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A recent news article by the University of Texas at Dallas (UTD) highlighted recent joint work by the Department of Materials Science and Engineering and Accelrys on critical surface reactions of Silicon. The research points the way to ”improve semiconductor devices’ performance in health care and solar power applications in particular.”
Who cares? Anybody who uses chips, solar cells, or any other device containing semiconductors (in other words, all of us.)

Insertion of Nitrogen atom is predicted to occur preferentially at the step edge of Si(111)
How does the latest research help? A typical semiconductor device consists of a metal oxide semiconductor layer (e.g., HfO2) deposited on a silicon substrate. As explained by co-author Dr. Mat Halls, formation of an SiO2interlayer between the silicon substrate and metal oxide can decrease semiconductor performance. One approach to solving this is to introduce a nitride barrier to prevent the growth of interfacial SiO2. The ability to introduce such heteroatoms into the topmost layers of Si affords additional opportunities to tune the surface properties by enhancing chemical reactivity at these sites to form functional surfaces. But how do you get the nitrogen to stick to the surface?
In the latest research, published in Nature Materials, used infra-red spectroscopy to explore the possible formation mechanisms of nitride on silicon surfaces terminated by hydrogen. Calculations using density functional theory (DFT) demonstrated how stepped edges are important to formation of the nitride layers. The reaction mechanism on the stepped surface provides a means of controlling the reaction. As the authors wrote: “The ability to control the reaction … enables the realization of applications … including sensing, electrical and thermal transport, and molecular computing.” This is a beautiful demonstration of the complementarity of theory and experiment. One can deal with facts, but requires interpretation. The other provides detailed explanations at the atomic level, but sometime requires an anchor to the “real world.” Together they can do more. Wouldn’t it be great if all viewpoints could be reconciled this well?
It seems ages since I last did one of these. There are plenty of new and newish blogs doing the rounds at the moment readers can get their teeth into. Without further ado ...
The article is worth reading especially for all the engineering students.Here is the link: [toponlineengineeringdegree.com]Aubrey de Grey says we can avoid aging
Inventions that slow or entirely halt the aging process have the potential to also cure humans of the many medical problems that settle in as one grows older.
My comment: Hi, your blog has always been interesting and cute. I’m a chemical engineering student.Could you please give me some advices for manipulating my engineering studies efficiently.Secondly, I’m interested in Alchemy.Could you please guide me regarding the same.
Gaussling said...
Hi Vinith, can’t help much with alchemy given that it faded out centuries ago. As a chemist, I’m reluctant to advise in regard to chemical engineering. But in regard to the chemical processing industry (CPI), you should decide where you would like the arc of your career to take you.
If you are business oriented, then you need to be sharp in the area of process economics and finance. If you want to be on the technical end, then you have to try to guess what part of the CPI you’ll end up in. Pharmaceuticals requires different skills than petroleum refining. Biotechnical engineering favors those with some knowledge of cell biology and microbiology. If you show good mechanical aptitude, you might end up being a plant engineer maintaining and installing equipment. You should have a good look in the mirror and decide on what really excites you about engineering – do you like to be out on the plant floor supervising or indoors planning? Perhaps you want to be a consulting engineer who flys in to solve specific problems. Before anyone can help you plan your studies, you need to look inward and decide what drew you to engineering and what do you love to do.
My comment:I'm a chemical engineering student and interested in rheology. Could you please give me any suggestions.
