Advances in Chemical Physics, Volume 162 E-Book
259,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Advances in Chemical Physics
- Sprache: Englisch
The Advances in Chemical Physics series provides the chemical physics field with a forum for critical, authoritative evaluations of advances in every area of the discipline.
• This is the only series of volumes available that presents the cutting edge of research in chemical physics.
• Includes contributions from experts in this field of research.
• Contains a representative cross-section of research that questions established thinking on chemical solutions
• Structured with an editorial framework that makes the book an excellent supplement to an advanced graduate class in physical chemistry or chemical physics
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 604
Veröffentlichungsjahr: 2017
Ähnliche
Table of Contents
Title Page
copyright
Editorial Board
List of Contributors Volume 162
Preface to the Series
Chapter 1: Electronic Structure and Dynamics of Singlet Fission in Organic Molecules and Crystals
I. Introduction
II. Electronic Structure of Low-Lying Excited States
III. Measuring Charge-Transfer Character
IV. Charge-Transfer Implications for Singlet Fission
V. Theory of Spectroscopy, Reaction Rates, and Singlet Fission Dynamics
VI. Conclusions and Outlook
Acknowledgments
References
Chapter 2: An Approach To “Quantumness” In Coherent Control
I. Introduction
II. The Coherent Control Interferometer
III. Path Distinguishability
IV. Quantum Erasure Coherent Control
V. Delayed Choice Coherent Control
VI. Toward Rigorous Experimental Certification of The Nonclassicality of Coherent Phase Control
VII. Application to Photoelectron Spin Polarization Control in Alkali Photoionization
VIII. Facing The Loopholes
IX. Conclusions
Acknowledgments
References
Chapter 3: Energetic and Nanostructural Design of Small-Molecular-Type Organic Solar Cells
I. Introduction
II. History
III. Principles
IV. Nanostructure Design
V. Energetic Structure Design
VI. Conclusion
Acknowledgments
References
Chapter 4: Single Molecule Data Analysis: An Introduction
I. Brief Introduction to Data Analysis
II. Frequentist and Bayesian Parametric Approaches: A Brief Review
III. Information Theory as a Data Analysis Tool
IV. Model Selection
V. An Introduction to Bayesian Nonparametrics
VI. Information Theory: State Identification and Clustering
VII. Final Thoughts on Data Analysis and Information Theory
VIII. Concluding Remarks and the Danger of Over-Interpretation
Acknowledgments
References
Chapter 5: Chemistry With Controlled Ions
I. Introduction
II. Cooling and Control of Ions in the Gas Phase
III. Control of Neutral Molecules in the Gas Phase
IV. Ion–Molecule Reaction Dynamics: A Brief Outline
V. Applications and Examples
VI. Conclusions and Outlook
Acknowledgments
References
Index
End User License Agreement
Pages
1
2
ix
xi
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
Guide
Cover
Table of Contents
Preface to the Series
Begin Reading
List of Illustrations
Chapter 1: Electronic Structure and Dynamics of Singlet Fission in Organic Molecules and Crystals
Figure 1 The prototypical herringbone crystal structure of the oligoacenes. Here, a cartoon of tetracene is shown for the single molecule from above (a), from the side (b), and for the crystal (c). The in-plane short and long axes of the crystal are denoted by and , respectively.
Figure 2 The five electronic configurations required for a minimal representation of the low-energy singlet excited states in organic molecules and crystals. Only the HOMO and LUMO orbitals of nearest-neighbor molecules are shown, and in practice the spin-adapted electronic configurations are used. The five states consist of intramolecular Frenkel-type excitations (two leftmost), intermolecular charge-transfer excitations (two center), and the triplet-pair double excitation (rightmost).
Figure 3 Configuration interaction diagram (top) and schematic real-space structure (bottom) of singlet (a) and triplet (b) excitons in oligoacene crystals. The configuration interaction diagrams show the evolution, starting from the noninteracting band gap , of the Frenkel exciton (FE) from the left and the charge-transfer exciton (CT) from the right. The exciton hole is fixed and indicated by a blue circle, and the conditional electron probability distribution is indicated by red circles.
Figure 4 Comparison of absorption spectra between experiment and the theory described in the text, for a single pentacene molecule in solution (a) and for a pentacene crystal (b). The theoretically predicted crystal spectrum is also divided into its polarized and components, where the onset energy difference yields the Davydov shift of approximately 150 meV.
Figure 5 The singlet fission rate in the oligoacene family as a function of two important electronic variables: the effective electronic coupling (a) and the energy difference or exoergicity (b). At large electronic coupling, a nonadiabatic to adiabatic transition takes place (a) and at large exoergicity, multiphonon effects are necessary to describe the smaller, but nonzero rate of singlet fission (b). The theory described in the text is shown as solid black lines, while the corresponding simpler lowest order results are shown as dashed red lines.
Chapter 2: An Approach To “Quantumness” In Coherent Control
Figure 1 (color online) In an optical MZI, two incoming beams of light are first split at a half-transparent mirror, , into two spatially well-separated daughter beams, and , then allowed to accumulate a mutual phase shift , and eventually recombined by another half-transparent mirror, . At the two output ports of the MZI, detectors measure the intensity of the outgoing light. Observed is an oscillation of the intensity with the phase shift . In units of the background intensity, this “fringe pattern” is, ideally, equal to .
Figure 2 Schematic of the proposed coherent control interferometer (CCI). Shown are the sets and as well as the CCI's input and output ports. The hyperplane separates the regions and .
Figure 3 (color online) Mach–Zehnder interferometer in the open configuration with missing beam merger. The beams are mapped one-to-one into the detector ports. No interference pattern is measured.
Figure 4 Sketch of the output port populations , , of the coherent control interferometer (CCI) in the open configuration . Interference is absent. An identical pattern is produced by the open Mach–Zehnder interferometer (MZI) shown in Figure 3.
Figure 5 (color online) A trivially open CCI. Solid: process A. Dashed: process B.
Figure 7 A closed CCI. Solid: process A. Dashed: process B.
Figure 8 Absolute Bell expectation value with respect to the state (63) as a function of the visibility . Maximum violation, , occurs for .
