Fusion on the Horizon?

When I arrived at MIT in 1976, fresh off the bus from Oklahoma, nuclear fusion looked like an exciting scientific career. The country was still reeling from "the" energy crisis (oil was over $50/barrel in today's prices!), and fusion was the energy source of the future.

It still is.

The promise has always been compelling, and is often described as "unlimited pollution-free energy from seawater." The fusing of two hydrogen nuclei to form a helium nucleus, releasing abundant energy without the radioactive products of nuclear fission, certainly seems cheap and clean. Indeed, this kind of process is the ultimate source of all solar energy as well, and the H-bomb showed that we can create it on earth.

So the challenges for fusion are not fundamental. They're "just engineering."

Foremost among these challenges is keeping the hydrogen nuclei together when they're heated to millions of degrees. This temperature is needed so they can overcome their natural electrical repulsion, but when they have a lot of energy they're just as likely to go in other directions. Sadly, techniques to confine these tiny nuclei seem to require tons and tons of expensive, high-tech equipment. Of course, advocates of cold fusion, now called "Low Energy Nuclear Reactions," think they don't have to solve this problem, but most scientists are unconvinced.

The traditional approach to fusion, then being pursued at MIT, involves confining a donut-shaped plasma of ultra-hot charged particles by using an enormous magnetic field. One problem is that the plasma finds all sorts of ways to wiggle out of the confinement. Over the decades, researchers have made steady progress in controlling these "instabilities." Recent research, still done at MIT and published online in Nature Physics this week, used a surprising technique of levitating a half-ton magnet in mid-air.

The other mainstream approach is to squeeze and heat hydrogen-containing materials by blasting a pellet with powerful lasers from all sides. Research at Lawrence Livermore's National Ignition Facility, published online in Science this week, showed promising results for this approach.

I've always found the idea of milking a steady stream of power out of occasional explosions inside of a horrendously expensive, delicate laser apparatus confusing. In fact, the long-defunct radical magazine Science for the Peoplepublished an article in 1981 claiming that "inertial confinement" fusion was just a plot by the military to test fusion explosions in the lab. That at least made sense.

The new results seem like steps forward for both approaches, but there's a long way to go. For one thing, neither group actually fused anything. They just set up conditions that seemed promising.

The reality is that no researchers want to actually use fusion-capable fuel in their machines, because it would make them radioactive (the machines, not the researchers). This may sound surprising, since fusion is supposed to be so clean. But although fusion doesn't produce radioactive nuclei, it does make a whole lot of high-speed neutrons. To generate power, researchers would need schemes to extract the energy from these neutrons. But the neutrons also irradiate everything in sight, turning much of the apparatus into hazardous waste, which would make experiments much harder.

But if the researchers keep making progress, they're going to have to use the real stuff soon. They'll look for any fusion at all, and eventually for "scientific breakeven," where they get more energy out than they use to power all the equipment. "Commercial breakeven," where the whole endeavor makes money, is much further down the road.

I wish the researchers good luck; they may yet save our planet. But I'm also glad I didn't decide to spend the last third of a century working on fusion.

Mix and Match

It's easier to reconfigure a complex system to do new things if it is built from simpler, independent modules. But in biology, modules may be useful for more immediate reasons.

Biological systems, ranging from communities to molecular networks, often feature a modular organization, which for one thing makes it easier for a species to evolve in response to changes in the environment. In some cases, this flexibility might have been selected during prior changes. But modularity can also make life easier for a single organism during its lifetime, and be selected for this reason.

In their book The Plausibility of Life, Marc Kirschner and John Gerhart include modularity as one aspect of "facilitated variation." In particular, they say, genetic changes that affect the "weak linkages" between modules can cause major changes in the resulting phenotype. As long as the modules, the "conserved core processes," are not disrupted, the resulting organism is likely to be viable, and possibly an improvement on its predecessors.

In describing facilitated variation, Kirschner and Gerhart defer the question of whether facilitating rapid future evolution alone causes these features to be selected. Perhaps it does, in some circumstances. But in any case, we can regard the presence of features that enable rapid reconfiguration as an observational fact.

Moreover, the practical challenges of development demand the same sort of robust flexibility that encourages rapid evolutionary change. Over the development of a complex organism, various cells are exposed to drastically different local environments. In addition, genetic or other changes pose unpredictable challenges to the molecular and other systems of the cells. Throughout these changes, critical processes, like metabolism and DNA replication, need to keep working reliably.