I'm not able to decide which area of chemical engineering more interests me. Our choices design our fortune and I hope, I would make a good choice. On the whole, many areas like I'd mentioned in my profile-rheology, nanotechnology, microbiology etc. have influenced me.And yesterday, I read some chemical engineering matter on the net. Then I decided to become a freelancer.However, I'm in a dilemma whether to procure a job and go for working in an industrial sector directly after b.tech, or opt for higher studies and go for R & D in my interested area(s). Have some time to decide. Before that I should just complete doing my engineering.Lastly, I would like to thank all the chemical engineering and other professionals who were supportive and did care my presence(blog) on the net and responded to my queries.They have included my blog on their sites. I want to list them up: Chemical Professionals, Chemical Engineering, It's a micro world, The Chem Blog, Coronene Chemistry Blog, Smart Process Design .I'm also a big fan of Zaki, Profmaster and Dilantha.John said... I'd say you are off to a good start being in chemical engineering. (That's my background as well, only I ended up here without any planning.) Despite its importance across so many fields, you can't get a degree in rheology. It's the same way with thermodynamics - so useful across so many fields, but you can't get a degree in it. There aren't job postings for "Rheologists" but for people (engineers, mostly) with experience in rheology. And it's mostly engineers because fluid mechanics is an engineering subject that few "scientists" ever study. Studying polymers is a good entry point, as is food processing. Lastly, take as much math as you can handle. The mathematics behind much of rheology is the same mathematics used in Relativity Theory - tensor analysis. Taking the derivative of a vector is trivial; taking the derivative of a tensor is a nightmare.
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In my previous post I had mentioned that key/value or non-relational data stores could be useful in certain cheminformatics applications. I had started playing around with MongoDB and following Rich’s example, I thought I’d put it through its paces using data from PubChem.
Installing MongoDB was pretty trivial. I downloaded the 64 bit version for OS X, unpacked it and then simply started the server process:
| 1 | $MONGO_HOME/bin/mongod --dbpath=$HOME/src/mdb/db |
where $HOME/src/mdb/db is the directory in which the database will store the actual data. The simplicity is certainly nice. Next, I needed the Python bindings. With easy_install, this was quite painless. At this point I had everything in hand to start playing with MongoDB.
Getting dataThe first step was to get some data from PubChem. This is pretty easy using via their FTP site. I was a bit lazy, so I just made calls to wget, rather than use ftplib. The code below will retrieve the first 80 PubChem SD files and uncompress them into the current directory.
| 1 2 3 4 5 6 7 8 9 10 11 12 13 | import glob, sys, os, time, random, urllib def getfiles(): n = 0 nmax = 80 for o in urllib.urlopen('ftp://ftp.ncbi.nlm.nih.gov/pubchem/Compound/CURRENT-Full/SDF/').read() o = o.strip().split()[5] os.system('wget %s/%s' % ('ftp://ftp.ncbi.nlm.nih.gov/pubchem/Compound/CURRENT-Full/SDF/', o)) os.system('gzip -d %s' % (o)) n += 1 sys.stdout.write('Got n = %d, %sr' % (n,o)) sys.stdout.flush() if n == nmax: return |
This gives us a total of 1,641,250 molecules.
Loading dataWith the MongoDB instance running, we’re ready to connect and insert records into it. For this test, I simply loop over each molecule in each SD file and create a record consisting of the PubChem CID and all the SD tags for that molecule. In this context a record is simply a Python dict, with the SD tags being the keys and the tag values being the values. Since i know the PubChem CID is unique in this collection I set the special document key “_id” (essentially, the primary key) to the CID. The code to perform this uses the Python bindings to OpenBabel:
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | from openbabel import * import glob, sys, os from pymongo import Connection from pymongo import DESCENDING def loadDB(recreate = True): conn = Connection() db = conn.chem if 'mol2d' in db.collection_names(): if recreate: print 'Deleting mol2d collection' db.drop_collection('mol2d') else: print 'mol2d exists. Will not reload data' return coll = db.mol2d obconversion = OBConversion() obconversion.SetInFormat("sdf") obmol = OBMol() n = 0 files = glob.glob("*.sdf") for f in files: notatend = obconversion.ReadFile(obmol,f) while notatend: doc = {} sdd = [toPairData(x) for x in obmol.GetData() if x.GetDataType()==PairData] for entry in sdd: key = entry.GetAttribute() value = entry.GetValue() doc[key] = value doc['_id'] = obmol.GetTitle() coll.insert(doc) obmol = OBMol() notatend = obconversion.Read(obmol) n += 1 if n % 100 == 0: sys.stdout.write('Processed %dr' % (n)) sys.stdout.flush() print 'Processed %d molecules' % (n) coll.create_index([ ('PUBCHEM_HEAVY_ATOM_COUNT', DESCENDING) ]) coll.create_index([ ('PUBCHEM_MOLECULAR_WEIGHT', DESCENDING) ]) |
Note that this example loads each molecule on its own and takes a total of 2015.020 sec. It has been noted that bulk loading (i.e., insert a list of documents, rather than individual documents) can be more efficient. I tried this, loading 1000 molecules at a time. But this time round the load time was 2224.691 sec - certainly not an improvement!
