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Energy and IT Technology in 20 Years:
A Prediction Based on Current Research
Progress
Alfred Hübler
Santa Fe Institute
and
Center for Complex Systems Research
University of Illinois at Urbana-Champaign
Predicted Technological Breakpoints:
-Merger of information and energy devices (Objective of DOE Smart Grid Initiative)
- Innovation driven by ANN which use humans and mixed reality
Current Research Progress:
- Digital Batteries (material with highest energy density & power density, inexpensive, nano)
-Digital Wires (robust power distribution & storage, move and process information)
- Atomic Neural Nets (nano-scale particle swarms, which self-assemble into fractal patterns,
which detect patterns, make abstractions; which innovate by association; which exceed
computational capacity of humans by a factor of 109, and need less power)
Digital Batteries
Alfred W. Hubler and Onyeama Osuagwu
Center for Complex Systems Research, UIUC
Digital batteries are arrays of nano junctions:
- where charge recombination is quantummechanically forbidden
- where each capacitor can be individually charged/discharged, as in a flash drive
- where design prevents tunneling, even if the energy density is very high
- which can be integrated on the wafer with sensors, CPUs
- which have an energy density > 1 GJ/m3 (200 kJ/kg), charging-discharging rates in
the THz range, and exceed number of charging cycles of chemical batteries and
conventional capacitors by orders of magnitude.
- which are fully operational in a large temperature range (from -273oC to 500oC) and
have no thermal run-away
We find:
- main problem: SiO2 compressive strength of 1 GPa limits energy density to 200 kJ/kg
http://www.physics.uiuc.edu/people/Hubler/ http://server10.how-why.com/blog/
Energy storage in conventional
capacitors
Capacitors are environmentally friendly, work in a large temperature
range (0K-melting temperature of metal ), and have a virtually
unlimited number of charging cycles.
The energy stored in a capacitor is:
W = ½ C V2 ,
(1)
where C=ε A / d is the capacity,
V = applied voltage, ε = electric constant, A = plate area, d = plate distance
The energy density is:
w = ½ ε E2 ,
(2)
Where the electric field, E = V/d
However, if the energy density in conventional
capacitors exceeds E=3 x 106V/m in air (6 x
107V/m in Teflon) the capacitor discharges by
arcing and the energy is lost. => Theoretical
value of maximum energy density is small,
w = 100 KJ/m3 (500J/kg)
Conventional capacitors need a long time (t ~
) to charge/discharge since inductance
L is large.
Energy storage in chemical batteries,
hydrogen fuel cells, and gasoline
Energy stored in chemical systems is stored as electrostatic energy,
as in capacitors. But, in chemicals such as hydrogen, the limiting
electric fields are much higher. Quantization phenomena at the atomic
level prevent charge recombination => high energy density.
Atomic hydrogen is a good example. Energy could be stored in a hydrogen
atom by lifting the electron from the ground state to the highest excited state (ionization).
In this case, the ratio between the stored energy and the volume of the atom is
w = 13.6eV / (volume of hydrogen atom) = 3.3 x 1013J / m3 (1.31 x 1012J/kg)
i.e. nine orders of magnitude above the maximum energy density in a conventional capacitor.
Since the excited state of hydrogen atoms is short lived, hydrogen atoms cannot be used for long
term energy storage. For this reason, hydrogen molecules—and carbohydrates, such as
gasoline—are commonly used for energy storage. Unfortunately, molecular hydrogen is difficult
to handle and the energy retrieval from hydrogen and carbohydrates in fuel cells is slow and
inefficient, works only in a small temperature range, and experimental energy density << limit.
Energy storage in faradic systems has low efficiency and is limited by diffusion, reaction
rates, fractal growth & irreversible chemical reactions.
Digital batteries
Digital batteries are arrays of nano vacuum
tubes = arrays of nano vacuum capacitors.
Digital batteries are arrays of nano-scale
junctions , where
field emission, avalanche breakdown and Zener
breakdown are prevented by quantization
phenomena,
Digital battery
and which are similar to:
-LEDs and laser diodes, but without charge
recombination or tunneling,
-Magnetic tunneling junctions, but much simpler
in design and cheaper to build
This work builds on our Correlation Tunnel Device patent
[H. Higuraskh, Toriumi, F. Yamaguchi, K. Kawamura,
A. Hübler, Correlation Tunnel Device,
A. U. S. Patent # 5,679,961 (1997)]
Break down probability versus junction size
(Alpert et al, Boyle t al., Hubler et al.)