Figure 9 (a) Contour plot of the absolute Bell expectation value with respect to the state (113) (from Eq. (115)) as a function of the phase and the overlap . Higher values have a darker shade (see scale in upper right corner). The dashed black line is the position of the global maximum of with respect to , but for fixed . This shows how, for given , one would have to choose to achieve the highest possible Bell violation. Local maxima are labeled and indicated with a dot. Especially noteworthy is the saturation of the Bell violation at the Tsirelson bound for and . (b) Plot of Eq. (118), which is a cut through panel (a) at .
Figure 10 Electric dipole selection rules between states in one- and two-photon absorption. These two processes are distinguishable by the final orbital angular momenta, in particular, the separation into states with even (, ) and those with odd parity ().
Figure 12 Scattering geometry of the open configuration with incident light wavevectors and with projection , the emitted photoelectron is detected in the direction , which, together with and , forms a right-handed orthogonal triad that is rotated from the laboratory's reference frame by around the -axis. The alkali atom is located at the origin. The polarization of the incoming radiation is indicated in color (shades of gray). The open configuration employs linearly polarized light. (
See color plate section for the color representation of this figure
.)
Figure 13 In the closed configuration , the processes are not distinguishable by their outcomes, since the transition amplitudes between all spin combinations are finite and unbiased. Our proposed implementation deviates from the demanded behavior. Only for and , the amplitudes are unbiased. However, for , they are biased, illustrated here by the width of the arrows. (
See color plate section for the color representation of this figure
.)
Figure 14 Scattering geometry of the closed configuration with the alkali atom at the center. Wavepacket polarizations are indicated in color (shades of gray). The amplitude function of the first wavepacket is concentrated around the incident direction and the circular polarization . makes an angle of rad with the -axis. The second wavepacket (described by ) is incident from the direction , for which . Its polarization is elliptical. (
See color plate section for the color representation of this figure
.)
Chapter 3: Energetic and Nanostructural Design of Small-Molecular-Type Organic Solar Cells
Figure 1 The history of organic solar cells. The main breakthroughs are indicated by arrows. The efficiencies after the year 2000 (closed circles) are plotted according to the NREL chart. Open circles are plotted according to recent press releases.
Figure 2 (a) Commercial flexible see-through organic solar cell module (Mitsubishi Chemical Corporation). (b) Building using these modules on the exterior walls and windows.
Figure 3 Chemical structures of typical organic semiconductors acting as donors and acceptors that appear in this chapter.
Figure 4 Size of excitons for an inorganic semiconductor (Si) (a) and an organic semiconductor (C
60
) (b). The former is a Wannier-type and easily dissociates into free carriers. The latter is a Frenkel-type and dissociates into free carriers with difficulty.
Figure 5 Carrier generation in organic semiconductors. (a) Single molecule solids. (b) Donor (D)/acceptor (A) sensitization for carrier generation by mixing two kinds of organic semiconductor molecules. Efficient free carrier generation occurs from the charge transfer (CT) exciton. (c) Photoinduced electron transfer from the HOMO of the donor molecule (D) to the HOMO of the acceptor molecule (A). (d) Photoinduced electron transfer from the LUMO of the donor molecule (D) to the LUMO of the acceptor molecule (A).
Figure 6 Schematic illustration of a two-layer cell composed of perylene pigment (Im-PTC) acting as an acceptor molecule (A) and copper phthalocyanine (CuPc) acting as a donor molecule (D). Photocurrent is generated only in the active region (shaded) close to the heterojunction, and all other parts of the organic films act as dead regions.
Figure 7 (a) A small-molecule cell consisting of blended junctions in an organic semiconductor, containing both donor and acceptor molecules. The entire bulk of the blended layer acts as an active layer for photocarrier generation. (b) Energetic structure of a blended junction in a small-molecule cell.
Figure 8 (a) Vertical superlattice structure. (b) Co-evaporant third molecule introduction. The balls, plates, and sticks correspond to C
60
, H
2
Pc, and third molecules, respectively. Matsuo
et al.
, 2009 [14]. Reproduced with permission of American Chemical Society. Cross-sectional SEM images of C
60
:H
2
Pc co-deposited films without (c) and with (d) third molecule. Matsuo
et al.
, 2009 [14]. Reproduced with permission of American Chemical Society. (e) Interdigitated structure of round columns of crystalline benzoporphyrin (BP) with diameters of around 20 nm standing almost vertically, used in highly efficient cells.
Figure 9 (a) Multilayered film and (b) vertical multilayered film.
Figure 10 Fabrication process. (i) Substrate. (ii) Deposition. (iii) Embedding. (iv) Slicing. (v) Electrode deposition.
Figure 11 (a) Dependence of photocurrent quantum efficiency on layer width (
x
). Only Me-PTC was excited by 480 nm monochromatic light in an electric field of 2 × 10
4
V/cm. (b) Schematic illustration of the active area (shaded) in the vicinity of Me-PTC/H
2
Pc junction. The dead area (not shaded) in the Me-PTC layer decreases as the number of heterojunctions increases and is eliminated at
x
= 10 nm. (c) Semilogarithmic plot of the data in Figure 11(a).
Figure 12 Photograph of single crystals of (a) C
60
, (b) C
70
, (c) H
2
Pc, and (d) 6T.
Figure 13 (a) Structure of organic p–i–n solar cell. The C
60
:H
2
Pc co-deposited layer (thickness:
X
nm) having a quasi-vertical superlattice structure (Figure 8(a,d)) sandwiched between p-type H
2
Pc and n-type NTCDA. (b) Current–voltage (
J–V
) characteristics for a p–i–n cell with an i-layer thickness (
X
) of 250, 600, 960 nm, and 1.2 µm. Cell parameters (
X
= 960 nm);
J
sc
: 18.3 mA/cm
2
,
V
oc
: 0.40 V, FF: 0.53, efficiency: 5.3%. The simulated light intensity transmitted through the ITO glass substrate is 74.2 mW/cm
2
.
Figure 14 (a) Dependence of fill-factor (FF) on the C
60
:H
2
Pc i-interlayer thickness (
X
) for p–i–n cells incorporating C
60
purified three times by single-crystal formed sublimation (open circles) and for those incorporating C
60
purified by conventional train sublimation under vacuum (open squares). (b) Dependence of short-circuit photocurrent density (
J
sc
) on
X
.
