Energy and IT Technology in 20 Years: A Prediction Based on Current Research Progress

1
Energy and IT Technology in 20 YearsA
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)

2

• 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 quantum-
• mechanically 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 gt 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/

3
Energy storage in conventional capacitors Capacit
ors 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 Ce A / d is the capacity, V applied
voltage, e electric constant, A plate area, d
plate distance The energy density is
w ½ e E2 ,
(2)
Where the electric field, E V/d
However, if the
energy density in conventional
capacitors exceeds E3
x 106V/m in air (6 x
107V/m in Teflon) the capacitor
discharges by
arcing and the energy is lost. gt
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.
4
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 gt 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 moleculesand
carbohydrates, such as gasolineare 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 ltlt limit. Energy storage in faradic
systems has low efficiency and is limited by
diffusion, reaction rates, fractal growth
irreversible chemical reactions.
5

• 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,
• 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

Digital battery
Break down probability versus junction size
(Alpert et al, Boyle t al., Hubler et al.)
6
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) 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 are similar to nano plasma
tubes, except that they store energy instead of
converting it to light
7
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.
8
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/ch
fquantificationofmaterialsrolea.pdf
9
Nano-junction arrays as Digital Batteries One
could design large arrays of individually
connected nano-junction, 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
10

• 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 quantum-
• mechanically 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 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 batteries are similar to flash drives
flash drives store charge, while digital
batteries store energy
11

• Digital Wires
• Alfred Hubler, a-hubler_at_illinois.edu, Physics,
  UIUC http//server10.how-why.com/blog
• 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 gt the dynamics
  of large networks of analog wires is unstable gt
  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 Wire
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Digital wires
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Digital Wires hardware implementation as a
transistor network
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Digital Wires Hardware implementation as a
Boolean network
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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 Ax1,y f (Ax,y-1,
Ax,y, Ax,y-1) -i.e. the state of the cell
Ax1,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
Digital wire (Boolean network, xor rule)
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Digital Wire
Data Program
Direction of pulse propagation
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Digital Wire
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Digital Wire
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Digital Wire
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Digital Wire
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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)

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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

Given is the state 000111010. What is the pulse
one time step later for rule 0? 000000000
Wire 1
Wire 2
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• Summary Digital Wires
• Analog wires are used to move energy (power
  lines,
• power grid) and information (data transmission
  lines,
• Internet) in electrical networks.
• -Dynamical systems with more than 7 degrees of
  freedom are chaotic (Lee Rubel )gt 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, a-hubler_at_illinois.edu, Physics,
  UIUC http//server10.how-why.com/blog

Digital Wire
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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.
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Atomic Neural Nets Description of experimental
setup
Basic experiment consists of two electrodes, a
source electrode and a boundary electrode
connected to opposite terminals of a power supply.
source electrode
battery
boundary electrode
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Atomic Neural Nets Description of experimental
setup
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)
source electrode
battery
particle
boundary electrode
oil
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Atomic Neural Nets Description of experimental
setup
20 kV
battery maintains a voltage difference of 20 kV
between boundary and source electrodes
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Atomic Neural Nets Description of experimental
setup
source electrode sprays charge over oil surface
20 kV
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Description of experimental setup
source electrode sprays charge over oil surface
20 kV
air gap between source electrode and oil surface
approx. 5 cm
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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
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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
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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
oil height is approximately 3 mm, enough to cover
the particles castor oil is used high viscosity,
low ohmic heating, biodegradable
particles are non-magnetic stainless steel,
diameter D1.6 mm particles sit on the bottom of
the dish
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Phenomenology Overview

12 cm
stage I strand formation
t0s
10s
5m 13s
14m 7s
34
Phenomenology Overview

12 cm
stage I strand formation
t0s
10s
5m 13s
14m 7s
14m 14s
stage II boundary connection
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Phenomenology Overview

12 cm
stage I strand formation
t0s
10s
5m 13s
14m 7s

14m 14s
14m 41s
15m 28s
stage II boundary connection
stage III geometric expansion
36
Phenomenology Overview

12 cm
stage I strand formation
t0s
10s
5m 13s
14m 7s

14m 14s
14m 41s
15m 28s
77m 27s
stage II boundary connection
stage III geometric expansion
stationary state
37
Motion of the strands pointed equilibrium
The motion of the lead particles of the six
largest strands from a single experiment.
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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.
39
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.
40
Most networks are trees.Only a few rare cases
contain loops (cycles).
41
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.
42
Fractal Dimension
Particles arrange themselves similarly in
different experiments.
43
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.
44
Predicting Network Growth Qualitative effects of
initial distribution
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Qualitative Predicting Network Growth
Qualitative effects of initial distributions of
initial distribution
N 752 T 131 B 85
N 720 T 122 B 106
N 785 T 200 B 187
N 752 T 149 B 146
Initial conditions have a strong influence on the
number of trees and are a strong constraint on
the final form of tree(s).
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Qualitative Predicting Network Growth
Qualitative effects of initial distribution
?
Will this initial configuration produce a spiral?
47
Qualitative Predicting Network Growth
Qualitative effects of initial distribution
No, system is unstable to ramified structures.
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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
particleits neighborhood.
We use data from the experiments a snapshot of
the particles directly preceding stage II.
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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
particleits 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
50
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
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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.
52
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.
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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.
54
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.
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Predicting the growth of a fractal particle
network.
random initial distribution
compact initial distribution

• Experiment J. Jun, A. Hubler, PNAS 102, 536
  (2005)
• Statistically robust features number of termini,
  number of branch points, resistance, open loop,
  Three growth stages strand formation, boundary
  connection, and geometric expansion
• 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)

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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)
57
Atomic Neural Nets Basic Units
Energy positive experience
Figure A self-assembling wires unit interacting
with a virtual environment.
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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.
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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.

60
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)
61
Energy and IT Technology in 20 YearsA
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