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Conductivities of the solutions of a number of protonic acids in ethyl acetate plotted against the composition of the solutions indicate the formation of 1:1 compounds. Their dipole moments indicate a very high polarity. This has been explained by proposing the formation of compounds of the type CH3COOC2H5 · H+ · A−. The conductivity of the solutions of tertiary bases has been explained on the basis of the formation of compounds of the type (CH2COOC2H5)−BH+. Conductometric, potentiometric, and titrations using visual indicators to detect the end point have been carried out to confirm the autoprotolysis of ethyl acetate as 2 CH3COOC2H5 rlhar2; (CH3COOC2H5 · H)+ + (CH2COOC2H5)−.

The compound, Fe2CIa(OH)2(MeCO2)2 " 2MeCO2It " H20, combines with lithium and silver acetates to form Fe2CI(OH)2(MeCO2)3 " 2MeCO2t-I and Fe2(OH)2(MeCO2)4 • H20, respectively. Iron(Ill) chloride in tetrahydrofuran reacts with silver acetate to give a precipitate consisting of a mixture of Fe(MeCO2)3 and AgCI which, when extracted with boiling acetic acid, yields " To whom all correspondence should be addressed. Fe202(MeCO2)2 3MeCO2H. With DMSO on theother hand an adduct Fe(MeCO2)3 • DMSO is formed which loses DMSO in vacuo to give ferric acetate, Fe(MeCO2)3. l.r. spectra, molar conductance, thermal decomposition, molecular weight and magnetic susceptibility measurements are reportcd.

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Microfluidic devices have found extensive applications in mechanical, biomedical, chemical, and materials research. However, the high initial cost, low resolution, inferior feature fidelity, poor repeatability, rough surface finish, and long turn-around time of traditional prototyping methods limit their wider adoption. In this study, a strategic approach to a deterministic fabrication process based on in-situ image analysis and intermittent flow control called image-guided in-situ maskless lithography (IGIs-ML), has been proposed to overcome these challenges. By using dynamic image analysis and integrated flow control, IGIs-ML provides superior repeatability and fidelity of densely packed features across a large area and multiple devices. This general and robust approach enables the fabrication of a wide variety of microfluidic devices and resolves critical proximity effect and size limitations in rapid prototyping. The affordability and reliability of IGIs-ML make it a powerful tool for exploring the design space beyond the capabilities of traditional rapid prototyping.