Rules of molecular self-organization
Volltext
(2) . . .
(3) RulesofMolecularSelfOrganization: Emergence,ControlandPredictability Dissertation amLehrstuhlfürExperimetalphysik Prof.J.O.Rädler FakultätfürPhysik LudwigMaximiliansUniversitätMünchen CarstenL.Rohr Juni2011 . .
(4) Betreuer:Dr.B.Hermann Erstgutachter:Prof.Dr.JoachimO.Rädler Zweitgutachter:PDDr.MarkusLackinger DatumderPrüfung:15.6.2011 .
(5) . Contents ZUSAMMENFASSUNG. III. ABSTRACT. V. 1. 1. INTRODUCTION 1.1 MISSINGRULESANDPREDICTABILITY 1.2 AIMSOFTHETHESIS 1.3 STRUCTUREOFTHETHESIS. 2. 1 3 4. SELFORGANIZATION. 5. 2.1 CONCEPT 2.2 MOLECULARSELFORGANIZATION. 5 6. 3 3.1 3.2 3.3 3.4 3.5 3.6 4. SCANNINGTUNNELINGMICROSCOPY. 9. SCANNINGTUNNELINGMICROSCOPY TUNNELTHEORY TUNNELINGTHROUGHMOLECULARADLAYERS SUBSTRATES SOLUTIONCASTING IMAGEPROCESSING. 9 13 17 19 20 22. MOLECULARSYSTEMS. 25. 4.1 SUPRAMOLECULARCHEMISTRY 4.2 FRÉCHETDENDRONS 5 5.1 5.2 5.3 5.4. 25 26. COMPLEXENERGYLANDSCAPES. 29. ENERGYLANDSCAPES METASTABLESTATES PHASETRANSFORMATIONS EMERGENCE. 29 30 31 31. . .
(6) Contents 6. COMPUTATIONALTOOLS 6.1 FORCEFIELDSINMOLECULARMECHANICSSIMULATIONS 6.2 DENSITYFUNCTIONALTHEORYSIMULATIONS 6.3 INTERACTIONSITEMODELINMONTECARLOSIMULATIONS. 7. CONCEPTSOFSELFORGANIZEDMOLECULES 7.1 2DSYMMETRYGROUPS 7.2 CHIRALITY 7.3 HIERARCHICALORGANIZATION. 33 34 35 38 41 41 45 46. 8. SUMMARIESOFPUBLICATIONS. 49. 9. PUBLICATIONS. 55. 9.1 SIZEMATTERSINOSTWALD´SRULEOFSTAGES:MULTIPHASETRANSFORMATIONSCAUSEDBY STRUCTURALCOMPLEXITY 55 9.2 MOLECULARJIGSAW:PATTERNDIVERSITYENCODEDBYELEMENTARYGEOMETRICAL FEATURES 59 9.3 MOLECULARSELFORGANIZATION:PREDICTINGTHEPATTERNDIVERSITYANDLOWEST ENERGYSTATEOFCOMPETINGORDERINGMOTIFS 67 9.4 PREDICTINGTHEINFLUENCEOFAP2SYMMETRICSUBSTRATEONMOLECULARSELF 75 ORGANIZATIONWITHANINTERACTIONSITEMODEL 9.5 SIMULATINGPATTERNSFORMOLECULARSELFORGANIZATIONONSURFACESUSING INTERACTIONSITEMODELS 91 9.6 MULTIHIERARCHICALASSEMBLYVIAMOLECULARFLEXIBILITY 101 9.7 SURFACECONTROLOFCHIRALITY,ORIENTATIONANDHIERARCHICALASSEMBLYOFSELF 109 ORGANIZEDMONOLAYERS 9.8 AVERSATILEFRÉCHETDENDRONCOMPOUNDUNIFIESHOSTGUESTANDTEMPLATED HETEROGENEOUSSELFASSEMBLY 115 10. PERSPECTIVES. 10.1 FROMPREDICTIONANDCOMPLEXITYINSELFORGANIZATION 10.2 TOWARDSDESIGNANDFUNCTIONALITY. 125 127. REFERENCES. 129. APPENDIX. 141. A B. BOOKCHAPTER:DIESUCHENACHDEMTIEFSTENTAL:SELBSTORGANISATIONVON MOLEKÜLENINGESCHLOSSENENSYSTEMEN ORGANICSUPERCONDUCTORSREVISITED. ACKNOWLEDGMENTS. . 125. 141 165 173.