Figure 15 (a) Spectral dependence of the internal quantum efficiency for a cell with
X
= 960 nm. (b) Spectral dependences of the light absorption ratio of cells with
X
= 180 nm (curve A), 600 nm (curve B), and 960 nm (curve C). (c) Photograph of cells with
X
= 180 nm (left) and 960 nm (right).
Figure 16 Negative ion mass spectrum (a) and depth profile (b) of SIMS measurements for C
60
crystal purified three times by single-crystal sublimation.
Figure 17 (a) “Three-sources” co-deposition. MoO
3
and V
2
O
5
, acting as acceptors, and Cs
2
CO
3
acting as a donor were doped in the H
2
Pc:C
60
(1:1) co-deposited film. (b) An example of the total thickness signal from QCM versus time relationship for 9 ppm doping. (c) Rotating slit having an aperture ratio of 1/10.
Figure 18 Energy diagrams of various organic semiconductor films. The middle, lower, and upper located lines show the energetic position of
E
F
for nondoped, MoO
3
-doped, and Cs
2
CO
3
-doped films. The doping concentration is 3000 ppm.
E
F
for the MoO
3
and Cs
2
CO
3
films (100 nm) are also shown.
Figure 19 (Upper) Photographs of C
60
:MoO
3
(1:1) film (a) and C
60
:Cs
2
CO
3
(10:1) film (b). Strong CT absorption was observed. (Middle) Mechanisms of p- and n-type C
60
formation by MoO
3
(a) and Cs
2
CO
3
(b) doping. (Lower) Corresponding mechanisms of p- and n-type Si formation by B (a) and P (b) doping.
Figure 20 Three structures of pn-homojunction C
60
cells. The thickness combinations of the MoO
3
- and Ca-doped layers are 250/750 nm (a), 500/500 nm (b), and 750/250 nm (c). The concentration was kept at 5000 ppm for both dopants.
hν
(ITO) and
hν
(Ag) denote light irradiation onto ITO and onto Ag electrodes, respectively. The locations of the homojunctions are indicated by the arrows.
Figure 21 Action spectra under irradiation onto the ITO electrode (a) (
hν
(ITO)) and onto the Ag electrode (b) (
hν
(Ag)). Curves A, B, and C are for the cells (a), (b), and (c) in Figure 20, respectively. The black curve shows the absorption spectrum of the C
60
film (150 nm).
Figure 22 Energetic structure of pn-homojunction formed after contact measured by Kelvin band-mapping. The doping concentrations of Cs
2
CO
3
and MoO
3
are 500 ppm and 3000 ppm, respectively.
Figure 23 Principle of band-mapping by Kelvin probe. An interface between ITO and a p-type semiconductor film is shown. Work function values (middle figure) corresponding to the double arrows (lower figure) depending on the thickness of the organic semiconductor film were measured by Kelvin probe.
E
vac
,
E
F
, CB, and VB denote the vacuum level, the Fermi level, the conduction band, and the valence band, respectively.
Figure 24 Work function shifts in C
60
films doped with Cs
2
CO
3
and MoO
3
on ITO substrates (triangles and circles). Band-bending was fitted by a quadratic relationship based on the Poisson equation (solid curves).
Figure 25 Dependence of carrier concentration (a) and doping efficiency (b) on doping concentration of Cs
2
CO
3
or MoO
3
.
Figure 26 Orbital of electron around positive charge on ionized donor. (a) P doping in Si. P
+
is represented by the dark grey shaded circle. This situation resembles the Wannier exciton (Figure 4(a)). (b) Cs
2
CO
3
doping in C
60
. Cs
2
CO
3
+
is represented by the dark grey shaded circle. This situation resembles the CT exciton (Figure 5(b)).
Figure 27 Structures of invertible two-layered H
2
Pc/C
60
cells with heavily doped organic/metal interfaces. For the cells in (a) and (b), photogenerated holes and electrons are extracted to ITO and Ag, and to Ag and ITO, respectively. The total thicknesses of the H
2
Pc and C
60
films are kept the same. (c) (d) Current–voltage (
J–V
) characteristics for cells (a) and (b) (curves A). The curves B are for the cells without heavily doped interfaces. The photocurrent and the dark current are shown by solid and broken curves, respectively. The ITO electrode was irradiated with simulated solar light (AM 1.5, 100 mW/cm
2
). Cell performances: (a)
J
sc
: 3.38 mA/cm
2
,
V
oc
: 0.46 V, FF: 0.59, efficiency: 0.91%. (b)
J
sc
: 2.70 mA/cm
2
,
V
oc
: 0.43 V, FF: 0.49, efficiency: 0.57%.
Figure 28 Energy structure for an ITO/n
+
-C
60
contact measured by Kelvin band-mapping. A tunneling junction for photogenerated electrons is formed.
Figure 29 pn-control of H
2
Pc:C
60
co-deposited films by doping.
E
F
shift occurs within the “bandgap of the co-deposited film.”
Figure 30 (a) Organic/organic ohmic n
+
p
+
-homojunction and (b) current–voltage characteristics.
Figure 31 (a) Structure of a p
+
in
+
-homojunction cell fabricated in a C
60
:6T co-deposited film. Ishiyama
et al.
, 2013 [61]. Reproduced with permission of Elsevier. A nondoped intrinsic C
60
:6T layer is sandwiched between heavily doped p
+
and n
+
-C
60
:6T layers. (b) Structure of a tandem cell connecting two unit p
+
in
+
-homojunction cells by ohmic n
+
p
+
-homojunction. Ishiyama
et al.
, 2013 [61]. Reproduced with permission of Elsevier. (c) Current–voltage (
J–V
) characteristics for a single p
+
in
+
-homojunction cell (curves A) and a tandem cell (curves B). The photocurrent and darkcurrent are shown by the solid and the broken curves, respectively. The ITO electrode was irradiated with simulated solar light (AM 1.5, 100 mW/cm
2
). The cell parameters, that is,
J
sc
,
V
oc
, FF, and efficiency were as follows. Curve A: 4.5 mA/cm
2
, 0.85 V, 0.41, 1.6%. Curve B: 3.0 mA/cm
2
, 1.69 V, 0.47, 2.4%.
Figure 32 Energy band diagram of a tandem cell depicted based on Kelvin probe measurements. The shaded region corresponds to the depletion layer for an n
+
p
+
-homojunction acting as an ohmic interlayer. VB and CB denote the valence band and the conduction band, respectively. The bands for C
60
and 6T are shown by the black and orange curves, respectively.