Note that the “_id” key is a “primary key’ and thus queries on this field are extremely fast. MongoDB also supports indexes and the code above implements an index on the PUBCHEM_HEAVY_ATOM_COUNT field.
QueriesThe simplest query is to pull up records based on CID. I selected 8000 CIDs randomly and evaluated how long it’d take to pull up the records from the database:
| 1 2 3 4 5 6 7 8 | from pymongo import Connection def timeQueryByCID(cids): conn = Connection() db = conn.chem coll = db.mol2d for cid in cids: result = coll.find( {'_id' : cid} ).explain() |
The above code takes 2351.95 ms, averaged over 5 runs. This comes out to about 0.3 ms per query. Not bad!
Next, lets look at queries that use the heavy atom count field that we had indexed. For this test I selected 30 heavy atom count values randomly and for each value performed the query. I retrieved the query time as well as the number of hits via explain().
| 1 2 3 4 5 6 7 8 9 10 11 12 13 | from pymongo import Connection def timeQueryByHeavyAtom(natom): conn = Connection() db = conn.chem coll = db.mol2d o = open('time-natom.txt', 'w') for i in natom: c = coll.find( {'PUBCHEM_HEAVY_ATOM_COUNT' : i} ).explain() nresult = c['n'] elapse = c['millis'] o.write('%dt%dt%fn' % (i, nresult, elapse)) o.close() |
A summary of these queries is shown in the graphs below.
One of the queries is anomalous - there are 93K molecules with 24 heavy atoms, but the query is performed in 139 ms. This might be due to priming while I was testing code.
Some thoughtsOne thing that was apparent from the little I’ve played with MongoDB is that it’s extremely easy to use. I’m sure that larger installs (say on a cluster) could be more complex, but for single user apps, setup is really trivial. Furthermore, basic operations like insertion and querying are extremely easy. The idea of being able to dump any type of data (as a document) without worrying whether it will fit into a pre-defined schema is a lot of fun.
However, it’s advantages also seem to be its limitations (though this is not specific to MongoDB). This was also noted in a comment on my previous post. It seems that MongoDB is very efficient for simplistic queries. One of the things that I haven’t properly worked out is whether this type of system makes sense for a molecule-centric database. The primary reason is that molecules can be referred by a variety of identifiers. For example, when searching PubChem, a query by CID is just one of the ways one might pull up data. As a result, any database holding this type of data will likely require multiple indices. So, why not stay with an RDBMS? Furthermore, in my previous post, I had mentioned that a cool feature would be able to dump molecules from arbitrary sources into the DB, without worrying about fields. While very handy when loading data, it does present some complexities at query time. How does one perform a query over all molecules? This can be addressed in multiple ways (registration etc.) but is essentially what must be done in an RDBMS scenario.
Another things that became apparent is the fact that MongoDB and its ilk don’t support JOINs. While the current example doesn’t really highlight this, it is trivial to consider adding say bioassay data and then querying both tables using a JOIN. In contrast, the NoSQL approach is to perform multiple queries and then do the join in your own code. This seems inelegant and a bit painful (at least for the types of applications that I work with).