(7) . Zusammenfassung ò ¡ Ǥ ± Ǧǡ ǡǤò ò òǦ ǦǦ
(8) ǦǤÚ Ú ¡ǡ Ú Ǥ ǣ 1. Verstehen und Kontrolle der Eigenschaften und Phasenvielfalt von Fréchet Dendronen x ¡ ò ¡ ǡ Úé ¡ Ǥ ¡¡ ͳͲͲ ¡ ¡ǡ Ǥ x ± Ǧ ò Ǧ Ǧ ¡ òǤ ǡ ò Ǥ ¡ òǤ 2. EntwicklungeinesMonteCarloModellszurMustervorhersage x ± Ǧ Ǧ Ǥ ǦǦ ǡ .
(9) o Ǥ x Ǧ ǡ ¡ Ǥ ǡ ò ǡ Ǥ x pʹǦ ǡ ¡ Ǥ Ǧ òǤ 3. Auffinden von allgemeinen Beziehungen zwischen molekularem Baustein und resultierendemMuster x ǡ ǡ ò Ǥ ò ʹǦǤ x ò ǡ ¡ ǡ Ǥ x ¡ ¡ ¡ǡ ¡Ǥ ò ò Ú ͻǤ . .
(10) . Abstract ǡʹ Ǧ ± Ǥ Ǧ ǡ ǡ ǡ Ǥ ǦǤ Ǧǡ ǡ ǡ Ǥ ± Ǧ Ǥ Ǧ ʹ Ǧ Ʋ Ǥ Ǥ
(11) Ǧ ± ǡ Ǥ
(12) ± ǦǤ Ǥ ± Ǥ .
(13) ǡǡ Ǥ Ǧ Ǥ Ǥ n Ʋ Ǥ ǡ Ǥ Ǥ ǡ Ǧ Ǥ. .
(14) 1. Introduction. 1.1 Missingrulesandpredictability Ǧ ǡ ǡ ȏͳȐǤ Ǧ Ǥ “…selfassemblyisoneofthefewpracticalstrategiesformakingensembles ofnanostructures.ItwillthereforebeanessentialpartofnanotechnologyǤdz ǤȏʹȐ ȏ͵ȐǡǡȏͶȐ ǡȏͷȐ ȏȐǤ
(15) ʹȏȐǤ
(16) ȏͺǦͳͶȐǡ ȏͳͷȐǤ DzOnce the mechanisms controlling the selfordering phenomena are fully understood,theselfassemblyandgrowthprocessescanbesteeredtocreate a wide range of surface nanostructures from metallic, semiconducting and molecularmaterials.dz Ǥ Ǧǡȏ͵ȐǤ Ǧ ǡunderstandingǡǤ ͳ.
(17) Ph.D.ThesisCarstenRohr ȏͳȐ ȏͳȐ Ǥ ʹͲͲͺǣ DzWhenfacedwiththequestionhowagivenmoleculeisgoingtoadsorbona wellknown surface, the answer is often vague and most often the “Idon’t know”approachisthemosthonestoneǤdz Ǥ ǦȏͳͺȐ Ǥ Ǧ ȏͳͻǦʹͳȐǤ ȏͳȐ Ǥ Ǧ ȏʹʹȐǤ Ǧ Ǧ Ǧȏʹ͵ǡ ʹͶȐ ȏʹͷǦʹȐ after ǡ Ǥ ȏʹͺǡ ʹͻȐ ȏʹͷǦʹȐ Ǥ Ǥ “… the increasing complexity of the assembly units used makes it generally more difficult to control the supramolecular organization and predict the assembling mechanisms. This creates a case for developing novel analysis methodsandevermoreadvancedmodelingtechniques” Ǧȏ͵ͲȐ ǦǤ Ǧ Ǥ Ǥ DzEngineeringisnoteasy.Itrequiresasetofruleswhichallowspredictingthe outcomeoftheselfassemblyprocesswithahighdegreeofreliability.” Ǥ ǦȏͳͺȐ ʹ.
(18) Chapter1:Introduction “Through progressive discovery, understanding, and implementation of the rules that govern the evolution from inanimate to animate matter and beyond,wewillultimatelyacquiretheabilitytocreatenewformsofcomplex matter.” ǤǤǡȏͶȐ ǡ ȏ͵ͳǦ ͵͵Ȑ ȏ͵ͶȐ Ǧ ȏ͵ͷǦͶͲȐǤ ǣ ͳǤ Ǧǫ ʹǤ
(19) ǫ ͵Ǥ Ǧǫ. 1.2 AimsoftheThesis ͳǤ Understand the mechanisms behind the phase variety and complex phase transformationsobservedinFréchetDendrons. ± Ǧ Ǥ Ǥ Ǥ Ǥ ǦǤ ʹǤ Develop in cooperation a simulation model capable of predicting the pattern varietyoftheFréchetDendronsystem. Ǧ Ǥ
(20) Ǥ Ǥ Ǥ ± Ǥ ͵.