Figure 33 Work functions in the p
+
n
+
-homojunctions. (a) n
+
on p
+
. (b) p
+
on n
+
. The black squares and solid curves are the observed points and the simulated curves, respectively.
W
n
,
W
p
, and
V
bi
denote the depletion layer widths in the n
+
- and p
+
-regions, and the built-in potential, respectively.
Figure 34 Electric potential profile (solid curve) in the p
+
n
+
-homojunction in Figure 33 illustrated based on Poisson's equation. The point
x
= 0 corresponds to the p
+
n
+
interface. The work function change in Figure 33(a) (solid curve) is calculated by adding the potential change that occurred in n
+
-layer (left solid double-headed arrow) and that which occurred in p
+
-layer (left broken double-headed arrow). The work function change in Figure 33(b) (solid curve) is calculated by adding the potential change that occurred in p
+
-layer (right solid double-headed arrow) and that which occurred in p
+
-layer (right broken double-headed arrow).
Figure 35 Energy band diagram of the p
+
n
+
-homojunction illustrated in Figure 33 based on the solid curve in Figure 34. VL,
E
F
, VB, and CB denote the vacuum level, Fermi level, the valence band, and the conduction band, respectively. Φ
n
and Φ
p
are the bulk work functions of n
+
- and p
+
-6T:C
60
. The bands for 6T and C
60
are shown by the light grey and dark grey lines, respectively.
Figure 36 Work functions in the pn-homojunction. (a) n on p. (b) p on n. The black squares and solid curves are the observed points and simulated curves, respectively. (c) Energy band diagram of the pn-homojunction for panels (a) and (b).
Figure 37 Work functions in the pn
+
-homojunctions. (a) n
+
on p. (b) p on n
+
. Inset: Magnified view of the work function shifts within the first 8 nm for n
+
on p. The open squares and solid curves are the observed points and simulated curves, respectively. (c) Energy band diagram of the pn
+
-homojunction for panels (a) and (b).
Figure 38 Energy diagrams of organic semiconductors. The work functions with donor (Cs
2
CO
3
) and acceptor (FeCl
3
) dopants are also shown.
E
F
s of C
60
, C
70
, and H
2
Pc films doped with Cs
2
CO
3
(MR = 0.02) are shown by upper broken lines.
E
F
s of H
2
Pc and Me-PTC films doped with FeCl
3
(MR = 0.03) are shown by lower broken lines.
Figure 39 Film thickness dependences of the work function. (a) H
2
Pc:C
60
(1:1) co-deposited film (circles) and its component single C
60
(diamonds) and H
2
Pc (triangles) films. The Cs
2
CO
3
doping concentration for all the films was MR = 0.005. (b) H
2
Pc:Me-PTC (1:1) co-deposited film (circles) and its component single Me-PTC (squares) and H
2
Pc (triangles) films. The FeCl
3
doping concentration for all of the films was MR = 0.03. The depths of the band-bending are indicated by the arrows. The solid curves are quadratic fits to the band-bending.
Figure 40 Dependence of carrier concentration and doping efficiency on Cs
2
CO
3
doping concentration for H
2
Pc:C
60
(a,b) and H
2
Pc:C
70
(c,d) co-deposited films and their component films.
Figure 41 (a) Energy diagrams of Cs
2
CO
3
-doped H
2
Pc and C
60
single films before contact. Δ
E
D
denotes the activation energy of the donors. Shinmura
et al.
, 2014 [53]. Reproduced with permission of American Institute of Physics. (b) Three-dimensional energy structure for a Cs
2
CO
3
-doped H
2
Pc/C
60
superlattice formed at an ITO interface after contact. Shinmura
et al.
, 2014 [53]. Reproduced with permission of American Institute of Physics. (c) Cross-sectional energy structure of a Cs
2
CO
3
-doped H
2
Pc/C
60
superlattice after contact, cut in parallel to the ITO interface after the end of the band-bending by ITO. The H
2
Pc:C
60
ratios are 1:1 (left) and 99:1 (right). The gray shaded areas show the accumulated electrons in C
60
. (d) Cross-sectional energy structure of a FeCl
3
-doped Me-PTC/H
2
Pc superlattice after contact. The energy relationships are depicted on the same scale in all figures.
Figure 42 (a) Dependence of work function on the film thickness for various ratios of H
2
Pc in H
2
Pc:C
60
co-deposited films doped with Cs
2
CO
3
(MR = 0.02). Shinmura
et al.
, 2014 [53]. Reproduced with permission of American Institute of Physics. (b) Ionization rate for various ratios of H
2
Pc in H
2
Pc:C
60
co-deposited films. Calculated ionization rate is shown by the broken line.
Figure 43 Schematic illustration of electron orbits around a positive ionized donor. The circles A are those for donors in single C
60
and H
2
Pc regions. The semicircle B is that for a donor just on the H
2
Pc side of the H
2
Pc/C
60
molecular interface.
Figure 44 (a) Structure of n
+
p-homojunction cell having a one-sided abrupt junction. Ohashi
et al.
, 2015 [71]. Reproduced with permission of Elsevier. (b) Energy band diagram of n
+
-C
60
:6T co-deposited films doped with Cs
2
CO
3
(10,000 ppm) and that of p-C
60
:6T doped with FeCl
3
(1, 10, 100, 1000 ppm) on an n
+
-layer. The positions of
E
F
are shown by the broken lines. The work functions of Cs
2
CO
3
and FeCl
3
are also shown.
Figure 45 Current–voltage characteristics for n
+
p-homojunction cells having p-layer doping concentrations of 0, 1, 10, 100, and 1000 ppm. The photocurrent and darkcurrent are shown by solid and broken curves, respectively. The maximum efficiency was observed at 100 ppm (
J
sc
: 4.48 mA/cm
2
,
V
oc
: 0.80 V, FF: 0.42, efficiency: 1.51%).
Figure 47 (a) Dependences of
J
sc
and fill-factor (FF) on doping concentration. Ohashi
et al.
, 2015 [71]. Reproduced with permission of Elsevier. (b) Dependences of fill-factor (FF) and cell resistance (
R
s
) on doping concentration. Ohashi
et al.
, 2015 [71]. Reproduced with permission of Elsevier. (c) Dependences of
J
sc
and built-in potential (
V
bi
) on doping concentration. Ohashi
et al.
