Molecular and SupramolecularInformation Processing -- Contents -- Preface -- List of Contributors -- 1 Molecular Information Processing: from Single Molecules to Supramolecular Systems and Interfaces - from Algorithms to Devices - Editorial Introduction -- References -- 2 From Sensors to Molecular Logic: A Journey -- 2.1 Introduction -- 2.2 Designing Luminescent Switching Systems -- 2.3 Converting Sensing/Switching into Logic -- 2.4 Generalizing Logic -- 2.5 Expanding Logic -- 2.6 Utilizing Logic -- 2.7 Bringing in Physical Inputs -- 2.8 Summary and Outlook -- Acknowledgments -- References -- 3 Binary Logic with Synthetic Molecular and Supramolecular Species -- 3.1 Introduction -- 3.1.1 Information Processing: Semiconductor Devices versus Biological Structures -- 3.1.2 Toward Chemical Computers? -- 3.2 Combinational Logic Gates and Circuits -- 3.2.1 Basic Concepts -- 3.2.2 Bidirectional Half Subtractor and Reversible Logic Device -- 3.2.3 A Simple Unimolecular Multiplexer-Demultiplexer -- 3.2.4 An Encoder/Decoder Based on Ruthenium Tris(bipyridine) -- 3.2.5 All-Optical Integrated Logic Operations Based on Communicating Molecular Switches -- 3.3 Sequential Logic Circuits -- 3.3.1 Basic Concepts -- 3.3.2 Memory Effect in Communicating Molecular Switches -- 3.3.3 A Molecular Keypad Lock -- 3.3.4 A Set-Reset Memory Device Based on a Copper Rotaxane -- 3.4 Summary and Outlook -- Acknowledgments -- References -- 4 Photonically Switched Molecular Logic Devices -- 4.1 Introduction -- 4.2 Photochromic Molecules -- 4.3 Photonic Control of Energy and Electron Transfer Reactions -- 4.3.1 Energy Transfer -- 4.3.2 Electron Transfer -- 4.4 Boolean Logic Gates -- 4.5 Advanced Logic Functions -- 4.5.1 Half-Adders and Half-Subtractors -- 4.5.2 Multiplexers and Demultiplexers -- 4.5.3 Encoders and Decoders -- 4.5.4 Sequential Logic Devices.

4.5.5 An All-Photonic Multifunctional Molecular Logic Device -- 4.6 Conclusion -- References -- 5 Engineering Luminescent Molecules with Sensing and Logic Capabilities -- 5.1 Introduction -- 5.2 Engineering Luminescent Molecules -- 5.3 Logic Gates with the Same Modules in Different Arrangements -- 5.4 Consolidating AND Logic -- 5.5 ''Lab-on-a-Molecule'' Systems -- 5.6 Redox-Fluorescent Logic Gates -- 5.7 Summary and Perspectives -- References -- 6 Supramolecular Assemblies for Information Processing -- 6.1 Introduction -- 6.2 Recognition of Metal Ion Inputs by Crown Ethers -- 6.3 Hydrogen-Bonded Supramolecular Assemblies as Logic Devices -- 6.4 Molecular Logic Gates with [2]Pseudorotaxane- and [2]Rotaxane-Based Switches -- 6.5 Supramolecular Host-Guest Complexes with Cyclodextrins and Cucurbiturils -- 6.6 Summary -- Acknowledgments -- References -- 7 Hybrid Semiconducting Materials: New Perspectives for Molecular-Scale Information Processing -- 7.1 Introduction -- 7.2 Synthesis of Semiconducting Thin Layers and Nanoparticles -- 7.2.1 Microwave Synthesis of Nanoparticles -- 7.2.2 Chemical Bath Deposition -- 7.2.2.1 Sulfide Ion Precursors -- 7.2.2.2 Commonly Used Ligand -- 7.3 Electrochemical Deposition -- 7.3.1 Nanoheterostructure Preparation -- 7.3.2 Nanoparticles Directed Self-Assembly -- 7.4 Organic Semiconductors-toward Hybrid Organic/Inorganic Materials -- 7.4.1 Self-Organization Motifs Exhibited by Acenes and Acene-Like Structures -- 7.4.2 Applications of Acenes in Organic Electronic Devices -- 7.5 Mechanisms of Photocurrent Switching Phenomena -- 7.5.1 Neat Semiconductor -- 7.5.2 Composite Semiconductor Materials -- 7.5.3 Semiconductor-Adsorbate Interactions -- 7.5.4 Surface-Modified Semiconductor -- 7.5.5 Optoelectronic Devices Based on Organic Molecules/Semiconductors -- 7.6 Digital Devices Based on PEPS Effect -- 7.7 Concluding Remarks.