(21) Ph.D.ThesisCarstenRohr ͵Ǥ Abstract rules of selforganization from the complex behavior of Fréchet Dendronswhicharealsoapplicabletoothermolecules. Ǧǡ ǡ Ǥ ± Ǥ Ǧ Ǥ. 1.3 StructureoftheThesis ǡ Ǥ ǡ ǡ Ǥ Ǥ
(22) Ǧ Ǥ ǡ ȋȌ Ǥ ± Ǥ ǡ Ǧ Ǥ ǡ Ǥ
(23) ǡǦǡ ǡ Ǥ
(24) Ǥ Ǥ Ǥ Ǧ Ǥ ǦǤ
(25) Ǥ ǡǤ Ͷ.
(26) 2. SelfOrganization. 2.1 Concept ǤǦ ȏʹȐǡ Ǧ Ǥ ǡ ǤǦǤ
(27) Ǧ ǡ ǡ ǡ Ǧ ǡ Ǥ͵Ǥ Ǧ ȏͳȐǣ DzSelforganization is a process in which pattern at the global level of a system emergessolelyfromnumerousinteractionsamongthelowerlevelcomponentsof the system. Moreover, the rules specifying interactions among the system's components are executed using only local information, without reference to the globalpattern.Inshort,thepatternisanemergentpropertyofthesystem,rather than a property imposed on the system by an external ordering influence.” Ǥ Ǥ
(28) ǤǦ ǡ Ǥ Ǥ ȋ ǡ ǤȌ Ǥ ͷ.
(29) Ph.D.ThesisCarstenRohr ǡ ǡ Ǥ ǡ ͷǤͶǤ. Figure 1. Fish schools and individual directives: Ǧ ǤȌ Ǥ Ȍ Ǥ Ȍ Ǥ Ǥ Ǥ Ǥ ǡͳǤͳǤ ȋ ȌǤ ʹǤ ȋ Ȍ Ǥ ͵Ǥ Ǥͳ Ǥ
(30) ǡǦ Ǥ . 2.2 MolecularSelfOrganization Ǧ Ǥ Ǧ ǡǦ ǡ . ͳ ǡ Ǥ. Ǥ. .
(31) Chapter2:SelfOrganization Ǥ
(32) ǡǦ Ǥ Ǧ ǡ Ǥ
(33) Ǥ ǡ Ǥ ǡ Ǥ Ǥ ǡ ǡ ǡʹǤ. Figure 2. Molecular selforganization: Ǧ Ǥ Ȍ Ǥ Ȍ Ǥ Ȍ Ǥ Ǧ ǡ Ǥ
(34) Ǧǡ Ǥ ǤͳǤ Ǧ ǡ Ǧ Ǥ ǡ ȏͶȐǤ . .
(35) Ph.D.ThesisCarstenRohr Ǥ ǦǤ ȏͶͳǡͶʹȐǤ Ǧ͵Ǥ Ǥ
(36) ǡ ǡ͵Ǥ ǡ͵ Ǥ. Figure 3. Information and DNA selfassembly:
(37) Ǧ Ǥ Ȍ Ǥ Ȍ Ǧ Ǥ Ȍ Ǥ. Ǧ ȏͶ͵Ȑ Ǧ Ǥ Ǧ Ǧǡ Ǥ Ǧ Ǧ ǡ Ǥ . ͺ.
(38) 3. ScanningTunnelingMicroscopy. 3.1 ScanningTunnelingMicroscopy
(39) ͳͻͺͳ ȋȌ Ǥ Ǥ ȏͶͶȐǤ Ǥ ȏͶͷǡ ͶȐǡ ȏͶȐǡ ȏͶͺȐǡ ȏͷȐǡ ȏͶͻǦͷͳȐǡ ȏͷʹȐ ȏͷ͵ȐǤ ǤU Ǥ Ǥ ǡ ǡ Ǥ
(40) ǡ Ǥ Ȃ ǡ ͵ǤʹǤ ʹǡ ǡ Ǥ ͳͲǦͻ ͳͲǦͳʹǤ Ǧ Ȃ Ǧ Ǥ Ǥ Ǧ ǡ . ʹ. ͳΨ ͳǤ. ͻ.
