ФИЗИКО-ХИМИЧЕСКИЕ ОСНОВЫ НАНОТЕХНОЛОГИИ Профессор Н.Г. Рамбиди

I. Исходные предпосылки

The term Nanotechnology Coined in 1974 by Nori Taniguchi to mean precision machining with tolerances of a micrometer or less Popularized by Drexler in 1986 By analogy with microtechnology –micro = one-millionth (10 -6 ) –nano = one-billionth (10 -9 ) Actually, nm

sequential miniaturization From Powers of Ten, by Philip and Phylis Morrison and the office of Charles and Ray Eames.

Definition The creation of functional materials, devices and systems through control of matter (atomic, molecular and macromolecular levels) at the scale of 1 to 100 nanometers, and exploitation of novel properties and phenomena at the same scale. Nanotechnology lets us fabricate an entire new generation of products that are cleaner, stronger, lighter, and more precise.

What is nanotechnology? Nanotechnology involves the manipulation of objects on the atomic level. Products will be built with every atom in the right place, allowing materials to be lighter, stronger, smarter, cheaper, cleaner, and more precise. In order for this science to be realized, positional control must be achieved, and self-replication is necessary to reduce costs.

Исходные посылки Два основных вопроса: Какой все-таки смысл вкладывается в понятие «нанотехнология»? Почему именно сейчас возник нанотехнологический бум?

Экономические и социальные корни нанотехнологического бума Демографические взрывы Сокращение природных ресурсов Глобальная информационная система Глобальная транспортная система Изменение социальных мотиваций

Синтез природного каучука как пример нанотехнологического подхода

Полимеризация этилена, стирола, хлорвинила

Структурные варианты полимерных систем

Синтетические каучуки

Синтез природного каучука

Корни нанотехнологического бума

Исходная и основная причина возникновения нанотехнологии Развитие информационных технологий, вызванное все увеличивающейся значимостью сложных динамических систем, необходимостью понимания их механизмов и управления ими

Сложные динамические системы

Многоуровневая структура Распределенная динамика Нелинейные динамические механизмы Развитая система обратных связей

Bees Colony cooperation Regulate hive temperature Efficiency via Specialization: division of labour in the colony Communication : Food sources are exploited according to quality and distance from the hive

Termites Cone-shaped outer walls and ventilation ducts Brood chambers in central hive Spiral cooling vents Support pillars

Ants Organizing highways to and from their foraging sites by leaving pheromone trails Form chains from their own bodies to create a bridge to pull and hold leafs together with silk Division of labour between major and minor ants

Question: How can ag systems be quantified & used to understand rural poverty and resource degradation? Hypothesis: Spatial heterogeneity and dynamic properties of these systems are key to understanding their behavior (and linkages to poverty & degradation) e.g., in understanding non-adoption of conservation techs A key question is how much detail is needed to answer important policy questions!!!

The soil microbial system More diversity in the palm of your hand than in the mammalian kingdom Most important and abused ecosystem in the world Essential features –Species concept not useful –Feedback and feedforward coupling to dynamic environment is central –Functionality –Cant measure much (anything)

If you had to move furniture, would you work better in a group, or alone? What if you were writing a term paper? Have you ever had a bad supervisor? What differentiated that person from a good supervisor? Have you ever done something in a crowd youd never have done alone?

The Nature of Groups Group - Two or more people who influence each other. Collections of individuals become increasingly group like when they: –Are interdependent –Share a common identity –Have a group structure

Crowds and Deindividuation Deindividuation - The process of losing ones sense of personal identity, which: makes it easier to behave in ways inconsistent with ones normal values. Example: Anonymous children in Halloween costumes stole more from a candy jar (Beaman et al., 1979)

In an initial vote, the opinions looked like the above. Red circles depict neighbors who are Anti-party Blue circles depict neighbors who are Pro- party What will happen if everyone checks with his or her immediate neighbors, and goes with the local majority? Copyright © 2002 by Allyn and Bacon

This individual will change because the majority of her neighbors are Anti Copyright © 2002 by Allyn and Bacon

