Dynamics of Disordered Structure of Ï-Conjugated Polymers

Chapter 10

Downloaded by PURDUE UNIVERSITY on August 5, 2013 | http://pubs.acs.org Publication Date (Web): October 14, 2011 | doi: 10.1021/bk-2011-1077.ch010

Dynamics of Disordered Structure of π-Conjugated Polymers Investigated by Solid-State NMR N. Asakawa,*,1 Y. Inoue,2 T. Yamamoto,3 R. Shimizu,4 M. Tansho,4 and K. Yazawa2 1Department of Chemistry and Chemical Biology, Graduate School of Engineering, Gunma University, 1-5-1 Tenjincho, Kiryu, Gunma 376-8515, Japan 2Department of Biomolecular Engineering, Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8501, Japan 3Chemical Resources Laboratory, Tokyo Insitute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kangawa 226-8503, Japan 4National Institute for Materials Science, 3-13 Sakura, Tsukuba, Ibaraki 305-0003, Japan *E-mail: [email protected]

Stochastically excitable threshold units using functional materials will potentially be among the key devices for the production of noise-driven bio-inspired sensors and information processors with ultra-low energy consumption. In particular, a noise generator and threshold unit will be able to be fabricated utilizing the fluctuations found in materials that include structural, electric dipole, magnetic, and spin. This article deals with studies of polymer dynamics mainly by 13C solid-state NMR and structural fluctuations due to twist dynamics of π-conjugated polymers near the order-disorder phase transition or twist glass transition. The twist dynamics of π-conjugated polymers will be important in the design and production of future electronic devices such as bio-inspired stochastic information processors.

© 2011 American Chemical Society In NMR Spectroscopy of Polymers: Innovative Strategies for Complex Macromolecules; Cheng, H., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2011.


Introduction Biological systems employ a mechanism of sensoring and information transmission/processing with ultra low energy consumption, which are found in their sensory (1–3) and central (4–6) nervous systems. This situation contrasts sharply with conventional digital computers, where energy consumption is quite high. The low energy consumption of biological information processing is due to its mechanism of environmental noise utilization. Conventional digital technology has been developed based on “noise suppression.” Because of this design, the system consumes high energy, say 3 or 5 volts and 10-100 watts per CPU in order to ensure error-less, deterministic operation with Boolean logic. On the other hand, it is known that, for instance, a human brain consumes at most 10 W (7) for information processing although the number of elements (neurons) are at least hundreds of times larger than a typical central processing unit (CPU). This is due to noise tolerant mechanism of information transmission and processing. Sensory nerves of several biological systems including crickets (1), crayfish (2), and paddlefish (3) exploit the phenomenon of stochastic resonance (SR) for weak signal detection within a noisy environment (4). Counterintuitively, the signal/noise ratio (SNR) of weak sensory signal is enhanced by internal and/or external noise to these systems. Thus. biological systems enhance their performance with progressive utilization of noise. Similar phenomena can be found in several layers of biological hierarchy, including membrane proteins (8), cells (9), nervous tissues (10), brains (11), and individuals (12). Recently, the concept of neuronal computations with stochastic network in the brain has also been accepted (13). If one seeks to bio-mimick artificial agents with high adaptability to unpredictable, abrupt environmental change, it would be difficult to do with conventional digital technology, where operations based on “If…Then…Else” are prerequisite. In such a case, one needs to establish novel information sensors and processors with ultra-low energy consumption that employs biological mechanisms of progressive utilization of noise. Recently, Asakawa et al. (14) demonstrated that a delayed feedback network of stochastic threshold units have the ability of noise-driven autonomous switching depending on a sensory signal. Below we listed the four important characteristics that can be realized with such a device element. i) ii) iii) iv)

nonlinear response to external field, noise generation, one-directional signal transmission, and dynamic modulation of inter-elemental connectivity.

Historically, polymeric materials had not been used for active electronic device materials mainly because many polymers show unstable electric properties due to poor heat resistance or heat deflection, large structural fluctuation, or chemical instability. Of course, recent developments in organic light emitting diodes(OLED) (15), organic field effect transistors(OFET) (16), and organic photovoltaic cells(OPV) (17) have been remarkable, and these applications 162 In NMR Spectroscopy of Polymers: Innovative Strategies for Complex Macromolecules; Cheng, H., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2011.


related to polymers will form a mainstream of polymer electronics. Even so, unstable properties, or (more correctly) large time and/or spatial fluctuations of physical properties, e.g., carrier mobility and electric conductivity, can be an important determinant in producing noise-driven bio-inspired devices that we are interested in. Poly(alkylthiophene)s [P3ATs] are a class of π-conjugated polymers soluble in ordinary organic solvents. Previous x-ray diffraction (18) and differential scanning calorimetry (DSC) measurements (19, 20) indicate that P3ATs show an order-disorder phase transition. By tuning the length of alkyl side-group, one can observe a transition temperature near room temperature. It is also well known that P3ATs without doping show nonlinear electrical conductivity. Since it is theoretically predicted that static (21) and dynamic (22) disorder affect the carrier mobility of π-conjugated polymers, the dynamics associated with the transition could be the origin of random electric properties and would have the potential to be utilized for a stochastic threshold unit or a molecular noise generator. An interacting ensemble of such stochastic threshold units can be thought of as a complex system that generates such emergent properties as “synchronization” (23, 24) and “chaotic state” (25) (Fig.1). We believe that such cooperative dynamics produced by an ensemble of stochastic elements will be able to be used for noise-driven information sensoring and processing in the future. In this article, we shall show our recent works (26–28) concerning dynamics of P3ATs investigated by DSC, Fourier transform infrared (FTIR), and 13C solidstate nuclear magnetic resonance (NMR) spectroscopies. The knowledge obtained here will be useful for material selection/screening in the fabrication of stochastic threshold units using π-conjugated polymers.