To survive and reproduce in the face of these variations organisms need a robust and flexible organization. Features that allow such flexibility should be selected, if only because they improve individual fitness. These same features may then increase evolutionary adaptability, whether or not that adaptability is, by itself, evolutionarily favored.

Whether adaptability is selected for its evolutionary potential or only for helping organisms thrive in a chaotic environment, it has a profound effect on subsequent evolution. A flexible organization including modularity and other features allows small genetic alterations to be leveraged into large but nonfatal changes in the developing creature, so the population can rapidly explore possible innovations.

Changing Times

One way to explain the modularity that is seen in biology is that it helps species to evolve quickly as their environment changes.

But the notion that "evolvability" can be selectively favored is tricky intellectual territory, and people can get drawn in to sloppy thinking. Just as group selection must favor more than just "the good of the species," selection for flexibility cannot be grounded in future advantages to the species. To be effective, evolutionary pressure must influence the survival of individuals in the present.

Whether this happens in practice depends on a lot of specific details. Some simulations of the effect of changing environments have not shown any effect. But at a meeting I covered last year for the New York Academy of Sciences, Uri Alon showed one model system that evolves modularity in response to a changing environment.

Alon is well known for describing of "motifs" in networks of molecular interactions. A motif is a regulatory relationship between a few molecules, for example a feed-forward loop, that is seen more frequently in real networks than would be expected by chance. It can be regarded as a building block for the network, but it is not necessarily a module because its action may depend on how it connects with other motifs.

Alon's postdoc Nadav Kashtan simulated a computational system consisting of a set of NAND gates, which perform a primitive logic function. He used an evolutionary algorithm to explore different ways to wire the gates. Wiring configurations that came closest to a chosen overall computation result were rewarded by giving making future generations more likely to resemble them. "The generic thing you see when you evolve something on the computer", Alon said, "is that you get a good solution to the problem, but if you open the box, you see that it's not modular." In general, modules cannot achieve the absolute best performance.

Kashtan then periodically changed the goal, rewarding the system for a different computational output. Over time, the structure of the surviving systems came to have a modular structure. One interesting surprise was that in response to changing goals, the simulated systems evolved much more rapidly than those exposed to a single goal.

But Alon emphasized that this was not a general feature. Instead, the different goals needed to have sub-problems in common. Evolution would then favor the development of dedicated modules to deal with these problems. It is easy to imagine the challenges facing organisms in nature also contain many recurrent tasks, such as the famous "four Fs" of behavior: feeding, fighting, fleeing, and reproducing.

So some biological modularity may reflect the evolutionary response to persistent tasks within a changing environment. But does this explain the wide prevalence of modules? In a future post, I will examine another explanation: that modularity is one of the tools that helps individual organisms adapt to the changing conditions of development and survival during their own lifetimes.

Modules

When people design a complex system, they use a modular approach. But why should biology?

For us, modularity is a way to limit complexity. Breaking a big problem into a series or hierarchy of smaller ones makes it more manageable and comprehensible, which is especially important if it is assembled by many people--or one person over an extended time.

The key to a successful module is that its "guts"--the way its parts work together--doesn't depend on how it connects to other stuff. The module can be thought of as a "black box," that just does its job. You don't have to think about it again. For this to work, the connections between modules must be weak, limited to well-defined inputs and outputs that don't directly affect its internal workings.

When it's done right, a module can be easily re-used in new situations. For example, the part of a computer operating system that offers a help menu is tapped by lots of programs without worrying about how it works.

But biology is not designed. Biological systems emerge from an evolutionary process that rewards only survival and reproduction, with no regard for elegance or comprehensibility. Re-usability sounds like a good thing in the long run, but doesn't help an individual survive in the here and now.

Nonetheless, modularity seems to be widespread feature of biological systems. Your gall bladder, for example, is a well-defined blob that receives and releases fluids like blood and bile, but otherwise keeps its own counsel. It can even be removed if necessary. And it does much the same thing in other people and animals.

At a smaller scale, many of the basic components of cells are the same for all eukaryotes. They have the discrete nuclei that define them, and they also have other organelles that perform essential functions, like mitochondria that generate energy. These modules work the same way, whether they happen to appear in a brain cell or a skin cell.

Even at the molecular level, re-usable modules abound. For example, although ribosomes don't have a membrane delimiting them, they consist of very similar bundles of proteins and RNA for all eukaryotic cells, and only modestly different bundles for bacteria. In addition to such complexes, many "pathways," or chains of molecular interactions, recur in many different species.