But this neighbor will change because the majority of his neighbors are Pro With all this changing back and forth, what will happen over the course of a few weeks? Copyright © 2002 by Allyn and Bacon

Because local majorities draw in people in their vicinity, the neighborhood will eventually stabilize into this pattern. Copyright © 2002 by Allyn and Bacon

Проблемы «искусственного интеллекта» как подход к пониманию сложных динамических систем

AI Defenitions The automation of activities that we associate with human thinking… Bellman 1978 The study of mental faculties through the use of computational models Charniak & McDermott The study of how to make computers do things at which at the moment people are better. Rich&Knight The branch of CS that is concerned with the automation of intelligent behavior. Lugar&Stubblefield

Applied Areas of AI Game playing Speech and language processing Expert reasoning Planning and scheduling Vision Robotics

IMPORTANT TO SCIENCE 1. What is the nature of matter? 2. What is the nature of life? 3. What is the nature of mind?

IMPORTANT TO ECONOMIC PROSPERITY Toffler's Three Waves 1. Invention of agriculture 2. Invention of steam engine and discovery of electricity 3. Invention of electronics and digital computer

IMPORTANT FOR ECONOMIC PROSPERITY Manufacturing Business management and financial services Marketing and customer services Transportation safety and efficiency Communications Construction Waste management and cleanup Health care Physical security Agriculture and food processing Mining and drilling Space and undersea exploration

IMPORTANT FOR MILITARY STRENGTH 1. Intelligent systems will enable a new generation of unmanned vehicles and weapon systems that will: 2. Intelligent systems technologies will enable: -- outperform manned systems -- with fewer casualties -- and lower cost for training and readiness -- faster information gathering and processing -- more rapid replanning for real-time events -- and more effective command and control

IMPORTANT FOR MILITARY STRENGTH 1. Intelligent systems will enable a new generation of unmanned vehicles and weapon systems that will: 2. Intelligent systems technologies will enable: -- outperform manned systems -- with fewer casualties -- and lower cost for training and readiness -- faster information gathering and processing -- more rapid replanning for real-time events -- and more effective command and control

IMPORTANT FOR HUMAN WELL BEING Clean up the environment Improve health care Improve transportation safety and efficiency Improve personal services and security Improve productivity End poverty and create golden age

The background of intellectual problems Recognition of images, scenes and situations Investigation of the system evolution having complicated behavioral dynamics Choice of optimal structure or behavior of multifactor systems having complex branching search tree Control problems

Biological roots of intellectual problems

сложность

Andrey Nikolaevich Kolmogorov Born: April 25, 1903 Tambov, Russia Died: Oct. 20, 1987

Organisms :1000 to 100,000 genes Boeing 777 : >100,000 subsystems and... …many subsystems are highly complex. Engine: > 10,000 subsystems Laptop: >1,000,000,000 transistors Refinery : > 10,000 integral feedback loops High rise heating-ventilation-AC: > 1000 Internet, power grid, etc.: ~ Complexity

Definitions and Properties Complexity –Non-linear interaction among multiple components –Complicated versus complex systems –Irreducible –Local and distributed –Non-deterministic / unpredictable –Emergence / self-organization Deterministic Reductionist principle Dynamic / stochastic Holistic

Nonlinearity in Spread of Innovation Cumulative number of farmers Year Source: Based on Ryan and Gross (1943) Number of Adopters of Hybrid Seed Corn in Two Iowa Communities

алгоритмическая сложность min l(p) : (p x)=y K (y x) = { : p S (p x) y

Computational Complexity P: Problems with time complexity of O(n k ) where, k is a constant. P-type problems are classically tractable or easy. Problems with exponential complexity such as O(N N ), O(2 N ), O(N!) are classically intractable or hard. NP: A solution, if exists, can be verified in polynomial time. NP-complete: NP and hard or intractable classically.