Acknowledgments -- References -- 8 Toward Arithmetic Circuits in Subexcitable Chemical Media -- 8.1 Awakening Gates in Chemical Media -- 8.2 Collision-Based Computing -- 8.3 Localizations in Subexcitable BZ Medium -- 8.4 BZ Vesicles -- 8.5 Interaction Between Wave Fragments -- 8.6 Universality and Polymorphism -- 8.7 Binary Adder -- 8.7.1 Sum -- 8.7.2 Carry Out -- 8.8 Regular and Irregular BZ Disc Networks -- 8.8.1 Elementary Logic Gates -- 8.8.2 Half Adder -- 8.9 Memory Cells with BZ Discs -- 8.10 Conclusion -- Acknowledgments -- References -- 9 High-Concentration Chemical Computing Techniques for Solving Hard-To-Solve Problems, and their Relation to Numerical Optimization, Neural Computing, Reasoning under Uncertainty, and Freedom of Choice -- 9.1 What are Hard-To-Solve Problems and Why Solving Even One of Them is Important -- 9.1.1 What is so Good About Being Able to Solve Hard-To-Solve Problems from Some Exotic Class? -- 9.1.2 In Many Applications Areas -In Particular in Chemistry -There are Many Well-Defined Complex Problems -- 9.1.3 In Principle, There Exist Algorithms for Solving These Problems -- 9.1.4 These Algorithms may Take Too Much Time to be Practical -- 9.1.5 Feasible and Unfeasible Algorithms: General Idea -- 9.1.6 Solving Equations of Chemical Kinetics: An Example of a Feasible Algorithm -- 9.1.7 Straightforward Solution of Schrödinger Equation: An Example of an Unfeasible Algorithm -- 9.1.8 Straightforward Approach to Protein Folding: Another Example of an Unfeasible Algorithm -- 9.1.9 Feasible and Unfeasible Algorithms: Toward a Formal Description -- 9.1.10 Maybe the Problem Itself is Hard to Solve? -- 9.1.11 What Is a Problem in the First Place? -- 9.1.12 What is a Problem: Mathematics -- 9.1.13 A Description of a General Problem -- 9.1.14 What About Other Activity Areas? -- 9.1.15 What is a Problem: Theoretical Physics.

9.1.16 What is a Problem: Engineering -- 9.1.17 Class NP -- 9.1.18 Class P and the P ?= NP Problem -- 9.1.19 Exhaustive Search: Why it is Possible and Why it is Not Feasible -- 9.1.20 Notion of NP-Complete Problems -- 9.1.21 Why Solving Even One NP-Complete (Hard-To-Solve) Problem is Very Important -- 9.1.22 Propositional Satisfiability: Historically the First NP-Complete Problem -- 9.1.23 What We Do -- 9.2 How Chemical Computing Can Solve a Hard-To-Solve Problem of Propositional Satisfiability -- 9.2.1 Chemical Computing: Main Idea -- 9.2.2 Why Propositional Satisfiability was Historically the First Problem for Which a Chemical Computing Scheme was Proposed -- 9.2.3 How to Apply Chemical Computing to Propositional Satisfiability: Matiyasevich's Original Idea -- 9.2.4 A Precise Description of Matiyasevich's Chemical Computer: First Example -- 9.2.5 A Precise Description of Matiyasevich's Chemical Computer: Second Example -- 9.2.6 A Precise Description of Matiyasevich's Chemical Computer: General Formula -- 9.2.7 A Simplified Version (Corresponding to Catalysis) -- 9.2.8 Simplified Equations: Example -- 9.2.9 Chemical Computations Implementing Matiyasevich's Idea Are Too Slow -- 9.2.10 Natural Idea: Let us Use High-Concentration Chemical Reactions Instead -- 9.2.11 Resulting Equations -- 9.2.12 Discrete-Time Version of These Equations Have Already Been Shown to be Successful in Solving the Propositional Satisfiability Problem -- 9.2.13 Conclusion -- 9.2.14 Auxiliary Result: How to Select the Parameter Dt -- 9.3 The Resulting Method for Solving Hard Problems is Related to Numerical Optimization, Neural Computing, Reasoning under Uncertainty, and Freedom of Choice -- 9.3.1 Relation to Optimization: Why it is Important -- 9.3.2 Relation to Optimization: Main Idea -- 9.3.3 Relation to Numerical Optimization: Conclusion.

9.3.4 Relation to Numerical Optimization: What Do We Gain from It? -- 9.3.5 Relation to Neural Computing -- 9.3.6 Relation to Reasoning Under Uncertainty -- 9.3.7 Relation to Freedom of Choice -- Acknowledgments -- References -- 10 All Kinds of Behavior are Possible in Chemical Kinetics: A Theorem and its Potential Applications to Chemical Computing -- 10.1 Introduction -- 10.1.1 Chemical Computing: A Brief Reminder -- 10.1.2 Chemical Computing: Remaining Theoretical Challenge -- 10.1.3 What We Do -- 10.2 Main Result -- 10.2.1 Chemical Kinetics Equations: A Brief Reminder -- 10.2.2 Chemical Kinetics Until Late 1950s -- 10.2.3 Belousov - Zhabotinsky Reaction and Further Discoveries -- 10.2.4 A Natural Hypothesis -- 10.2.5 Dynamical Systems -- 10.2.6 W.l.o.g., We Start at Time t = 0 -- 10.2.7 Limited Time -- 10.2.8 Limited Values of xi -- 10.2.9 Limited Accuracy -- 10.2.10 Need to Consider Auxiliary Chemical Substances -- 10.2.11 Discussion -- 10.2.12 Effect of External Noise -- 10.3 Proof -- Acknowledgments -- References -- 11 Kabbalistic-Leibnizian Automata for Simulating the Universe -- 11.1 Introduction -- 11.2 Historical Background of Kabbalistic-Leibnizian Automata -- 11.3 Proof-Theoretic Cellular Automata -- 11.4 The Proof-Theoretic Cellular Automaton for Belousov-Zhabotinsky Reaction -- 11.5 The Proof-Theoretic Cellular Automaton for Dynamics of Plasmodium of Physarum polycephalum -- 11.6 Unconventional Computing as a Novel Paradigm in Natural Sciences -- 11.7 Conclusion -- Acknowledgments -- References -- 12 Approaches to Control of Noise in Chemical and Biochemical Information and Signal Processing -- 12.1 Introduction -- 12.2 From Chemical Information-Processing Gates to Networks -- 12.3 Noise Handling at the Gate Level and Beyond -- 12.4 Optimization of AND Gates -- 12.5 Networking of Gates -- 12.6 Conclusions and Challenges.

Acknowledgments.