO)26]3-. Moreover, a more straightforward synthesis for [Rh6InCl3(CO)15]2- was found, and this also led to the isolation of the [Rh6InCl2(DMF)(CO)15]-. The recover or rhodium as valuable carbonyl compound was also studied, and starting from a mixture of by-products it was possible to obtain the starting material [Rh7(CO)16]3-.

Development and testing of methodologies for the control and diagnosis of innovative combustion systems

In pursuit of aligning with the European Union's ambitious target of achieving a carbon-neutral economy by 2050, researchers, vehicle manufacturers, and original equipment manufacturers have been at the forefront of exploring cutting-edge technologies for internal combustion engines. The introduction of these technologies has significantly increased the effort required to calibrate the models implemented in the engine control units. Consequently the development of tools that reduce costs and the time required during the experimental phases, has become imperative. Additionally, to comply with ever-stricter limits on 〖"CO" 〗_"2" emissions, it is crucial to develop advanced control systems that enhance traditional engine management systems in order to reduce fuel consumption. Furthermore, the introduction of new homologation cycles, such as the real driving emissions cycle, compels manufacturers to bridge the gap between engine operation in laboratory tests and real-world conditions. Within this context, this thesis showcases the performance and cost benefits achievable through the implementation of an auto-adaptive closed-loop control system, leveraging in-cylinder pressure sensors in a heavy-duty diesel engine designed for mining applications. Additionally, the thesis explores the promising prospect of real-time self-adaptive machine learning models, particularly neural networks, to develop an automatic system, using in-cylinder pressure sensors for the precise calibration of the target combustion phase and optimal spark advance in a spark-ignition engines. To facilitate the application of these combustion process feedback-based algorithms in production applications, the thesis discusses the results obtained from the development of a cost-effective sensor for indirect cylinder pressure measurement. Finally, to ensure the quality control of the proposed affordable sensor, the thesis provides a comprehensive account of the design and validation process for a piezoelectric washer test system.