Results and Discussions Molecular Dynamics of Regioregulated P3AT We have recently investigated the phase diagram for P3ATs (26–28). There are various thermodynamic and non-equilibrium states including crystalline (C), glassy crystalline (GC), plastic crystalline (PC), liquid glass (G), and isotropic lipuid (I). The complex diagram can be understood using the concept of frustration against crystallization (29). In this article, we focus on the structure and dynamics of poly(3-butylthiophene) [P3BT] and poly(3-hexylthiophene) [P3HT]. P3BT shows the richest phase diagram among P3AT, and P3HT is the most widely used polymer in P3ATs mainly because it shows the largest carrier mobility among P3ATs (30). DSC of Regioregulated P3BT and P3HT Figure 2A shows the DSC chart of P3BT. We found an endothermal peak at 333 K as well as a melting peak (520 K). The peak at 333 K is probably different from the heat capacity jump at 303K for the glass transition of the quenched sample by liquid nitrogen (Figure 2B). (Here, glass transition means conventional liquidglass transition.) Thus, the peak at 333 K is attributable to some sort of solid-solid 163 In NMR Spectroscopy of Polymers: Innovative Strategies for Complex Macromolecules; Cheng, H., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2011.


transition. The endothermal peak at 333 K could not be observed in the second DSC heating scan after cooling at a rate of 50 K/min., inferring that the kinetics of the transition is quite slow. An x-ray diffraction study of P3BT is available (31), where the authors concluded that there is a side-group transition from a mixture of end-to-end (phase I) and interdigitation (phase II) packings to the pure phase I state. Nevertheless, on the DSC of P3HT, we could not detect anything other than the melting peak around 500 K (Figure 2C).

Figure 1. An example of Langevin dynamics simulation for an ensemble of stochastic threshold units. Gray dots stands for the timing when the unit outputs a firing pulse. A) Schematic drawing of a neural network consisting of 100 excitatory units (shown as “E”) and 100 inhibitory neurons (“I”). All the excitatory units are connected to one another and the same is true for the inhibitory units. B) “oscillation” emergent from an ensemble of units. External noise input or internal noise is required for the network to emerge from the cooperative dynamics. C) Typical bifurcation phenomenon from oscillation (“limit cycle”) to chaos when inter-unit interaction is modified through sensory input by abrupt environmental change.

164 In NMR Spectroscopy of Polymers: Innovative Strategies for Complex Macromolecules; Cheng, H., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2011.


Figure 2. Differential scanning calorimetry (DSC) charts for powdered samples for regioregulated P3ATs. (A) the first heating scan for pristine P3BT, (B) the second heating scan for a quenched sample by liquid nitrogen after the first heating scan in DSC measurements, and (C) the first heating scan for P3HT. Here, three questions remain. First, why did the π-π stacking peak in the x-ray powder pattern smear out below 323 K, while the same peak was visible above 323 K? Secondly, what is the driving force of transition? Thirdly, why is the difference between P3BT and P3HT? We performed FTIR and solid-state NMR measurements in order to address these problems.

FTIR Measurements for Regioregulated P3BT and P3HT Figure 3 shows variable temperature FTIR measurements for P3BT and P3HT. The spectra are for the out-of-plane deformation mode for the C4-H bond in the main chain. For P3BT, there are two peaks, one at 825 cm-1 and the other at 810 cm-1. With increasing temperature, these signals are merged to the 820 cm-1 band (Fig.3A, right). At higher temperatures, the absorption of the 820 cm-1 band is reduced and that of the 837 cm-1 band for melting is increased. In total, we observed four peaks. At lower temperatures, the 825 cm-1 band is constant down to 173 K, and the 820 cm-1 band is gradually shifted to 810 cm-1. From these experiments, we assigned the four peaks as follows: 837 cm-1 (liquid), 825 cm1(unknown from IR measurements), 820 cm-1 (metastable crystalline state), and 810 cm-1 (crystalline state, energetically stabler than that of 820 cm-1).

165 In NMR Spectroscopy of Polymers: Innovative Strategies for Complex Macromolecules; Cheng, H., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2011.