Digital batteries
We find: Nano vacuum capacitors arrays could
sustain energy densities up to
10MJ/kg
without significant charge recombination,
however the compressive strength of the materials
(1GPa for SiO2) limits the energy density to
Emax = compressive-strength / density
= 200 kJ/kg (for SiO2 substrates)
Digital batteries are similar to
nano plasma tubes, except that
they store energy instead of
converting it to light
The charge – discharge rate is limited by the induction
f = junction-size / speed-of-light
which is in the THz range.
The energy density of chemical batteries is less than 1 kJ/kg.
The charge – discharge rate of batteries is limited by diffusion and reaction rates.
Digital batteries: Power Density
and Energy Density
Fast and light
Small and light
Digital batteries are arrays of nano vacuum tubes = arrays of nano vacuum capacitors.
Christopher L. Magee, Massachusetts Institute of Technology,”Towards quantification of
the Role of Materials Innovation in overall Technological Development”,
http://cmagee.mit.edu/images/docs/chfquantificationofmaterialsrolea.pdf
Nano-junction arrays as Digital
Batteries
One could design large arrays of individually connected nanojunction, which could be charged and discharged one-by-one,
similar to flash drive technology.
In contrast to conventional batteries, the output voltage would
remain constant until the last nano-capacitor is discharged and
charging/discharging digital batteries would be orders of
magnitude faster. Such arrays of nano-capacitors could serve as
digital batteries.
Digital batteries would produce a stable output voltage, making
them ideal for sensors and other sensitive devices.
Digital batteries could be recharged probably millions of times,
whereas chemical batteries can be recharged only a few
thousand times.
Digital batteries are
similar to flash
drives: flash drives
store charge, while
digital batteries store
energy
Conclusion
Digital batteries are potentially an inexpensive and
environmentally-friendly alternative to both chemical
Batteries.
Digital batteries are arrays of nano junctions:
- where charge recombination is quantummechanically forbidden
Digital batteries are
- where each capacitor can be individually chargedsimilar to flash drives:
discharged, as in a flash drive
flash drives store charge,
- where design prevents tunneling, even if the energy
while digital batteries
density is very high
store energy
- which can be integrated on the wafer with sensors, CPUs
- which have high energy density, up to 1 GJ/m3 (200 kJ/kg), charging-discharging
rates in the THz range, and exceed number of charging cycles of chemical batteries
and conventional capacitors by orders of magnitude.
- which are fully operational in a large temperature range (from -273oC to 500oC)
and have no thermal run-away
- main problem: SiO2 compressive strength = 1 GPa (200 kJ/kg)
http://www.physics.uiuc.edu/people/Hubler/ http://server10.how-why.com/blog/
Digital Wires
Alfred Hubler, [email protected], Physics, UIUC
http://server10.how-why.com/blog
Digital Wire
Analog wires are used to move energy (power lines, power grid) and information (data
transmission lines, Internet) in electrical networks.
However, most dynamical systems with more than 7 degrees of freedom are chaotic
=> the dynamics of large networks of analog wires is unstable => congestions &
cascading failures
Digital wires: Wires that propagate only patterns of rectangular pulses
Specific advantages of digital wires:
- Fixed pulse shape (increased reliability & speed);
- Robust against electric smog (increased reliability & speed);
- No cross talk (increased reliability & speed);
- No echoes (increased reliability & speed);
- Adjustable pulse speed (increased adjustability);
- Encryption (increased security);
- Digital wire can be general purpose computers (increased adjustability).
Neurons are digital wires. Digital wires move information in parallel.
Digital wires
Graphical depiction of a “Digital Wire” formed by imposing circular
boundary conditions on a CA. A digital wire implemented by a CA has
notable advantages over copper wires, such as isolating defects. By
choosing the appropriate rule, the effects of a defect such as a short
circuit can be isolated to a single cell site, rather than propagating the
defect along the entire wire, as copper wires do.