We have to be careful, of course: simply because we represent complex biological systems as modules doesn't mean they are there. The modules we think we see could simply reflect our limited capacity to understand messy reality. But when researchers have looked at this question carefully, they found that modules really exist in biology, much more than they would in a random system of similar complexity.

But why should evolution favor modular arrangements? And how does a modular structure change the way organisms evolve?

Don't Fear the Hyphen

Hyphens come up a lot in scientific writing. Or at least they should. Unfortunately, small as they are, many people are afraid of them.

One problem is that hyphens get used for some very different purposes, although all of them tend to bind words or fragments together. I'm only going to talk here about making compound words with them.

Another problem is that you don't have to use a hyphen if you don't have to: if the meaning is clear without it (semantics), you're allowed to omit it (punctuation). This makes it very hard to figure out the real punctuation rules, since they don't arise from syntax alone.

Compound words are hard to predict. Sometimes two words are locked together to form one new word, as in German: eyewitness. Sometimes the two words keep their distance even as they form a single, new concept: eye shadow. But some pairs are bound with the medium-strength hyphen: eye-opener. This mostly happens when both words are nouns, so that the first noun is acting as an adjective, making the second more specialized.

There's really no perfect way to know which form is favored, and it varies over time. Novel pairings generally start out separated and become hyphenated when they seem to represent a unique combined identity. When that combined form becomes so familiar that it is easily recognized, the hyphen disappears, (unless the result would be confusing, as in a recent example, "muppethugging," or the less novel "cowriting.") You just have to look in an up-to-date dictionary.

For the single compound words and those that are always hyphenated that's the end of the story. The problem is that the isolated pairs of words sometimes should be hyphenated, too. This hyphenation is not a property of the pair, and it can't be found in the dictionary. It's a real punctuation mark, and depends on the details of the sentence.

The hyphen belongs when the pair is used as an adjective, known as a compound modifier, as in the previous "medium-strength hyphen." But generally the hyphen is omitted when the adjective occurs later on: "The hyphen has a medium strength." But the AP Stylebook (not hyphenated!) says that when that "after a form of to be, the hyphen usually must be retained to avoid confusion: The man is well-known." AP also has a rule that when for an adverb-adjective pair, the hyphen is not used if the adverb is "very" or ends in "-ly."

OK, this is getting confusing, so let's regroup: The important principle is that the hyphen is there to make clear when there is a link that might otherwise be missed. If we talk about a "highly important person," it's clear that it's the importance that's high, not the person. But if we talk about a "little known person," it's not so obvious whether it's the knowledge or the person that's diminutive, because
"little" can be an adjective or an adverb. If it's an adjective, you might have said "little, known person, " but "little-known person" avoids any chance of confusion.

The problem is worse when the first word is a noun, because it doesn't really give you any syntax clues about whether it's acting as an adjective or adverb. This issue comes up frequently in science writing. I imagine most people will realize that a "surface area calculation," refers to a calculation of surface area, and not a surface calculation of the area. But sometimes it's hard to know what will be confusing. I prefer to assume as little possible about what my readers are getting, so I would use "surface-area calculation." But many editors correct this (and I generally defer to them).

Unfortunately, there is a compelling to reason to be sparing with hyphens, which is that they can't be nested. This also comes up frequently in science writing, when a compound modifier is constructed from another compound modifier, as in "surface area calculation results." If we used parentheses to tie together related words, this would be rendered "((surface area) calculation) results." But there's no way to indicate priority with hyphens.

The right thing to do is to decide what level of compounding needs to be made explicit, and retain hyphens at all levels up to that, for example "surface-area-calculation results." Sadly, I frequently see something like "surface area-calculation results." That's just wrong, since the hyphen ties together "area" and "calculation" more strongly than "surface" and "area." In this case you'd be better off leaving out all hyphens and hoping for the best.

Of course, as in most cases of confusing writing, the best alternative is "none of the above": recast the sentence so that it doesn't have compound-compound modifiers. "The results of the surface-area calculation" leaves nothing to chance. But it's clunkier.

Innovation

About 540 million years ago, virtually all the basic types of animals appeared in a geographic eyeblink known as the Cambrian explosion.