(41) Ph.D.ThesisCarstenRohr Ǥ Ǥ
(42) Ǧ ǦȏͷͶȐǡͶǤ. Figure4.Thescanningtunnelingmicroscope:U ǡ IǤ ǡ Ǥ I ǡ Ǥ
(43) ǤǦ ǡ Ǥ Ǥ ǡ Ǥ I ǦǤ
(44) ǦǦ ȋ
(45) Ȍ Ǧ Ǥ ǡ KPǡKIKDǡͷǤ. ͳͲ.
(46) Chapter3:ScanningTunnelingMicroscopy. Figure 5. The feedback loop (PID): Ǥ P ǤI ǡD Ǥ KP ǡe(t)Ǥu(t) Ǧ Ǧ Ǥ ݑሺݐሻ ൌ ܭ ή ݁ሺݐሻ KI ǡ ǦǤ Ǥ ௧. ݑሺݐሻ ൌ ܭூ න ݁ሺ߬ሻ݀߬ . Ǥ ǡ KD Ǥ ݀ ݑሺݐሻ ൌ ܭ ݁ሺݐሻ ݀ݐ Ǥ
(47) Ǥ
(48) Ǥ. ͳͳ.
(49) Ph.D.ThesisCarstenRohr. Figure6.Scanningmovement: Ǥ Ǧ ǦǤ Ǥ Ǥ Ǧ ǡ Ǧ Ǥ ǡ ȋȌ ȏͷͷȐǤ
(50) ǡ ȋ Ȍ ȋȌ ǡ Ǥ ǡ ͵Ǥ͵Ǥ Ǥ ǡ ǤʹǤ Ǥ ǤǦ Ǥ ȏͷȐǤ ͳʹ.
(51) Chapter3:ScanningTunnelingMicroscopy
(52)
(53)
(54) Ǥ Ǥ. 3.2 TunnelTheory ǣ ǦǦ ǡ Ǥ Ǥ Ǧ Ǥ Ǧ Ǥ Ǧ ȏͷȐǤ ȏͷͺȐ ȏͷͻȐǡ ȏͲȐ Ǥ ȏͳȐǤ ǡ Ǧ Ǥ Ǥ ȏʹȐǤ ǦǦ Ǥ ǦǦ ǡ ǡ Ǥ USUT,ȋSȌ ȋTȌ ǡ Ǥ ܷௌ ்ܷ ൌ ܷܷௌ ή ்ܷ ൌ Ͳ Ǥ ǡ ǡǤǡ ǡ UǤ ǡUS0US Ǥ
(55) VS=US0–USǡǦ \PǤ
(56) ǡ Ú ͳ͵.
(57) Ph.D.ThesisCarstenRohr. Figure7.Perturbationtheoryatthetipsamplegap:Ȍ Ǥ Ȍ Ǧ ǤȋȌ ǤEFSEFT ǡEPEQ ǡ Ǥ Ǥ ǦȌ Ǥ ሺܶ ܷௌ ሻ߰ఓ ൌ ܧఓ ߰ఓ. Ǥ ሺܶ ்ܷ ሻ߯ఔ ൌ ܧఔ ߯ఔ. \P FQǡ Ǧ Ǥ. ͳͶ.
(58) Chapter3:ScanningTunnelingMicroscopy Ǧ Ú Ǥ Ǧ Ǥ
(59) Ǧ ǯ Ǥ ǡ ǤtεͲ UT Ǧ Ú ݅. ߲Ȳ ൌ ሺܶ ܷௌ ்ܷ ሻȲ ߲. Ǧ <FQ ாഌ ௧ . Ȳ ൌ ܽఔ ሺݐሻ߯ఔ ݁ ି ఔ. tαͲǡ \Pǡ ሺாഋ ିாഌ ሻ௧ . ܽఔ ሺݐሻ ൌ ൻ߯ఔ ห߰ఓ ൿ݁ ି. ܿఔ ሺݐሻ. QȋͲȌαͲǤa < ǣ ாഋ ௧ . Ȳ ൌ ߰ఓ ݁ ି. ாഌ ௧ . ܿఔ ሺݐሻ߯ఔ ݁ ି ఔ. <UT ǡ UTǤ δFQȁ QȋȌǣ ሺாഋ ିாഌ ሻ௧ . ݅ܿሶఔ ሺݐሻ ൌ ൻ߯ఔ ห்ܷ ห߰ఓ ൿ݁ ି. ሺாഊ ିாഌ ሻ௧ . ߯ۦఔ ȁܷௌ ȁ߯ఒ ۧܿఒ ሺݐሻ݁ ି ఒ. \P FQǡ PPQǡ cQ(t)ǣ ଶ. ௧. ܲటഋ՜ఞഌ ൌ ܲఓఔ ൌ ቤන ܿሶఔ ሺݐሻ݀ݐቤ . ZPQȀW ǯ ሺଵሻ. ߱ߤߥ ൌ . ʹߨ . ห ߥߤܯห;. ݀ܰ ݀ܧ. ͳͷ.