Digital Wires: hardware implementation as a transistor
network
Vin
Vout
Cell 1
Cell 2
(a)
Cell n
(b)
(a) A digital wire constructed of resistors and pnp transistors. (b)
Experimental measurement of the input-output response of the pnptransistor digital wire. The sharp transition at 77% of the total supply
voltage results in a noise immunity threshold. In order for noise to affect
the outcome of a signal, it must exceed this threshold.
Digital Wires:
Hardware implementation as a Boolean
network
Digital wires: a simple model
Definition: A digitial wire is a long network of
cells. Digital pulses travel along the digital wire,
according to the following rule:
Digital Wire
Ax+1,y = f (Ax,y-1, Ax,y, Ax,y-1)
-i.e. the state of the cell Ax+1,y =0,1, depends only
on the “upstream” neighbors.
Discussion: Digital wires can be viewed as
hardware implementations of elementary cellular
automata (S. Wolfram). Therefore a digital wire
can be a general purpose computer.
Digital wire (Boolean network, xor rule)
Digital Wire
Data
Program
Direction of
pulse
propagation
Digital Wire
Digital Wire
Digital Wire
Digital Wire
Digital Wire
Discussion, continued …
Digital wires on various scales:
-nano- level: thin film transistor networks (parallel , reliable input for
CPUs, may replace CPU), quantum dot networks, neurons (brain)
-Atomic level: electron hopping from atom to atom along a path on a
macro molecule (hard ware implementations of neural nets)
-Microscopic level: transistor networks
-Mesoscopic level: Boolean networks, Field programmable gate arrays
(image processing)
- Macroscopic level: power lines with phase sensitive switches every 10
miles (no cascading power failures), city traffic
Data transmission lines versus power lines:
There is energy traveling with every pulse. Computation does not
necessarily consume much power (conservative computation).
-Periodic pulses can produce a lot of power.
-Pulses that carry information look random.
H. Higuraskh, A. Toriumi, F. Yamaguchi, K. Kawamura, A. Hübler, Correlation Tunnel Device, U. S. Patent # 5,679,961 (1997)
Digital Wire
Discussion, continued …
Different cellular automata rules:
-Rule 110: general purpose computer
-Rule 204: identity rule
-Rule 30: random number generator
-Rule 254: self-repairing pulses
-Rule 0: trivial
Merging data from different digital wires:
Wire 1 Wire 2
Given is the state
000111010.
What is the pulse one
time step later for rule 0?
000000000
Summary: Digital Wires
Analog wires are used to move energy (power lines,
power grid) and information (data transmission lines,
Digital Wire
Internet) in electrical networks.
-Dynamical systems with more than 7 degrees of freedom are chaotic (Lee Rubel )=>
the dynamics of large networks of analog wires are unstable.
Digital wires: wires that propagate only patterns of rectangular pulses (thresholds)
Specific advantages of digital wires:
- Fixed pulse shape (increased reliability)
- Robust against electric smog (increased reliability)
- No cross talk (increased reliability)
- No echoes (increased reliability)
- Adjustable pulse speed (increased adjustability)
- Encryption (increased security)
- Digital wire can be general purpose computer s(increased adjustability)
Human Neurons are digital wires.
Alfred Hubler, [email protected], Physics, UIUC
http://server10.how-why.com/blog
Atomic Neural Nets: Self-assembly of a particle swarms into
wire networks with thresholds.
random initial distribution
compact initial distribution
Experiment: Agglomeration of conducting particles in an electric field
1) We focus on the dynamics of the system
2) We explore the topology of the networks using graph theory.
3) We explore a variety of initial conditions.
Atomic Neural Nets: Description of experimental setup
source
electrode
battery
boundary
electrode
Basic experiment consists of two
electrodes, a source electrode and a
boundary electrode connected to
opposite terminals of a power supply.
Atomic Neural Nets: Description of experimental setup
source
electrode
battery
particle
Basic experiment consists of two
electrodes, a source electrode and a
boundary electrode connected to
opposite terminals of a power supply.
The boundary electrode lines a dish
made of a dielectric material such as
glass or acrylic.