The late Stephen Jay Gould used this amazing event (if a period of millions of years can be called an event) in his 1989 bestseller Wonderful Life: The Burgess Shale and the Nature of History to debunk two popular myths about evolution. First, evolution is not a steady march toward more and more advanced forms (presumably culminating in us). Second, diversity does not steadily increase. The Cambrian was populated by many types of creature no longer around today, some so exotic as to be worthy of a science fiction film. Rather than growing steadily bushier, the tree of life was later brutally pruned, and we grew from the remaining twigs.

In The Plausibility of Life, Marc Kirschner and John Gerhart highlight another critical facet of this amazing period: since that period of innovation, no more than one new animal type has appeared. The diversity we enjoy today is built from basic parts that were "invented" in the Cambrian explosion.

Rather than get into how and why this happened, for now let's just regard it as an observational fact from the fossil record:

True innovation is rare.

We know this is true in human affairs. Producers of movies and TV shows, for example, often play the odds by re-using and recombining proven concepts. The same goes for technology, where many innovations combine familiar elements in new ways. It's faster, it's cheaper, and it's safer.

For whatever reason, the evolutionary history of life is a series of one-time innovations. After they are adopted, these "core processes" change very little, even though they have eons of time to do so. That doesn't mean that the organisms themselves stay the same--far from it. But they use the core processes in different ways, just as a bat wing is built in the same way as the human hand.

Kirschner and Gerhart discuss several examples of conserved core process, starting with the fundamental chemistry of DNA, RNA, proteins and the genetic code that connects them. Every living thing on earth uses the same chemistry. The appearance of the eukaryotic cell is defined by the presence of the nucleus, but a host of other innovations occurred at the same time. All eukaryotes from amoebae to people share these features even now. The joining of cells into multicellular organisms was also accompanied by innovations that helped the cells stick together and cooperate. Every plant and animal has retained these features largely intact.

Like the animal body plans of the Cambrian explosion, these burst of innovation occurred over relatively short periods, and were permanently added to the toolkit. All of the animals we know, from cockroaches to cockatoos, from squirrels to squids, arose by applying those tools in new ways.

But what is it about the core processes that makes them so resistant to change? What makes them so useful? And are the answers to these questions related?

Monday Morning Quarterbacks

Many news stories simply report the facts; the better ones put the facts in the context and explore their likely impact.

Then there are stories that explain "why."

I have a simple assessment for these explanation stories that saves me a lot of time. This is it:

"What's new?"

More specifically, is there anything about the "explanation" that wasn't known before the event actually happened? If not, click on.

A classic example is election-night analysis. Even before all the votes are counted, pundits materialize to explain what message the voters were trying to send, and which campaign screwed up.

Unfortunately, most of what they say was just as true the day before. Sure, they now have more precise results, and maybe a geographic breakdown and some exit polls. But most of the commentators' facts were already known to everyone. Somehow the surrounding story seems more profound once it aligns with actual events--and once the arguments for the other side have been conveniently forgotten.

Stories about stocks can also be amusing, or pathetic. It's hard to find any report on a market move that doesn't attribute it to concern about Chinese exports, or some such. When the market is truly uncooperative, analysts resort to saying that it "shrugged off" some really dire economic news. Sorry, guys. If you knew how the market would react, you'd be rich.

Unfortunately, the challenge of explanation applies to the real economy as well. I'm a regular reader of Paul Krugman, who warned a year ago that the stimulus package would likely not generate enough jobs. Sure enough, we now have an unemployment rate that once would have been thought unacceptable. So was Krugman right? Or were the conservatives who said the stimulus just wouldn't work?

The sad fact is we hardly know any better now than we did a year ago. We know what happened, of course. But we still don't know what would have happened if we had done something different. Evaluating such past hypothetical situations requires the same kind of modeling, and relies on the same ideological assumptions, as predictions do. Unsurprisingly, the experts mostly see the past the same way they saw the future.

The same problem may apply to climate change. I don't usually think of the existence of global warming as a political question (as opposed the policy response). In a hundred years, after all, liberals and conservatives will both suffer the same heat and drought and see the same sea level rise, or not. But even then, they probably won't agree on what we did, or didn't do, to get them there. We don't have a duplicate world to do control experiments on.

As Yogi Berra is quoted, "Predictions are hard, especially about the future." Sadly, they are almost as hard about the past.

