



    Nanotechnology 101:  Where can I find such a course?  




Many students have asked what they should study to prepare
for careers in nanotechnology. Giving a decent answer requires
outlining the different fields of research that fall under the
nanotechnology umbrella and describing the background knowledge
required to work in them. It also seems wise to say something about
the different levels of knowledge and modes of learning that are
relevant to such a broad, interdisciplinary area. The
following is a personal view, based on what I have learned (and wished
I had learned), and on how learning in these areas seems to work best.


Fields of research

Nanotechnology will mean complete control of the structure of matter,
building complex objects with molecular precision. It doesn't exist
yet, because we don't have molecular assemblers yet. Work related
to nanotechnology accordingly falls into two broad areas: the study of
nanotechnology itself (which must remain theoretical, for the time
being) and research on enabling technologies leading toward assemblers
and nanotechnology (which can be theoretical in part, but which also
has an experimental, developmental component).

The theoretical study of nanotechnology involves exploratory
engineering work in any of several of areas. It includes basic studies
in nanomechanical engineering (the study of molecular machines) and
nanoelectrical engineering (the study of molecular and
atomically-precise nanometer-scale electronic systems). It also
includes studies of complex systems, such as assemblers, replicators,
and nanocomputers. More broadly, it includes studies of non-nanoscale
applications, such as large systems built by teams of assemblers.

Because we lack the tools to do real nanotechnology today, these
theoretical studies amount to building castles in the air.
Accordingly, there is little funding for such efforts and frequent
skepticism about their value. Nonetheless, such studies can be pursued
with intellectual discipline, yielding firm results and a better
understanding of our choices as a society. They have been my main
focus and have spawned the current interest in
nanotechnology--including the interest in giving these theoretical
castles hardware foundations.

Studying what can be done with assemblers yields more foresight than
it does progress; working to develop assemblers yields more progress
than it does foresight. Inevitably, more resources will go into
development than into theory, because technology development will
yield practical, short-term results on the way to long-term
objectives. It makes no practical sense to try to build an assembler
today, but it does make sense to build tools today that will make it
easier to build assemblers tomorrow. These tools are termed
"enabling technologies."

Promising enabling technologies fall into several familiar categories.
These include:

	protein engineering (involving efforts to develop techniques
for designing molecular devices made of protein),

	general macromolecular engineering (involving efforts to
develop techniques for designing and synthesizing molecular devices
made of more tractable materials)

	micromanipulation techniques (involving efforts to extend the
technology of scanning tunneling and atomic force microscopy to
chemical synthesis, and then to the construction of molecular
devices).

These approaches have differing strengths and weaknesses. Protein
engineering can draw on a host of examples and prototypes from nature,
and can exploit existing self-replicating machines (bacteria) to make
products cheaply--a major consideration, where short-term payoffs
are concerned. General macromolecular engineering avoids the major
problem with protein engineering (proteins, not having been designed
for designability, are hard to design), but at the cost of moving away
from natural prototypes and requiring more expensive chemical
synthesis techniques for making near-term products (thus reducing the
potential market). Micromanipulation techniques promise to ease design
problems by allowing direct construction of molecular objects, but
they suffer from higher costs: a chemical reaction typically makes
many trillions of molecules at once, while a manipulator would make
but one, hence manipulator-made products can be expected to cost
trillions of times more, dramatically reducing the potential market.
Also, as of this writing, micromanipulation has not achieved even a
single chemically-specific step in molecular synthesis, while chemists
have built specific molecules containing thousands of atoms.

All the above areas bear watching, and all will be pursued to some
extent, regardless of which ultimately proves to have the biggest
payoff. Hybrid approaches, combining techniques from several of these
areas (e.g., micromanipulation of molecular tools), seem promising.
Finally, improved computational modeling of molecular systems is a
generic enabling technology, relevant to all these approaches.

Background fields: molecular science and technology

There are, as yet, no college curricula aimed at preparing students
for work in nanotechnology. My own course at Stanford provided at best
an overview of the field. Rather than seeking courses (and books, and
journals) in nanotechnology, one should seek courses (etc.) in the
broad field of molecular science and technology.

Unfortunately, there are, as yet, few (if any) schools that treat
molecular science and technology as a unified field. (A note to
curriculum reformers: developing a program having this focus makes
sense in terms of current science and technology, and would provide a
natural home for early studies in nanotechnology.) Students aiming to
gain a solid background in areas important to nanotechnology should be
prepared to shop around from department to department. The following
section lists some of the important topics and some of the departments
in which they are frequently taught.

To study science and technology in a serious way, one must have an
adequate background in mathematics. Basic calculus is essential, and
differential equations and linear algebra are widely used. Problems in
nanotechnology vary widely in the mathematical sophistication required
for their solution.