The dish contains particles and a
dielectric medium (oil)
oil
boundary
electrode
Atomic Neural Nets: Description of experimental setup
20 kV
battery maintains a voltage difference of 20 kV
between boundary and source electrodes
Atomic Neural Nets: Description of experimental setup
source electrode sprays
charge over oil surface
20 kV
Description of experimental setup
source electrode sprays
charge over oil surface
20 kV
air gap between source electrode
and oil surface approx. 5 cm
Atomic Neural Nets: Description of experimental setup
source electrode sprays
charge over oil surface
20 kV
air gap between source electrode
and oil surface approx. 5 cm
boundary electrode has a diameter of
12 cm
Atomic Neural Nets: Description of experimental setup
needle electrode sprays
charge over oil surface
20 kV
air gap between needle electrode
and oil surface approx. 5 cm
boundary electrode has a diameter of
12 cm
oil height is approximately 3 mm,
enough to cover the particles
castor oil is used: high viscosity, low
ohmic heating, biodegradable
Atomic Neural Nets: Description of experimental setup
needle electrode sprays
charge over oil surface
20 kV
air gap between needle electrode
and oil surface approx. 5 cm
ring electrode forms boundary of dish
has a radius of 12 cm
particles are non-magnetic
stainless steel, diameter D=1.6 mm
particles sit on the bottom of the
dish
oil height is approximately 3 mm,
enough to cover the particles
castor oil is used: high viscosity, low
ohmic heating, biodegradable
Phenomenology Overview
12 cm
stage I:
strand
formation
t=0s
10s
5m 13s
14m 7s
Phenomenology Overview
12 cm
stage I:
strand
formation
t=0s
stage II:
boundary
connection
14m 14s
10s
5m 13s
14m 7s
Phenomenology Overview
12 cm
stage I:
strand
formation
stage II:
boundary
connection
t=0s
10s
5m 13s
14m 14s
14m 41s
15m 28s
stage III: geometric expansion
14m 7s
Phenomenology Overview
12 cm
stage I:
strand
formation
stage II:
boundary
connection
t=0s
10s
5m 13s
14m 7s
14m 14s
14m 41s
15m 28s
77m 27s
stage III: geometric expansion
stationary
state
Motion of the strands: pointed equilibrium
The motion of the lead
particles of the six largest
strands from a single
experiment.
Adjacency defines topological species of each particle
Termini = particles
touching only one other
particle
Branching points =
particles touching three
or more other particles
Trunks = particles
touching only two other
particles
Particles become one of the above three types in stage II and III. This
occurs over a relatively short period of time.
Relative number of each species is robust
Graphs show how the number of termini, T, and branching
points, B, scale with the total number of particles in the tree.
Most networks are trees.
Only a few rare cases contain loops (cycles).
Loops (cycles) are unstable
Insets on the left show two particles artificially placed into a
loop separate from one another.
The graph on the right shows the separation between the two
particles as a function of time.
Fractal Dimension
Particles arrange themselves similarly in different experiments.
Overall electrical resistance of system
The resistance decreases as a function of time. The limiting value is
reproducible. If the current is fixed, the system minimizes energy
consumption.
Predicting Network Growth: Qualitative effects of initial
distribution
Qualitative Predicting Network Growth: Qualitative effects
of initial distributions of initial distribution
N = 752
T = 149
B = 146
N = 785
T = 200
B = 187
N = 720
T = 122
B = 106
N = 752
T = 131
B = 85
Initial conditions have a strong influence on the number of trees and
are a strong constraint on the final form of tree(s).
Qualitative Predicting Network Growth: Qualitative effects
of initial distribution
Will this initial configuration produce a spiral?
Qualitative Predicting Network Growth: Qualitative effects
of initial distribution
No, system is unstable to ramified structures.
Qualitative Predicting Network Growth
Since topology of the networks is established relatively quickly, particles
connect to one another before they have moved far.
Thus, we attempt to model the connections formed by the system using only
the local information for each particle—it’s neighborhood.
We use data from the
experiments: a snapshot of
the particles directly
preceding stage II.
Qualitative Predicting Network Growth
Since topology of the networks is established relatively quickly, particles
connect to one another before they have moved far.
Thus, we attempt to model the connections formed by the system using only
the local information for each particle—it’s neighborhood.
We take data from the
experiments: a snapshot of
the particles directly
preceding stage II.
Digitize the
positions.
Run the adjacency
algorithm to
obtain a base
neighborhood.
cutoff length = 3  particle diameter
Predicting Network Growth: Sequences of disruptions with different likelihood
loner
Growth models:
Particles articles can only connect
to particles that neighbor it.