The Plausibility of Life

When creationists, or, as they would have it, advocates of "intelligent design," talk about the "weaknesses" of evolutionary theory, knowledgeable people generally roll their eyes and ignore them. This is appropriate, as these advocates only raise the questions in a disingenuous attempt to promote a religious agenda, under the pretense of open-mindedness and "teaching the controversy." In truth, there is no controversy in the scientific community about the dominant role of natural selection (evolution, the theory) in shaping the observed billions of years of change (evolution, the fact) of life on this planet.

But this response obscures the fact that very interesting issues in evolution remain poorly understood.

I'm not referring to the direct exchange of genetic material between single-cell organisms, although that does call into question the tree-like structure of relationships between these simple species. But at the level of complex, multi-cellular creatures like ourselves, this "horizontal gene transfer" is unimportant compared the "vertical" transfer from parents to offspring. The tree metaphor is still intact.

But even for complex creatures-- especially for complex creatures--there are important open questions about how evolution works in detail. The insightful (and cheap!) 2006 book, The Plausibility of Life, by Marc Kirschner and John Gerhart, began to frame some answers to these questions.

The fundamental ingredients of evolution by natural selection were laid out by Darwin: heritable natural variations lead some individuals to be more likely to survive and thus to pass on these variations.

We now know in great detail how cells use some genes in the DNA as a blueprint for proteins, and how these proteins and other parts of the DNA in turn regulate when various genes are active. And we know, as Darwin could only imagine, how that DNA is copied and mixed between generations, only occasionally developing mutations at single positions or in larger chunks. We understand heritable variation.

We also understand the arithmetic of natural selection, which confirms Darwin's intuition: a mutation that improves the chances that its host will survive to be reproduced will spread through a population, while a deleterious mutation will die out (although evolution is indifferent to most mutations). This all takes many generations, but the history of life on earth is long.

But there is something missing, what Kirschner and Gerhart call the third leg of the stool: how does the variability at the DNA level translate into variability at the level of the organism? Selection must occur at this higher level, the level of phenotype, but can only be passed on at the level of the genotype. How do we close this loop?

It would be easy if a creature's fitness were some average of the fitness of each of the three billion bases in the DNA, but it's not that simple. For example, if two proteins work together as a critical team, a mutation in one can kill the organism, even if they could be an even better team if they both mutated in a coordinated way.

This sounds disturbingly reminiscent of the neo-creationist argument that life is so "irreducibly complex" that there must have been a creator--er, designer. But Kirschner and Gerhart don't believe that for a second. What they argue instead is that organisms are constructed so that genetic change can dramatically alter phenotype without sacrificing key functions--in a process they call facilitated variation.

In future posts I will discuss clues that this construction--I'm avoiding the word "design"-- is present in organisms today, and some of the principles it follows.

A man who knew how to inspire

This speech was given the day before MLK was assassinated.

Sys Devo

Lawrence Berkeley Labs

My latest eBriefing for the New York Academy of Sciences, Growth Networks: Systems Biology Meets Developmental Biology, is now up (the direct link should work only for Academy members; others may get to it through the NYAS page of my website.)

The symposium was very interesting, but, as often happens, it was challenging to present the three talks as a coherent unit. In this case, the overall message (provided by the visionary organizer, Andrea Califano) is that the sweeping and irreversible changes that occur during early development, which are often driven by a relatively few molecular events (perhaps dozens), can provide stringent and useful tests for understanding molecular regulation. This is quite a different way to learn about networks than by gently poking ("perturbing," for example with stress or drugs or RNA interference) a mature animal, in which various molecules are generally cooperating to keep things stable.

The hope is that the overlap between development and systems biology, can have the sort of powerful synergy that have enriched evolutionary and developmental biology in Evo Devo, as popularized by Sean B. Carroll and others. But I suspect the final synthesis will be more of a three way combination, SysEvoDevo.

Angela DePace of Harvard, for example, described her nascent efforts to exploit evolutionary comparisons between related species from the fly genus Drosophila, which have been a playground for development (once called embryology) for nearly a century. In the past couple of decades researchers have learned how to modify particular genes so they produce fluorescent molecules of various colors along with their normal protein products. The results have shown in living color how various transcription factors interact to generate that spatial patterns and compartments that ultimately shape the segmented body of the fly. DePace and her former colleagues at Lawrence Berkeley Labs refined the technique to let them measure the quantitative changes in gene expression at thousands of individual cells in the early embryo (see the figure), which let them test the models of gene activity (and the differences between species) in fascinating detail.