The study of physical systems is founded on physics. A knowledge of
basic classical mechanics and electromagnetism is essential, as is a
knowledge of at least the rudiments of quantum mechanics. Anyone
aiming to do any sort of sophisticated work in chemistry and molecular
machines can benefit from deeper knowledge of quantum mechanics;
anyone interested in molecular electronics should make quantum
mechanics a chief focus of study. "Quantum mechanics" is a broad
area, however. The quantum mechanics of interest here is not quantum
electrodynamics, quantum chromodynamics, or superstring theory, but
the garden-variety quantum mechanics of electrons in matter, the sort
studied by chemists and solid-state physicists. Both quantum chemistry
and solid state physics are topics of great relevance to
nanotechnology.

As with mathematics, so with physics: problems in nanotechnology vary
widely in the sophistication needed for their solution.

Nanomachines and nanoelectronic devices are often greatly influenced
by thermal noise. To understand its effects, one needs knowledge of
thermodynamics and of statistical mechanics. Thermodynamics deals with
the flow of energy and heat in matter in bulk; its principles
constrain all physical systems and its subject matter is regarded as a
prerequisite for the study of statistical mechanics, which describes
much the same territory in a more detailed, molecular fashion. These
topics are often taught in chemistry and physics departments.

Nanotechnology can be viewed as an outgrowth of chemistry,
the leading science in the field of molecular devices and molecular
manipulation. Anyone planning serious work in nanotechnology should
seek at least a basic background in chemistry, focusing on its
structural, molecular aspects. Those interested in assemblers and
molecular mechanical devices should study organic chemistry, and those
interested in the chemical-synthesis path to nanotechnology should
study synthetic organic chemistry, and learn the arts of the chemistry
lab.

Many specific fields have special relevance. Chemical kinetics and
reaction transition-state theory is of special relevance to assembler
theory. Molecular mechanics is fundamental to any sort of molecular
machine design. Studies in materials science (often considered closely
allied to chemistry) are also of value; materials scientists consider
the mechanical behavior of larger systems of bonded atoms than
chemists typically contemplate.

Biology is the leading science in the study of existing molecular
machines. Here, biochemistry is central: enzyme reaction mechanisms
provide examples of what many nanomachines will need to do; the
folding of proteins and the self-assembly of protein systems provide
examples of how complex first-generation molecular machines may be
made. Familiarity with these fields is of considerable importance to
anyone interested in enabling technologies.

Although nanotechnologists will need a thorough grounding in relevant
scientific principles, nanotechnology is fundamentally a branch of
engineering. To work as an engineer, one must learn to think as an
engineer, and that means studying (and doing) design. Nanosystems will
be systems, and so the principles of systems engineering apply. Many
nanosystems will be mechanical, and so the principles of mechanical
engineering apply. Studies in solid mechanics, system dynamics,
mechanisms, and control theory all are relevant to both nanotechnology
and enabling technologies. Engineering departments often teach more
specialized topics of relevance to nanotechnology, such as VLSI
circuit design (relevant to nanocomputer design) and microfabrication
(relevant to possible enabling technologies). The principles of
conventional electronic circuit design are applicable to moderately
large nanoelectronic systems, and the principles of quantum
electronics are applicable to the smallest systems.

Software systems will be vital to nanotechnology and to enabling
technologies along the way. A basic introduction to computers and
software (preferably not in BASIC) will be of value to anyone in any
area of science or technology. Those interested in software related to
nanotechnology should pay special attention to numerical simulation
methods for molecular mechanical and quantum electronic systems, and
to the design of programs for highly parallel computer systems, since
this is the direction hardware will be moving in the coming years.
Parallel systems will help designers develop nanotechnology, and
nanocomputers will later be used to build massively parallel (trillion
processor and up) computer systems. Finally, if powerful systems are
to be useful in molecular design, they will need to be accessible
through fast, clean, intuitive interfaces that let designers see and
manipulate model molecules.

Levels of knowledge

"In short, to do good work in nanotechnology, one must master
everything relevant to the physics, chemistry, and engineering of
molecules, from quantum mechanics to advanced software
architectures." Fortunately, this isn't true. Of course, the
more you know, the better you'll do (within limits--studying
mustn't completely displace doing), but one can't master
everything relevant to so broad a field.

What one can and should do is try to master some areas and know a lot
about the others. Real molecular devices can do many different things:
they can vibrate, pull apart, shake apart, deform, transform,
photolyse, or pop from state to state--any of these behaviors can
occur in a simple mechanical part, and any can make it fail. Real
physical systems will do something when used, and if what they will do
is strikingly different from what you think they will do, then the
work you're doing may be a waste of time for you and for anyone who
listens to you. It's much better to be right about what will work,
and this means knowing enough to steer clear of potential problems.

It makes sense to think in terms of three levels of knowledge about a
field:

1) Knowing what a field is about--knowing what sorts of physical
systems and phenomena it deals with, and what sorts of questions it
asks and answers.