, 2015 [71]. Reproduced with permission of Elsevier. (d) Dependences of hole concentration (
N
h
) and hole mobility (
μ
h
) on doping concentration.
Figure 46 (a) The dependence of the work function on the p-layer thickness for various doping concentrations. p-layers were formed on n
+
-layers. Ohashi
et al.
, 2015 [71]. Reproduced with permission of Elsevier.(b) Energy band diagrams of n
+
p-homojunction cells with 10, 100, and 1000 ppm FeCl
3
-doped p-layers.
Single Molecule Data Analysis: An Introduction
Figure 1
Single-molecule experiments often generate time traces. The goal is to infer models of single-molecule behavior from these time traces
: (a) A cartoon of a single-molecule force spectroscopy setup probing transitions between zipped and unzipped states of an RNA hairpin [57]. Change-point algorithms, that we later discuss, were used in (b) to determine when the signal suddenly changes (red line). The signal indicates the changes in the conformation of the RNA hairpin obscured by noise. Clustering algorithms, also discussed later, were then used to regroup the “denoised” intensity levels (red line) into distinct states (blue line).
Figure 2
The posterior probability sharpens as more data are accumulated
. Here we sampled data according to a Poisson distribution with (designated by the dotted line). Our samples were . We plotted the prior (Eq. (20) with , ) and the resulting posterior after collecting , then and points.
Figure 3
Venn diagram depicting different information quantities and their relationship
. The value of each entropy is represented by the enclosed area of different regions. and are both complete circles.
Figure 4
FCS may be used to model the dynamics of labeled particles at many cellular locations (regions of interest (ROIs)), both in the cytosol and in the nucleus
. (a) Merged image of a cerulean-CTA fluorescent protein (FP) used to image the cytosol and mCherry red FP used to tag BZip protein domains. In Ref. [5], we analyzed FCS data on tagged BZips diffusing in the nucleus and the cytosol. We analyzed diffusion in ROIs far from heterochromatin by avoiding red FP congregation areas (bright red spots). MaxEnt analysis revealed details of the fluorophore photophysics, crowding, and binding effects that could otherwise be fit using anomalous models. (b) A cartoon of the cell nucleus illustrating various microenvironments in which BZip (red dots) diffuses (A: free region, B: crowded region, C: nonspecific DNA-binding region, and D: high-affinity binding region). Tsekouras
et al
., 2015 [5]. Reproduced with permission of Elsevier. (
See color plate section for the color representation of this figure
.)
Figure 5
Protein-binding sites of different affinities yield a
that is well fit by an anomalous diffusion model
. A theoretical (containing 150 points) was created from an anomalous diffusion model, Eq. (52) with , to which we added white noise (a, dark gray dots, logarithmic in time). Using MaxEnt, we infer a from this (b) and, as a sanity check, use it to reconstruct a (a, solid curve). In the main body, we discuss how protein-binding sites of different affinities could give rise to such a . Part of is then excised, yielding a new (d, solid curve). Conceptually, this is equivalent to mutating a binding site, which eliminates some 's. We created a from this theoretical distribution with white noise (c, dark gray dots, logarithmic in time). We then extracted a from this (d, dark gray curve), and we reconstructed a from this distribution as a check (c, solid curve). Time is in arbitrary units.
Figure 6
Probability distributions of diffusion coefficients can be inferred from FCS curves
. (a) for freely diffusing Alexa568 shows no “superdiffusive plateau” (defined in the text) that arises from dye flickering. Rather, it shows its main peak at 360 very near the reported value of 363 [165]. We attributed the smaller peak centered at to dye aggregation [5]. (b) and (c) We analyzed 's obtained from FCS data acquired on mCherry and mRuby2 diffusing freely in solution, and (d) and (e) mCherry- or mRuby2-tagged BZip protein domains in the cytosol and (f) and (g) the nucleus far from heterochromatin [165]. Black curves are averages of the red curves (total number of data sets: b:3, c:9, d:5, e:16, f:7, and g:21). The additional blue curve in (g) shows the analysis of the best data set (i.e., the most monotonic ). See text and Ref. [5] for more details.
Figure 7
The AIC and BIC are often both applied to step-finding
. (a) We generated 1000 data points with a background noise level, . On top of the background, we added six dwells (five change points) with noise around the signal having a standard deviation of (see inset). At this high noise level, and for this particular application, the BIC outperforms the AIC and the minimum of the BIC is at the theoretical value of 5 (dotted line). All noise is Gaussian and decorrelated. (b) For our choice of parameters, the AIC (green) finds a model that overfits the true model (black) while the BIC (red) does not. However, as we increase the number of steps (while keeping the total number of data points fixed), the AIC does eventually outperform the BIC. This is to be expected. The AIC assumes the model could be unbounded in complexity and therefore does not penalize additional steps as much. The BIC, by contrast, assumes that there exists a true model of finite complexity. We acknowledge K. Tsekouras for generating this Figure (Note: Colored Figure available in online version)
Figure 8
Noise models can be adapted to treat outliers
. We are given a sequence of data points, . We want to find the posterior over . Blue: We assume the standard deviation is fixed at 0.25 and use a Gaussian likelihood with a single variance for all points. Orange: We assume that the standard deviation's lower bound is 0.25, see Eq. (65), but that we still have a single variance for all points. Green: We still assume the standard deviation's lower bound is 0.25 but that all points are assumed to have independent standard deviations, see Eq. (66). (Note: Colored Figure available in online version)
Figure 9
BIC finds correct steps when the noise statistics are well characterized
. (a) Our control. We generated synthetic steps (black line) and added noise (white, decorrelated) with the same standard deviation for each data point. We used a greedy algorithm [59] to identify and compare models according to Eq. (73) and identify the correct step locations (red line) from the noisy time trace (blue). (b) Here we use a different, incorrect, likelihood that does not adequately represent the process that we used to generate the synthetic data. That is, we correctly assumed that the noise was white and decorrelated but also, incorrectly, assumed that we knew and fixed (and therefore did not integrate over in Eq. (71)). We underestimated by 12%. Naturally, we overfit (red) the true signal (black). Green shows the step-finding algorithm rerun using the correct noise magnitude. (c) Here we use the BIC from Eq. (73) whose likelihood assumes no noise correlation. However, we generated a signal (black) to which we added correlated noise (by first assigning white noise, , to each data point and then computing a new correlated noise, , at time from ). As expected, the model that the BIC now selects (red) interprets as signal some of the correlated noise from the synthetic data. We acknowledge . Tsekouras for generating this Figure (Note: Colored Figure available in online version)
Figure 10
Identifying states can be accomplished while detecting steps
. STaSI is applied to synthetic smFRET data. STaSI works by first iteratively identifying change points in the data (successive steps shown by arrows in panel (a). The mean of the data from change point to change point defines an intensity (FRET) state. An MDL heuristic is subsequently used to eliminate (or regroup) intensity levels (b). The MDL is plotted as a function of the number of states (c). The final analysis – with change points and states identified – is shown in (d).