Stanislav Shvartsman of Princeton also looked at early Drosophila development, but he showed that the transcription factors alone don't explain everything. Instead, some of the patterning depends on protein phosphorylation, which is a half-century old process that among other things carries signals from a cell's outer membrane to its nucleus, but is rarely considered in development. Antonio Iavarone of Columbia studies the development of the early nervous system in mice from stem cells to differentiated neurons. This is a process that is subverted by brain cancers, which re-activate this cellular program to grow and nourish themselves.

Pulling these three diverse talks together was a bit of a shoe-horning exercise, but they were all fascinating.

Outliers

One of the most subtle and perilous questions in science is when to omit data.

Sometimes there are really good reasons to leave something out. After all, there are lots of ways to screw up data.

In the old days, people could read instruments wrong, or write or copy measurements incorrectly. Even with data acquired and processed by computers, instruments can overload or malfunction and produce incorrect readings. More frequently, even if a measurement itself is correct, changes in the apparatus or the external context can destroy its apparent significance. And it's almost always possible to save data in the wrong place or with the wrong description.

And it matters. Wrong data can cause a lot of headaches. Many analyses reflect a statistical representation of the complete data set, such as the average value, for example, and curve fitting typically penalizes large deviations even more than small ones. So even a single errant measurement can distract from many good ones.

All this means that scientists have good and powerful reasons to eliminate "outliers" that fall outside the normal range of variation, since there's a good chance they are wrong and could skew the results away from the "real" answer.

The problem is that eliminating points requires a subjective judgment by a human experimenter. Often this person is testing a hypothesis, and so has a working expectation of what the data "should" look like. The experimenter will be strongly motivated to toss out points that "don't look right"--even if that just means they are unexpected. That temptation must be avoided.

Distinguishing truly nonsensical measurements from those that simply don't accord with a researcher's expectations requires a level of objectivity and humility that is rare in most people, and difficult even for well-trained scientists.

But it is one of a scientist's most important tasks.

I once heard someone say that it's OK to throw out one data point out of seven. I think that's ridiculously general, and also dangerous. Human nature being what it is, I think the standards need to be higher.

What I learned in my undergraduate laboratory class is that you should check the data as you go along (plotting it by hand in your ever-present lab notebook, if you must know how old I am), to be ready for any measurement problems that arise. If a measurement looks funny, repeat it. If the repeat is what you originally expected, it may be OK toss out the funny one. The repeat might be an individual point, or an entire series. Even better is to do the new measurement twice, and use the majority rule.

Unfortunately, it's not always possible to repeat the measurement exactly. Another alternative is to make a similar measurement, for example with a similar sample. Whenever possible, replication should be part of normal quality control anyway, so this may not be too hard.

But what about when no repeat is possible at all, as happens in historical sciences? You could just throw out all the measurements as unreliable and find a new line of work. But if you opt to toss some of it and not the rest, you really need a very good argument about why that data has a problem. This is a really slippery slope, if there is no way to double-check your argument. People--including scientists--are notoriously good at coming up with post-hoc "just-so" stories for why things are the way they are.

If you really think the data is wrong, but you can't be sure everyone would agree with your logic, scientific tradition still gives you an option: say what you did. Whenever a data is chosen or processed according to a questionable procedure, proper conduct requires that you declare the procedure, certainly in any journal article.

Unfortunately, I have the feeling that, in the era where hot results are sent to general-interest journals like Scienceandnature, this sort of documentation is relegated to the supplementary material or never stated at all. This is a dangerous trend.

Incidentally, many definitions of scientific misconduct include errors of omission. For example, here is the relevant definition from the National Science Foundation's policy:

Falsification means manipulating research materials, equipment, or processes, or changing or omitting data or results such that the research is not accurately represented in the research record.

In other words, if your deliberate omission distorts the conclusion, you are guilty of fraud. Don't do it.

    

Anniversary of Hopping Paper

Thursday, January 14, 2010 is the 25^th anniversary of one of my first scientific papers, Hopping in Exponential Band Tails, in Physical Review Letters. It came out just as I arrived at Bell Labs.

It still surprises me that this paper has gotten nearly 200 citations, and that they continue to dribble in even now. Most papers are surpassed by new developments within a few years of publication. In this case, I stumbled on a useful but very accessible concept that people can easily wrap their head around. But I'd guess that most people that cite it have never read it.

