Spectral stability study and molecular modeling of fluorence based conjugated polymers 2

2-1 Measurements 2-1-1 Ultraviolet -visible absorption spectroscopy UV-vis UV-Vis spectrometer measures the absorption of light in the visible and “near” ultraviolet region i.e., in the

CHAPTER 2 EXPERIMENTAL AND CALCULATION

METHODS

2-1 Measurements

2-1-1 Ultraviolet -visible absorption spectroscopy (UV-vis)

UV-Vis spectrometer measures the absorption of light in the visible and “near” ultraviolet region (i.e., in the range of 250-800 nm) As a whole, ultraviolet radiation is absorbed by a chromophore rather than the molecules When absorption occurs, electronic transition of molecule takes place It is thus particularly suitable for the study of electronic structure of conjugated polymers, which contain extended π-conjugated chain and exhibit unusual color changes

2-1-2 Photoluminescence spectroscopy (PL)

During the process of absorbing ultraviolet or visible electromagnetic radiation, molecules are elevated to an excited electronic state Some molecules will emit some

of this excess energy as light of a wavelength different from that of the absorbed radiation This process is called fluorescence (photoluminscence), which can be considered as a deexcitation process that occurs after excitation by photons

2-1-3 Differential scanning calorimetry (DSC)

DSC measures the heat flow of a sample with increasing temperature, and is a determinant of exo/endothermic transitions such as glass transition temperature (Tg), crystallization temperature and melting point (m.p.)

2-2 Semiempirical molecular orbital calculation

Various semiempirical methods could be employed in calculating the desired parameters In these methods, calculations are greatly simplified by approximating certain integrals with parametric functions or by neglecting some of these integrals The numerical parameters are usually obtained through fitting calculated properties to known experimental data, such as heats of formation and ionization potentials, of a selected set of compounds

As only a limited set of molecules, for which precisely known experimental data exist, is chosen for parametrization, the semiempirical approach is therefore not expected to give good fits in all instances Nonetheless, semiempirical methods are widely used because the computational time required for such methods is relatively short

Various semiempirical SCF methods with different parametrization schemes have been developed over the past years

AM1(Austin Model 1) was introduced in 1985 with the aim of overcoming some common defects in MNDO (Modified Neglect of Diatomic Overlap), such as overestimation of energies for sterically crowded molecules and inadequate description

of hydrogen bonding The MNDO method tends to overestimate the repulsion between atoms that are separated by a distance comparable to the νan der Waals distance By introducing additional radical Gaussian terms to the core-core repulsion terms, the inadequacies in MNDO could be reduced

Figure 2.1 shows the flow chart for a typical semiempirical geometry optimization process The Fock matrix is generated from empirical parameters

No No

Yes

Yes

alculate New Geometry

Print Output Calculate Other Properties Requested

Optimized ? Calculate Energy Gradients

? Achieved SCF

Solve SCF Equations Calculate Initial Guess of MOs Assign Parameters Read Input Geometry

C

Figure 2.1 Flow chart for a typical semiempirical geometry optimization [Dewar,

M J S.; Zoebisch, E G.; Healey, E F.; Steward, J J P., J Am Chem

Soc., 1985, 107, 3902.]