2) Knowing the content of a field in a qualitative sense--having a
good feel for what sorts of phenomena can be important in what
circumstances, and knowing when you need answers from work in that
field.

3) Knowing how to get those answers yourself, based on personal
mastery of enough of the field's subject matter.

If one has enough knowledge at levels (1) and (2) in enough fields,
then one can steer clear of problems in those fields while doing work
in a related field where you have knowledge at level (3). And this is
a good thing, because knowledge at levels (1) and (2) takes far less
time to acquire. But to make proper use of knowledge at levels (1) and
(2) requires a harsh discipline: attempt to assume the worst about
what you don't know. Don't assume that a poorly-understood
physical effect will somehow save your design; do assume (until
finding otherwise) that it may utterly ruin it. Without this
discipline, you'll become an intellectual hazard. With it,
you'll be able to make a real contribution.

Modes of learning

How can one get this sort of general knowledge of a field? Courses can
help, but they tend to focus on mastery of a narrow range of
knowledge, rather than familiarity with a wide range of knowledge. One
can gain this familiarity by reading magazines and journals that offer
broad coverage of science and technology: good choices include
Science, Nature, Science News, Scientific American, and IEEE Spectrum.
Another good tactic is to skim a wide range of books on the new-books
shelf of a science library, on a regular basis, and to do likewise
with a wide range of technical journals.

To do all this properly requires the discipline to read what you
don't understand--despite the school-induced reflex which says
"Oh, no! I don't understand, so I'll fail the test--maybe I
should drop this subject!" By reading what you don't understand,
you gain a sense of the patterns of the field--the terms and
abstract relationships, the kinds of problems being addressed, and the
kinds of knowledge required to understand more. And this adds up to an
important sort of understanding. Later, this familiarity makes it much
easier to consult the literature: one knows which disciplines deal
with what problems, and what one needs to study to gain a deeper
understanding. Also, it fills your mind with questions, so that you
can later recognize the answers and have your mind seize them more
firmly.

For a thorough grounding in a basic field, classes can be excellent.
If classes aren't available, textbooks can often serve well,
especially if you work many of the problems.

In any evolving, interdisciplinary field, you must learn to learn from
books and journals. Learn to use libraries (as horrible as they are,
compared to tomorrow's hypertext publishing systems). Learn to read
skeptically--it is a rare book or journal that doesn't have a few
serious errors, and occasionally publications are utter bilge,
especially in interdisciplinary fields (which too often lack any
discipline at all).

Finally, tackle problems. If you can find a professor doing good,
interesting work, consider becoming an apprentice researcher. If not
(or in addition), pursue technical problems that interest you. The
best way to learn is to seek answers to questions that interest you,
and there is no other way to make an original contribution.

Learn to criticize ideas, especially your own. Most new ideas are
wrong or inadequate. If you don't reject most of your ideas
promptly, then you're almost surely fooling yourself, and if you
also spread them, you're almost surely polluting the intellectual
world. But if an idea really seems to stand up under testing, try
filling in more details, and criticizing it again.

Get criticism from others. Learn to present ideas in discussions,
papers, and talks, and listen to the responses, especially from people
who know relevant fields. If they disbelieve your idea and tell you
why, either understand and refute their criticism, or consider working
on a different idea. If they look at you oddly and change the subject,
consider whether you are perhaps overlooking a really big, basic
problem--are you really familiar with the relevant fields? If they
disbelieve you at first, but can be persuaded, congratulations!
You've probably got hold of something interesting, perhaps even new
and important.

Always remember that ideas about real systems must somehow be
disciplined by reality. Experimental work brings its own discipline
from nature, if the experimenter uses good technique. This discipline
is direct and hard to escape. Theoretical work, in contrast, must be
disciplined by knowledge of experimental results and natural law; this
discipline doesn't impose itself, it must be sought out and largely
self-applied. To be a careful thinker, try to understand things in
more than one way: if you get the same answer from physical
calculations and from analogies to known machines and from analogies
to biology, then you're probably right. If all you have is a rough
analogy or a crude calculation, you may well be wrong.

Seek out weaknesses in ideas, and build only on ideas that pass
rigorous tests, or you may see the foundations of your thinking later
crumble and dump a year's (or a decade's) work into the trash.
Beware of those who have neither experimental results nor a
theoretician's voluntary discipline; expect them to spout great
streams of plausible nonsense, unconstrained by reality. Don't
become one of these, even if you find that many (ignorant) people are
intrigued and entertained by your wilder imaginings.

In short, learn the fundamentals of molecular science and technology.
Survey other relevant knowledge. Learn to learn from books and
journals. Pursue problems, think critically, and learn more. Design
and calculate or experiment. Publish your contribution and add to the
world's knowledge. Good luck.