Figure 11
Maximum evidence can be used in model selection
. (a) For this synthetic time trace, maximum likelihood (ML) will overfit the data. This is clear from (b) where it is shown that the log likelihood or probability of the model – evaluated at – increases monotonically as we increase the number of states, . By contrast, maximum evidence (ME) – obtained by marginalizing the likelihood over – identifies the theoretically expected number of states, . Sample time traces are shown in (a) and the log probability is plotted in (b).
Figure 12
The number of diffusive states detected using maximum evidence can establish changes in interactions of Hfq upon treatment of E. coli cells with rifampicin
. (a) vbSPT analysis of the RNA helper protein Hfq tracking data. Three distinct diffusive states are detected, and sample trajectories are shown color-coded according to which state they belong. The kinetic scheme shows the diffusion coefficient in each state as well as transition rates between diffusion coefficients. (b) When treated with a transcription inhibitor (rif), vbSPT finds that the slowest diffusive state vanishes suggesting that the slowest diffusive state of Hfq was related to an interaction of Hfq with RNA. Hz throughout the Figure The scale bar indicates 0.5 . See details in Ref. [35]. Persson
et al
., 2013 [35]. Reproduced with permissions of Nature Publishing Group. (
See color plate section for the color representation of this figure
.)
Figure 13
DPMMs can be used in deconvolution
. (a) A density generated from data points from the mixture of four exponential components. (b) After fewer than 200 MCMC iterations, the DPMM has converged to four mixture components. (c) The marginal distribution of the parameter for each mixture component is shown with the red line indicating the theoretical value used to generate the synthetic data . Hines
et al
., 2015 [133]. Reproduced with permissions of Elsevier. (Note: Colored Figure available in online version)
Figure 14 iHMM graphical model [256].
Figure 15
iHMM's can learn the number of states from a time series
. iHMMs not only parametrize transition probabilities as normal HMMs do. They also learn the number of states in the time series [133]. Here they have been used to find the number of states for (a) ion (BK) channels in patch clamp experiments (with downward current deflections indicating channels opening) and (b) conformational states of an agonist-binding domain of the NMDA receptor. Hines
et al
., 2015 [133]. Reproduced with permissions of Elsevier. (
See color plate section for the color representation of this figure
.)
Figure 16
A soft clustering algorithm based on RDT is used to determine states from smFRET trajectories
. (a) A crystal structure of the AMPA ABD. The green and red spheres represent the donor and acceptor fluorophores, respectively. (b) Detection of photons emitted in an smFRET experiment. (c) An experimental smFRET trajectory obtained by binning the data in (b). (d) Probability mass functions (pmfs) of the blue and red segments highlighted in (c). (e) Cumulative distribution function (cdfs) of the highlighted segments in (c). The shaded area represents the Kantorovich distance. (f) Visual representation of clusters in (c) based on multidimensional scaling. (g) Transition disconnectivity graph (TRDG) resulting from the trajectory in (c). Taylor
et al
., 2015 [46], http://www.nature.com/articles/srep09174?WT.ec_id=SREP-639-20150324. Used under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/. (
See color plate section for the color representation of this figure
.)
Figure 17
RDT clustering reveals differences in conformational dynamics for the AMPA ABD
. State distributions and TRDGs are given for the full agonist-bound ABD (a), the partial agonist-bound ABD (b), and the antagonist-bound ABD (c). denotes the mean efficiency. Taylor
et al
., [46], http://www.nature.com/articles/srep09174?WT.ec_id=SREP-639-20150324. Used under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/. (
See color plate section for the color representation of this figure
.)
Figure 18
The IB method can be used to construct dynamical models
. (a) The IB method starts from the data to be clustered (top left), clustering then compresses the information contained in by minimizing the rate (from top left to top right). Instead of introducing an a priori distortion measure, the IB compression maximizes quantifying how well another observable, , is predicted (from top right to bottom). The maximum achievable “relevance,” predicting from , is given by . (b) To construct a predictive dynamical model from time series data, we may define past sequences (top left) as the data to be clustered and future sequences (bottom) as the relevant observables .
Figure 19
Diminishing returns: most data collected from additional experiments does not result in information gain
. The expected information gained, Eq. (100), grows sublinearly with the number of photon arrival measurements.
Chapter 5: Chemistry With Controlled Ions
Figure 1 Linear radiofrequency (RF) ion traps and Coulomb crystals. (a) Schematic representation of a linear RF ion trap consisting of four cylindrical electrodes arranged in a quadrupolar configuration. The electrodes are sectioned into segments for the application of RF voltages and static voltages to allow the confinement of charged particles in the center of the trap. (b) Laser-cooling scheme for . See text for details. (c) Fluorescence images of Coulomb crystals of laser-cooled ions. Left: a single ion, right: a large spheroidal Coulomb crystal consisting of several hundred ions. (d) Bicomponent Coulomb crystal consisting of laser-cooled ions and sympathetically cooled ions. The ions form a nonfluorescing string of ions in the center and are only indirectly visible as a dark core of the crystal.
Figure 2 Preparation of molecular ions in selected rotational and vibrational states. (a) [2+1′] multiphoton threshold-photoionization scheme in from Ref. [58]. and denote the rotational quantum numbers in the neutral and ionic states, respectively. An initial two-photon excitation step selects a specific rotational level (here with rotational quantum number ) in the excited electronic state. Photoionization with an additional photon just above the lowest accessible rotational ionization threshold (corresponding to the cationic rotational ground state in this case) results in the generation of rotationally state-selected molecular ions. Tong
et al.
, 2010 [58]; Tong
et al.