The paper concerns motion of electrons in amorphous semiconductors, that is, semiconductors without a crystalline lattice. The best known example is the amorphous silicon alloys that are used for cheap solar cells.

Until the 1960s, some physicists questioned whether amorphous semiconductors could even exist (although they clearly did), because the quantum-mechanical understanding of semiconductors depended on the mathematical properties of wavelike electrons moving among the regularly-spaced atoms in a crystal. For electrons in some range of energies, the electron waves that diffract from the atoms destructively interfere, creating a bandgap with no electron states. At other energies, where there are electron states, they extend through the entire crystal. None of this mathematical framework for understanding semiconductors seemed to work unless the atoms were arranged in a regular crystal.

Phil Anderson, then at Bell Labs, showed in 1958 that if atoms were arranged in an irregular pattern, electronic states could exist, but be localized near particular atoms. Neville Mott and others suggested that in an amorphous semiconductors, electrons would be localized over some range of energies but extend infinite distances at higher or lower energies. The energies that separated the localized and extended states, which have the character of a phase transition, were called "mobility edges." If one conceptually replaces the band gap of crystalline semiconductors with the gap between mobility edges, then the mathematical treatment of amorphous semiconductors looks very familiar. Anderson and Mott shared the 1977 Physics Nobel with John van Vleck for their discoveries. Instead of being denigrated as "dirt physics," disorder is now a perennial topic in condensed matter physics

In Mark Kastner's group at MIT, we were studying what happened to optically generated electrons in the "band tails": the localized states near the mobility edge, whose number decreases exponentially into the gap. Based on some experiments I had done, I proposed that, at low temperatures, electrons would at first simply "hop" from one localized state to another, avoiding the extended states at the mobility edge altogether. Later on, as they moved to energies where the states where farther and farther apart, they would find it faster to hop up to where there were more states--but not all the way back to the mobility edge.

I called the energy to which electrons hopped--and where they could move easily--the "transport energy," and used a simple model to calculate how this energy varies with temperature.

If once conceptually replaces the band gap of crystalline semiconductors with the gap between transport energies, then the mathematical treatment of amorphous semiconductors looks very familiar. There are some important differences, though. For example, a magnetic field has a different effect on hopping electrons than on those that are freely moving. But although some details are different, the overall picture of amorphous semiconductors looks much like the pictures used by electrical engineers.

At the time, I was concerned that people would only remember Marc Kastner's name, so he graciously agreed to let me be sole author. I later regretted that selfishness, because anyone who knew Mark could see his style in it, and he certainly helped me to frame the ideas. Such are the follies of youth.

"World's First Molecular Transistor"

Overall electrode geometry, probably not for a device that was actually measured. Inset: Molecule-coated gold nanowire between the electrodes has developed a nanometer-scale gap because of electromigration, in which current pushes atoms away. White rectangle is 100nm long, for scale. Inset of inset: complete fantasy of what might happen in a small fraction of cases.

An interesting article in Nature, Observation of molecular orbital gating, got somewhat lost over the holidays, in spite of the breathless Yale University press release, Scientists create world's first molecular transistor.

Mark Reed of Yale and his colleagues made hundreds of small gold wires between large electrodes on a nominally 3nm-thick alumina insulator on a substrate and coated it with organic molecules. They then applied an electric current that pushed enough atoms out of the way to make a small gap in the wire. Near absolute zero, in a few of the wires, they then measured a current variation with source-drain voltage that looked like what they expected if the current was passing through a molecule close to the surface, whose energy they could change by applying a voltage to the substrate, or gate. In addition, to test whether the current was really going through the extra organic molecules, the researchers found sharp features in the current trace when the source-drain voltage brought the energy levels into alignment, in agreement with the molecules' "signature."

So far, so good. As the authors note, there have been previous observations of gated conduction in molecules before, but it's not easy to get two electrodes connected to a tiny molecule, let alone three.

But the press release says this shows that "a benzene molecule attached to gold contacts could behave just like a silicon transistor."

How does this fall short of that description? Let me count the ways.

No saturation. The current doesn't look at all like a normal silicon field-effect transistor (FET), where the gate voltage changes the channel resistance. Instead, the authors describe the conduction as tunneling between the two remaining pieces of the wire, while the gate voltage changes the precise energy levels in the molecule. The current is low near zero source-drain voltage and rises dramatically as the voltage increases. In contrast, the current in an ordinary FET rises linearly with voltage, like a resistor, and then saturates. In ordinary circuits, this saturation, corresponding to a voltage-independent current, is central to the transistor's gain, which gives it the ability to amplify power or to drive other transistors.