Figure 3. Variable temperature FTIR spectra for thin films of regioregulated P3ATs, P3BT (A) and P3HT (B). For P3HT, the IR spectrum was similar to but simpler than that for P3BT. Below 300 K (=Tcp, the transition temperature between the crystalline and plastic crystalline states; vide infra for the detailed definition), two main peaks were observed at 816 and 820 cm-1. From curve fitting, a small signal at 826 cm-1 was also detected as a minor component. Above 300K, the 820 cm-1 peak is dominant up to the melting temperature (500 K). 13C

CPMAS NMR Measurements of Regioregulated P3BT

In order to investigate the transition around 333 K, we performed 13C crosspolarization magic-angle sample spinning (CPMAS) experiments for powdered samples of P3BT and P3HT. The chemical structure of 3-butylthiophene unit and 13C CPMAS spectra of P3BT are shown in Figure 4A and B, respectively. The clear shoulder signal of the C4 methyl carbon of P3BT can be seen around 16.0 ppm below 333 K, whereas the spectra at the higher temperatures show a unique component (14.6 ppm), meaning that at least two chemically inequivalent methyl carbons in P3BT exist below 333 K (Fig.4C). Because the methylene carbons show no peak splitting, only the end of the butyl chain shows two or more distinct states. In Figure 4D, the signals of the thiophene ring for P3BT appear at 120–145 ppm as noted in previous studies of oligothiophene (32) and regiorandom poly(3166 In NMR Spectroscopy of Polymers: Innovative Strategies for Complex Macromolecules; Cheng, H., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2011.


octylthiophene) (33). Broadening and shift of C2 and C5 peaks with heating were also observed. We shall discuss the broadening and shift later in conjunction with spin-lattice relaxation times. Results of FTIR and CPMAS NMR show that the structural change of P3BT occurs markedly around 333 K. At temperatures greater than 333 K, the state of the main chain is attributed almost uniquely to 820 cm1 in FTIR. The side chain is also at a unique state, as shown by the results of CPMAS NMR. Below 333 K, the main chain consists of mainly two states: the main component attributed to 825 cm-1 and the other to 810 cm-1. From these, the fact that the methyl moiety of the side chain also shows at least two components is probably related to the main-chain states.

Figure 4. Variable temperature 67.8MHz 13C CPMAS NMR spectra for a powdered sample of P3BT. A) chemical structure of 3-butylthiophene unit, B) full range spectra, C) expanded spectra for alkyl carbon, and D) expanded spectra for thiophene ring carbons.

Let us recall the three aforementioned questions. What explains the absence of the (010) peak (π-π stacking) and the weakness of (100) peaks (inter-chain packing parallel to the planar π-conjugation) below 323 K in the WAXD measurement (31)? Even if both phases (phase I and phase II) coexist, the respective scattering peaks of x-ray diffraction should be observed. Furthermore, the peak shifts in the IR spectrum from 825 and 810 cm-1 to 820 cm-1 with heating do not agree with the argument by Causin et al. (31), i.e., the transition from phase I and phase II to phase I, because no common IR absorption peak attributable to phase I is visible below and above the transition temperature. Furthermore, what is the driving force of the transition? What is the difference between P3BT and P3HT? Clarifying 167 In NMR Spectroscopy of Polymers: Innovative Strategies for Complex Macromolecules; Cheng, H., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2011.

these issues would be the key to rationalizing the data obtained from DSC, FTIR, CPMAS NMR, and WAXD. Here, we specifically examine the dynamics of the twisting motion of the main chain in the crystalline state.


13C

Spin-Lattice Relaxation Time Measurements for Regioregulated P3BT

We performed 13C spin-lattice relaxation time measurements for P3BT using the Torchia method (34) at various temperatures to investigate the molecular dynamics. An Arrhenius plot of the spin-lattice relaxation rate (R1=T1-1) for each carbon is shown in Figure 5. For the side group (Figure 5A), each R1 value was obtained using a simple exponential fitting curve. The figure shows the subtle change of slopes around 333 K, but the decrease of R1 was observed throughout the measured temperature range with heating (from 303 to 373 K). This tendency is visible in the extremely narrowed regime according to the classical Bloembergen-Pound-Purcell (BPP) theory (35), indicating that the side chains behave similarly to liquid. The subtle change of slopes was inferred to result from the conformational change of the main chain because it would be difficult to believe that further changes in average conformation should occur in the side group in the liquid state. On the other hand, the relaxation of main-chain carbons showed no single exponential decay in the measured temperature range. For that reason, we tried to fit the decay curves using a Kohlraush-Williams-Watts (KWW) function (36). In general, the KWW function is used to express magnetization near and below the glass-transition temperature (Tg ) because the distribution of the relaxation rate gave rise to nonexponential recoveries (37).The temperature dependences of R1 for each aromatic carbon are shown in Figure 5B. This figure shows that the main chain must be in the slow motion regime because of the increase of R1 with heating. More noteworthy is the fact that discontinuous slope changes are apparent around 333 K. In many cases, similar tendency is apparent in 2H NMR measurements, at the glass transition for many glass formers (38–40). In the case of NMR spin-lattice relaxation measurements, we can distinguish three temperature regions for the arguments of glass transition. (i) T

Dynamics of Disordered Structure of Ï-Conjugated Polymers

Recommend Documents

Dynamics of Disordered Structure of Ï-Conjugated Polymers