, 2011 [69]. Reproduced with permissions of American Physical Society. (b) Rotational laser cooling in using an infrared laser irradiating the vibrational-rotational transition as implemented in Ref. [70]. The interplay between optical pumping on this transition (thin solid arrow), fluorescence from the upper level back to rotational levels of the ground state (wavy arrows), and population redistribution between the rotational levels of the ground vibrational state by black-body radiation (BBR, thick solid arrows) leads to an accumulation of the population in the absolute ground state . See text for details.
Figure 3 Principle of Stark deceleration. (a) Schematic representation of a Stark deceleration experiment consisting of a gas nozzle for producing a pulsed supersonic molecular beam which is coupled into an array of pairs of cylindrical electrodes. (b) Alternating pairs of electrodes are switched to high voltage creating a potential-energy barrier for molecules in low-field seeking Stark states. Thus, the molecules are slowed down when approaching the electrodes. (c) When the molecules approach the top of the barrier, the fields are switched off and applied to the next pair of electrodes. Repeated application of this scheme results in a gradual deceleration of the packet of molecules along the decelerator.
Figure 4 Reactions of cold ions with velocity-selected molecules. (a) Schematic of an experimental setup for studying translationally cold ion–molecule reactions. An electrostatic quadrupole-guide velocity selector produces a continuous beam of translationally cold polar molecules. The molecular beam enters a linear RF ion trap containing Coulomb crystals of cold ions. Laser beams for the cooling and generation of ions are labeled by their wavelengths 397, 866, and 355 nm. Willitsch
et al.
, 2008 [47]. Reproduced with permissions of American Physical Society. (b) Translational energy distributions of various polar molecules which have been velocity-filtered by the electrostatic guide at a voltage of 4 kV applied to the quadrupole electrodes. Inset: Averaged signal of polar molecules detected after the velocity filter at different quadrupole voltages. Bell
et al.
, 2009 [92]. Reproduced with permissions of The Royal Society of Chemistry. (c) Rates of chemical reactions between cold ions and cold F molecules determined by monitoring the decrease of the volume of the Coulomb crystals, reflecting the decrease of the number of ions, as a function of the reaction time. The bimolecular rate constant of the reaction is determined from a fit (solid line) of the experimental data to an integrated pseudo-first-order rate law; see text for details.
Figure 8 Conformationally controlled ion–molecule reactions. (a) Experimental setup consisting of an electrostatic deflector for conformers with different dipole moments entrained in a supersonic molecular beam. The inset on the left-hand side shows the electric field generated by the deflector electrodes. The spatially separated conformers (represented by the spheres) are directed at a tightly localized reaction target consisting of an ion Coulomb crystal in a linear RF trap. The inset on the right-hand side shows a Coulomb crystal of ions before and after reaction with conformers of 3-aminophenol. By tilting the molecular beam assembly, reaction rates can be determined as a function of the coordinate of deflection for the individual conformers enabling to extract conformer-specific rate constants. (b) Density profiles of the deflected
cis
- and
trans
3-aminophenol (AP) conformers as a function of the deflection coordinate at a deflector voltage of kV. (c) Bimolecular rate constant for reactions of
cis
/
trans
-AP with laser-cooled ions as a function of the deflection coordinate. denotes the number density of the relevant conformers in the beam. The total rate constant is about a factor of 2 larger at deflection coordinates at which the
cis
conformer prevails, indicating a higher reactivity for the
cis
-species. See text for details.
Figure 5 Cold ion–atom reactions. (a) Schematic of an ion-atom hybrid-trapping experiment consisting of a linear RF trap for the laser and sympathetic cooling of ions integrated into a magneto-optical trap (MOT) for the laser cooling and trapping of neutral atoms, in the present case Rb. The MOT consists of two coils situated above and below the ion trap which are operated in an anti-Helmholtz configuration to create a quadrupolar magnetic field. In combination with six laser beams at 780 nm arranged in an optical-molasses configuration, the atoms are laser-cooled and confined to the center of the trap. The circular inset shows a fluorescence image of two laser-cooled ions immersed into a cloud of trapped ultracold Rb atoms. Hall
et al.
, 2013 [126]. Reproduced with permissions of Taylor & Francis. (b) Rate constant as a function of the collision energy for reactions of ions with Rb atoms in the excited state predicted by classical capture theory taking into account the charge-induced dipole (CID, Langevin, lower trace) and charge-induced dipole plus charge–quadrupole (CQ) interactions (upper trace). The inset shows a comparison of the predicted CID + CQ capture rate constants with experimental data. See text for details. Adapted from Ref. [121].
Figure 6 Single-ion reaction experiments. (a) A Coulomb crystal of two laser-cooled ions prepared in the hybrid-trapping experiment shown in Figure 5 (a). (b) The same crystal after exposure to a cloud of ultracold Rb atoms. One of the ions has undergone a charge-transfer reaction and has been replaced by a nonfluorescing ion (position indicated by the circle). The presence of the product ion manifests itself by the displacement of the remaining ion from the trap center (labeled with an X) by the Coulomb repulsion between the ions. (c) Histogram of the time-to-reaction of ions with Rb obtained from 49 single-ion reaction experiments. The solid line shows a fit to a pseudo-first-order rate law.
Figure 7 (a) Collision- and state-controlled ion–molecule reactions. (a) Schematic of the experimental setup. ions were prepared in their rotational–vibrational ground state using the threshold-photoionization scheme shown in Figure 2(a) and were sympathetically cooled into a Coulomb crystal of laser-cooled ions (inset). The state-selected ions were exposed to rotationally cold neutral molecules introduced into the ion trap via a doubly skimmed supersonic molecular beam. (b) Dynamics of the populations in different spin-rotational states of sympathetically cooled ions as a function of the time of exposure to the molecular beam of neutral molecules. The populations in the different states have been probed by laser-induced charge transfer (LICT) with Ar atoms. See text for details.