Логистическое уравнение

Проблема «Коммивояжер» { V in,1,2,3..N, V out } fixed V in,V out P{1,2,3..N} N! paths

Проблема «Коммивояжер»

Combinatorial Explosion A 10 city TSP has 181,000 possible solutions A 20 city TSP has 10,000,000,000,000,000 possible solutions A 50 City TSP has 100,000,000,000,000,000,000,000,000,000,00 0,000,000,000,000,000,000,000,000,000,000 possible solutions There are 1,000,000,000,000,000,000,000 litres of water on the planet Mchalewicz, Z, Evolutionary Algorithms for Constrained Optimization Problems, CEC 2000 (Tutorial)

Проблема «Коммивояжер» исходная система (100 точек)

Проблема «Коммивояжер» после 500 итераций

Проблема «Коммивояжер» после 4000 итераций

Реакционно-диффузионная среда

Среда Белоусова-Жаботинского

Нейросетевой подход как инструмент эффективного решения задач высокой вычислительной сложности

Принципы парадигмы фон Неймана Вводимая извне программа Последовательное выполнение операций Программа записывается теми же кодами, что и данные, что позволяет изменять программу в ходе вычислений (разветвления, циклы) Простейшие двоичные элементарные операции, позволяющие создать универсальный вычислитель

The brain The cortex – kg (2% of the body weight) … [13,14] –2,500 cm 2 (rat: 6 cm 2, elephant: 6,300 cm 2 ) [14] –1,300-1,500 cm 3 2 hemispheres connected by corpus callossum (250 mill. nerve fibers) Inputs: –spinal cord –optic nerve (1.2 mill.) –cranial nerves (12) –auditory system, …

Neurons 100 billion neurons (children) –loss: ~1/sec 31 million/year –Octopus: 300 million Diameter: 4 – 100 microns Weight: grams Length:

The brain as a computational system The brain is –biological –de-central (plasticity) –non-digital –highly parallel What does this mean?

The Biological Neuron 10 billion neurons in human brain Summation of input stimuli –Spatial (signals) –Temporal (pulses) Threshold over composed inputs Constant firing strength billion synapses in human brain Chemical transmission and modulation of signals Inhibitory synapses Excitatory synapses

Biological Neural Networks 10,000 synapses per neuron Computational power = connectivity Plasticity –new connections (?) –strength of connections modified

Artificial Neuron Abstract away from almost everything except connectivity…

Binary Neurons Hard threshold = threshold ex: Perceptrons, Hopfield NNs, Boltzmann Machines Main drawbacks: can only map binary functions, biologically implausible. off on StimulusResponse

Analog Neurons Soft threshold ex: MLPs, Recurrent NNs, RBF NNs... Main drawbacks: difficult to process time patterns, biologically implausible. off on StimulusResponse

Artificial Neural Networks Output layer Input layer Hidden layers fully connected sparsely connected

Neural network mathematics Inputs Output

Когда аппаратные возможности компьютера сравняются с возможностями человеческого мозга?

The most powerful experimental supercomputers in 1998 composed of thousands or tents of thousands of the fastest microprocessors and costing tens of millions of dollars can do a few million MIPS. They are within striking distance of being powerful enough to match human brainpower, but are unlikely to be applied to that end. Why tie up a rare twenty-million-dollar asset to develop one erzatz-human, when millions of inexpensive original-model humans are available. Such machines are needed for high-value scientific calculations, physical simulations, having no cheaper substitute. AI research must wait for the power to become more affordable. The most powerful experimental supercomputers in 1998 composed of thousands or tents of thousands of the fastest microprocessors and costing tens of millions of dollars can do a few million MIPS. They are within striking distance of being powerful enough to match human brainpower, but are unlikely to be applied to that end. Why tie up a rare twenty-million-dollar asset to develop one erzatz-human, when millions of inexpensive original-model humans are available. Such machines are needed for high-value scientific calculations, physical simulations, having no cheaper substitute. AI research must wait for the power to become more affordable. H. Moravec When will computer hardware match the human brain? H. Moravec When will computer hardware match the human brain?

Некоторые перспективы

Ферменты – принцип «ключ-замок»

Механизм ферментативной реакции

Three historical trends in manufacturing More flexible More precise Less expensive