Low current. Tunneling conduction gives inherently low current levels. The currents observed are in the nanoamp range, a million or so times smaller than those in transistors on integrated circuits. This small current would take correspondingly longer to charge up any capacitor, so the circuits would be slow.

Large parasitic capacitance. Because the source and drain electrodes lie right on top of the gate, the actual capacitance is even bigger. Modern transistors are built with self-aligned processes that minimize the overlap capacitance.

Low gate coupling. The authors estimate that they need to put 4V on the gate to change the electron energies by 1V (25%). This is actually surprisingly good for a device like this, where multiplies of 0.1% are not unheard of. But it's still a problem. Silicon technologists work very hard to get perhaps 80-90% of the gate voltage to show up on the channel, and if it doesn't the device is very hard to turn off, resulting in excessive power. Moreover, if the energy isn't being controlled by the gate, it should be controlled by the drain, which means that it will never be possible to saturate the current to isolate the input from the output.

Packing Density. The entire device is much bigger than the molecule. From the micrograph, the electrodes are many microns in size. No doubt the electrodes could be made more compact, but to compete with integrated circuits they would have to be packed to separations comparable to their size, and this technique doesn't look like it could ever do that. No one really cares whether transistors are small. They care if they can be packed densely (and are cheap and fast and use little power).

Low Yield. The authors measured 35 devices that did what they hoped, out of 418 attempts, so about 8% of them worked. In contrast, in an integrated circuit only about 0.000001% of the transistors fail. (Or something like that--I don't have access to real numbers these days, but you get the idea.) Building a large circuit from occasionally-functional devices would require a completely new type of circuit design, and probably wouldn't be worth it.

This low yield is not surprising (or easily avoided), since the fabrication seems to demand that most of the current run through a molecule that is positioned right just at the gap and right at the corner where the wire meets the substrate, and that it not get blown away during the electromigration. Still, it is a matter of concern that devices are defined to be working if they do what the experimenters think they ought to do. This is a problem with many molecular fabrications schemes, and I give the team credit for doing the "inelastic tunneling spectroscopy" to verify the molecules have something to do with the current. But I would feel better if they gave an indication of how representative the devices they showed are.

The authors did a couple of other tests that I'm guessing didn't work as dramatically as they had hoped. First, they saw rather small differences between "insulating" molecules--fully saturated alkanes sandwiched between sulfur groups-- and "metallic" molecules--in which the organic "meat" of the sandwich is an aromatic benzene ring. Second, the voltage "fingerprints" of the molecules didn't shift when they applied the gate voltage, as one would naively have expected. The shapes and sizes of the peaks changed, but not their positions.

Overall, this is a nice research result, with some strong observations and some puzzling features. Some of my quibbles could be addressed in time, but it's not clear that these molecules will ever behave "just like a silicon transistor."

In 1997, Mark Reed was quoted to the effect that silicon technologists were "shaking in their boots" over his team's results. Those of us working in silicon technology at the time got a good laugh out of that claim, and went back to work. The new press release says that "Reed stressed that this is strictly a scientific breakthrough and that practical applications such as smaller and faster 'molecular computers'—if possible at all—are many decades away. 'We're not about to create the next generation of integrated circuits,' he said."

He's got that right.

Delayed Re-entry

The new year finds me working through the summary for the RECOMBsat/DREAM conference last month in Cambridge, MA (more than 10,000 words on over 20 separate subjects), so daily blogging will have to wait until the week of January 11.

Check back then--upcoming posts will include molecular transistors, the 25^th anniversary of one of my first scientific papers, issues raised by the "climategate" emails, a series on evolutionary biology inspired by the book, The Plausibility of Life, new stories I've written, and more.

In the meantime, check out this video (Click the "Technology" tab) explaining how modern "deep sequencing" technology (here from Illumina) can simultaneously determine the base sequences for something like a million short snippets of DNA simultaneously. In contrast to the "traditional" (decade-old) microarray method of matching to preselected targets, this method allows new sequences to be quickly found, for example in the human gut or other natural environments. In addition, by matching these sequences to previously mapped genomes, this technology has revolutionized the identification of short regulatory RNAs, alternative splicing of proteins, DNA changes in cancer, DNA binding sites for transcription factors, fractal DNA structure, and many other areas of biology.

And it's just beginning.