List of Tables
Chapter 3: Energetic and Nanostructural Design of Small-Molecular-Type Organic Solar Cells
Table I Distance for Which 90% of Excitons can Reach a D/A Heterointerface
Table II Ionization Rates (Doping Efficiencies) of Co-deposited Films and Their Component Single Films and The Factor by Which the Doping Sensitization Has Increased for Donor-doped H
2
Pc:C
60
and Acceptor-doped H
2
Pc:Me-PTC Systems
Single Molecule Data Analysis: An Introduction
Table I RDT clustering returns state properties for the AMPA ABDs
Advances in Chemical Physics
Volume 162
Edited by
Stuart A. Rice
The University of Chicago, Illinois, US
Aaron R. Dinner
The University of Chicago, Illinois, US
This edition first published 2017
© 2017 John Wiley & Sons, Inc.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
The right of Stuart A Rice and Aaron R Dinner to be identified as the editors of this work has been asserted in accordance with law.
Registered Office
John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA
Editorial Office
111 River Street, Hoboken, NJ 07030, USA
For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.
Wiley also publishes its books in a variety of electronic formats and by print-on-demand. Some content that appears in standard print versions of this book may not be available in other formats.
Limit of Liability/Disclaimer of Warranty
In view of ongoing research, equipment modifications, changes in governmental regulations, and the constant flow of information relating to the use of experimental reagents, equipment, and devices, the reader is urged to review and evaluate the information provided in the package insert or instructions for each chemical, piece of equipment, reagent, or device for, among other things, any changes in the instructions or indication of usage and for added warnings and precautions. While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
Library of Congress Catalog Number: 58-9935
ISBN: 9781119324577
Cover design: Wiley
Cover image: Courtesy of Stefan Willitsch. From “Specific Chemical Reactivities of Spatially Separated 3-Aminophenol Conformers with Cold Ca+ Ions,” by Yuan-Pin Chang, et al., published in Science (volume 342, issue 6154, October 2013). Reprinted with permission from AAAS.
Editorial Board
Kurt Binder, Condensed Matter Theory Group, Institut Für Physik, Johannes Gutenberg-Universität, Mainz, Germany
William T. Coffey, Department of Electronic and Electrical Engineering, Printing House, Trinity College, Dublin, Ireland
Karl F. Freed, Department of Chemistry, James Franck Institute, University of Chicago, Chicago, IL, USA
Daan Frenkel, Department of Chemistry, Trinity College, University of Cambridge, Cambridge, UK
Pierre Gaspard, Center for Nonlinear Phenomena and Complex Systems, Université Libre de Bruxelles, Brussels, Belgium
Martin Gruebele, Departments of Physics and Chemistry, Center for Biophysics and Computational Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
Gerhard Hummer, Theoretical Biophysics Section, NIDDK-National Institutes of Health, Bethesda, MD, USA
Ronnie Kosloff, Department of Physical Chemistry, Institute of Chemistry and Fritz Haber Center for Molecular Dynamics, The Hebrew University of Jerusalem, Jerusalem, Israel
Ka Yee Lee, Department of Chemistry, James Franck Institute, University of Chicago, Chicago, IL, USA
Todd J. Martinez, Department of Chemistry, Photon Science, Stanford University, Stanford, CA, USA
Shaul Mukamel, Department of Chemistry, School of Physical Sciences, University of California, Irvine, CA, USA
Jose N. Onuchic, Department of Physics, Center for Theoretical Biological Physics, Rice University, Houston, TX, USA
Stephen Quake, Department of Bioengineering, Stanford University, Palo Alto, CA, USA
Mark Ratner, Department of Chemistry, Northwestern University, Evanston, IL, USA
David Reichman, Department of Chemistry, Columbia University, New York City, NY, USA
George Schatz, Department of Chemistry, Northwestern University, Evanston, IL, USA
Steven J. Sibener, Department of Chemistry, James Franck Institute, University of Chicago, Chicago, IL, USA
Andrei Tokmakoff, Department of Chemistry, James Franck Institute, University of Chicago, Chicago, IL, USA
Donald G. Truhlar, Department of Chemistry, University of Minnesota, Minneapolis, MN, USA
John C. Tully, Department of Chemistry, Yale University, New Haven, CT, USA
List of Contributors Volume 162
Timothy C. Berkelbach, Department of Chemistry, and The James Franck Institute, The University of Chicago, Chicago, IL 60637, USA
Paul Brumer, Chemical Physics Theory Group, Department of Chemistry, and Center for Quantum Information and Quantum Control, University of Toronto, Toronto, ON, Canada M5S 3H6
Masahiro Hiramoto, Department of Materials Molecular Science, Institute for Molecular Science, National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji, Okazaki 444-8787, Aichi, Japan
Tamiki Komatsuzaki, Research Institute for Electronic Science, Hokkaido University, Kita 20 Nishi 10, Kita-Ku, Sapporo 001-0020, Japan
Chun-Biu Li, Department of Mathematics, Stockholm University, 106 91 Stockholm, Sweden; Research Institute for Electronic Science, Hokkaido University, Kita 20 Nishi 10, Kita-Ku, Sapporo 001-0020, Japan
Steve Pressé, Department of Physics and School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA; Physics Department, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA; Department of Chemistry and Chemical Biology, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA; Department of Cell and Integrative Physiology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
Torsten Scholak, Chemical Physics Theory Group, Department of Chemistry, and Center for Quantum Information and Quantum Control, University of Toronto, Toronto, ON, Canada M5S 3H6
Meysam Tavakoli, Physics Department, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA
J. Nicholas Taylor, Research Institute for Electronic Science, Hokkaido University, Kita 20 Nishi 10, Kita-Ku, Sapporo 001-0020, Japan
Stefan Willitsch, Department of Chemistry, University of Basel, Klingelbergstrasse 80, 4056 Basel, Switzerland
Preface to the Series
Advances in science often involve initial development of individual specialized fields of study within traditional disciplines followed by broadening and overlap, or even merging, of those specialized fields, leading to a blurring of the lines between traditional disciplines. The pace of that blurring has accelerated in the past few decades, and much of the important and exciting research carried out today seeks to synthesize elements from different fields of knowledge. Examples of such research areas include biophysics and studies of nanostructured materials. As the study of the forces that govern the structure and dynamics of molecular systems, chemical physics encompasses these and many other emerging research directions. Unfortunately, the flood of scientific literature has been accompanied by losses in the shared vocabulary and approaches of the traditional disciplines, and there is much pressure from scientific journals to be ever more concise in the descriptions of studies, to the point that much valuable experience, if recorded at all, is hidden in supplements and dissipated with time. These trends in science and publishing make this series, Advances in Chemical Physics, a much needed resource.
The Advances in Chemical Physics