Algorithms run until all available
particles connect into a tree.
Some particles will not connect to
any others (loners). They
commonly appear in experiments.
loner
We chose three growth models:
1) random growth model: all neighbors equally likely to connect, but no loops
2) minimum spanning tree model: closer neighbors a more likely, no loops
3) propagating front model: one neighbor has to be connected, no loops
Predicting Network Growth: Random Growth Model
Typical connection structure from RAN algorithm.
Distribution of termini produced from 105
permutations run on a single experiment.
Number of termini produced for all
experiments, plotted as a function of N.
Predicting Network Growth: Minimum Spanning Tree Growth
Typical connection structure from MST algorithm.
Distribution of termini produced from 105
permutations run on a single experiment.
Number of termini produced for all
experiments, plotted as a function of N.
Predicting Network Growth: Propagation Front Model
Typical connection structure from PFM algorithm.
Distribution of termini produced from 105
permutations run on a single experiment.
Number of termini produced for all
experiments, plotted as a function of N.
Comparison of all models to experiments
The number of termini and branching points for all three models
and the natural experiments.
The minimum spanning tree model produces the most accurate
prediction of the experimental data.
Predicting the growth of a fractal particle network.
random initial distribution
compact initial distribution
Experiment: J. Jun, A. Hubler, PNAS 102, 536 (2005)
1) Statistically robust features: number of termini, number of branch points,
resistance, open loop, Three growth stages: strand formation, boundary
connection, and geometric expansion;
2) Features that depend sensitive on noise, initial conditions and other external
influences: number of trees, ….
3) Minimum spanning tree ensemble predictor predicts emerging pattern best
therefore these self-assembling, self-repairing networks could be used as
ensemble predictors.
Applications: Hardware implementation of neural nets, nano neural nets with
SC particles - M. Sperl, A Chang, N. Weber, A. Hubler, Hebbian Learning in the
Agglomeration of Conducting Particles, Phys.Rev.E. 59, 3165 (1999)
Hebbian Learning in a three-electrode system:
Pattern recognition, abstraction, innovation by
association …
M. Sperl, A Chang, N. Weber, A. Hubler, Hebbian Learning in the
Agglomeration of Conducting Particles, Phys.Rev.E. 59, 3165 (1999)
Atomic Neural Nets: Basic Units
Energy = positive experience
Figure: A self-assembling wires unit interacting with a virtual environment.
Atomic Neural Nets: Pre-wired Networks of Basic Units
Energy = positive experience
Figure: A network of basic units with nonlinear input nodes with a threshold. The lines indicate
pre-wired connections. Sub-network may emerge, when the self-assembling wires use certain
pre-wired connections and ignore others.
Atomic Neural Nets
Experiments by Peter Fleck et al. show that
superconducting nano- particles behave similarly.
The wires of such atomic neural nets, have a
diameter of roughly 1 nanometer, whereas
human neurons have a diameter of roughly 1
micrometer. Therefore:
-1 billion atomic neural net neurons fit have the same volume as one human neuron
- the power consumption of these 1 billion atomic neural net neurons is less than that of one
human neuron
- the behavior of atomic neural net neurons, depends on materials, geometries, …
Conclusion: The number of neurons in Atomic Neural Nets can exceed number of neurons in
human brains by a factor of 109 and use less power.
Levels of Understanding of Perceptrons (=Machine with understanding)
Understanding =
ability to translate
-between observations and a
conceptual network (virtual
world)
-between conceptual networks
Atomic neural nets may reach a level of understanding
that is incomprehensible for humans
… and speak and read English (such as the How-Why tutoring system)
Energy and IT Technology in 20 Years:
A Prediction Based on Current Research
Progress
Current Research Progress:
- Digital Batteries (material with highest energy density & power
density, inexpensive, nano-scale)
- Digital Wires (robust power distribution & storage, move and process
information)
- Atomic Neural Nets (nano-scale particle swarms, which self-assemble
into fractal patterns, which detect patterns, make abstractions; which
innovate by association; which exceed computational capacity of humans
by a factor of 109, and need less power)
Predicted Technological Breakpoints:
- Merger of information and energy devices
- Innovation driven by self-assembling ANN which ‘understand’ the
world better than humans