(60) Ph.D.ThesisCarstenRohr ߥߤܯൌ ർ߯ߥ ቚܷܶ ቚ߰ߤ ൌ න ߯ ܸ݅ܶ݀ ߤ߰ ܷܶ ߥכ ܸܶ݅. I ǡ MPQǤ HȋȌ ȋȌȋȌ ȋȌ Uǡ ߩௌ ሺܧிௌ െ ܷ݁ ߝሻ ߩ் ሺܧி் ߝ ሻEFSEFT EFǡ Ǥ ǡ ǣ ȋȌ ȋȌ ǡͺǤ. Figure8.Thetunnelingcurrent: Ǥ EFȋȌȋȌǤ Hǡ Ǥ Ǥ Ǧ ǡ F ȋȌǣ ܨ՜ ൌ ݂ௌ ሺߝ െ ܷ݁ሻሾͳ െ ்݂ ሺߝሻሿ Ǧ ͳ ݂ሺ ܧሻ ൌ ாିாಷ ͳ ݁ ் ǣ ܨ՚ ൌ ்݂ ሺߝሻሾͳ െ ݂ௌ ሺߝ െ ܷ݁ሻሿ ǡ ǣ ͳ.
(61) Chapter3:ScanningTunnelingMicroscopy ܨ՜ െ ܨ՚ ൌ ݂ௌ ሺߝ െ ܷ݁ሻ െ ்݂ ሺߝሻ ǣ Ͷߨ݁ ஶ ܫൌ න ሾ݂ ሺ ܧെ ܷ݁ ߝ ሻ െ ்݂ ሺܧிௌ ߝ ሻሿ ିஶ ௌ ிௌ ߩ ڄௌ ሺܧிௌ െ ܷ݁ ߝ ሻߩ் ሺܧிௌ ߝ ሻȁܯȁଶ ݀ߝ MǦ Ǥ Ǥ ǡ Ʋ Ǥ ȋǦ Ȍ ͳͻͺ͵ ȏͳȐ. Ǧ ǡ ǤʹǤ Ǥ p d;Ǧ Ǥ Ǥ ȏʹȐǤ. 3.3 Tunnelingthroughmolecularadlayers Ǥ
(62) Ǧ ȏͳȐ ʹ݉߶ ȁܯȁ; ି ݁ ןଶௗ ߢ ൌ ඨ d ǡm Ǥ Ǧǡ Ǥ
(63) ǡ Ǥ ߛௌ ߛ் ሾ݂ ሺ ܧെ ܷ݁ ߝ ሻ െ ்݂ ሺܧிௌ ߝ ሻሿ ି ݁ ן ܫଶௗ ߛௌ ߛ் ௌ ிௌ ఌ. ͳ.
(64) Ph.D.ThesisCarstenRohr Ǥ ǡ Ǥ. Figure 9. Tunneling through molecules: Ȍ ȋǤǤ Ȍ ǡ ǤȌ Ǧ Ǥ
(65) ǡ ȋ Ȍ ȋȌǡǡͻǤ ǡ Ǥ
(66) Ǥ
(67) ǡ ǡ ǡ ͻǤ ȏ͵Ȑ ȏͶȐǤ Ǧ Ǥ ò Ǧ Ǧ ǡ ȋ ȌȏͷȐǤ ͳͺ.
Outline
ÄHNLICHE DOKUMENTE
b Lebanese University, Faculty of Science III, Tripoli, Lebanon Reprint requests to Dr. High-resolution Fourier transform spectroscopy has been used to ana- lyze the
It appears that a study of laser- induced fluorescence provides precise and extensive results for the lower states [1] and limited results for.. the upper states, since this
b Sana’a University, Faculty of Science, Department of Physics, P. Box 13783, Sana’a, Republic
Molecular dynamics simulations of molten (La 1 / 3 , K)Cl at 1123 K have been performed in order to investigate the correlation between simulated dynamical properties such as
Self-Organization, Nonlinearities, Visual Cortex, (Anti-)Hebbian Learning, Lateral Plasticity A neural network model with incremental Hebbian learning of afferent and lateral synap-
r S Sector model of the equatorial plane of a black hole (enlarge to A3). Sectors arranged symmetrically, for use with
Sektoren in symmetrischer Anordnung, zur Verwendung mit Transfersektoren Mit Startstrich für eine
Sektoren in symmetrischer Anordnung, zur Verwendung mit Transfersektoren Mit Startstrichen für zwei parallel startende