Finally, one of my interests was to make use of the map/reduce functionality in MongoDB. However, it appears that such queries must be implemented in Javascript. As a result, performing cheminformatics operations (using some other language or external libraries) within map or reduce functions is not currently possible.
But of course, NoSQL DB’s were not designed to replace RDBMS. Both technologies have their place, and I don’t believe that one is better than the other. Just that one might be better suited to a given application than the other.



Click on the picture for an interactive version
Elliot Morley, David Chaytor, Jim Devine. As Labour MPs go they're cards aren't they? It's not enough that they've allegedly swindled the system by submitting false expense claims *and* will be in receipt of at least £30k "relocation money" when they step down, but now the right honourable members have the cheek to pursue parliamentary privilege defence to avoid court action. In a society where the rich and powerful are litigious, this is an important democratic gain worth defending. So to see it dragged through the muck to defend the sullied reputations of men caught with their hands in the taxpayer's pocket is a sickening sight. You cannot but agree with Ruth Cox of the Hansard Society, who saysComing on top of the wider expenses crisis, if this defence is allowed and the trio of troughers emerge unscathed this will compound the widespread antipathy against 'official' politics. Not good news for the mainstream parties or the minor ones to their left, but it provides fertile environment for the far right."If it is a defence against almost any action that an MP takes in parliament, in any relationship with their work, then I think that is going to be deeply damaging for the public. They will see that it is putting MPs above the public, giving them enhanced powers, making them essentially above the laws that they themselves make." (source)
For what it's worth, the two visitors from Cuba spend most time on my site :) More seriously, the information on the site does not include any error bars, such as the standard deviation:
Most of use know that 4:24 for two visitors is not necessarily significantly different from 2:43 for one hundred visitors (actual numbers). Standard deviation information would have helped significantly here (pun intended :).
Much, much better than the The Sun's hand-wringing hypocrisy.Pyongyang, February 5 (KCNA) -- The national football team of the Democratic People's Republic of Korea, qualified for the 2010 FIFA World Cup, embraces a lot of competent players.
Typical of them is Hong Yong Jo, famous for many scores he has made in international and local tournaments.
He, who has been interested in sports, especially football, from primary school days, is characterized by running fast, dribbling well, taking scoring positions in time and skillfully getting goals.
He has been active as a forward in many international tournaments.
He fully demonstrated his skill by scoring three goals in the home-and-home matches with Jordan at the third-phase preliminaries of the Asian region for the 2010 FIFA World Cup.
Hong, captain of the national team, was crowned with the title of People's Athlete last year.
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We have recently been awarded an Innovative Medicines Initiative grant in the area of predictive toxicology - the project is called eTox. This is a very exciting project and will build an unprecedented collaborative database of rat toxicology data for a large number of clinical development candidates. It is also a chance to work in close collaboration with a network of some of the leading European academics, SMEs and pharmaceutical companies.We have two posts available under this funding. The first is for a scientist with experience of datamining and analysis of data, preferably with good experience of bioinformatics or chemoinformatics. The second post is for an informatician to build a data repository, and then populate this with deposited curated toxicology data.
If you have any questions about the jobs, please feel free to mail us.
The deadline for applications is 14th March 2010.

Also approved in January is Liraglutide, on January 25th, under the trade name Victoza. Liraglutide, previously known as NN2211, is a glucagon-like peptide-1 (GLP-1) receptor agonist indicated as an adjunct to diet and exercise to improve glycemic control in adults with type 2 diabetes mellitus. People with this type of diabetes have difficulty making and using insulin.
Liraglutide works by activating the GLP-1 receptor (GLP-1R), which in turn stimulates the adenylyl cyclase pathway leading to insulin release in the presence of elevated glucose concentrations. GLP-1R is a class-II GPCR (also known as secretin receptor family). Liraglutide is the second GLP-1 agonist approved for the treatment of type 2 diabetes, after Exenatide (marketed as Byetta), which reached the market in 2005.
Liraglutide is an engineered form of the natural human GLP-1 peptide (one amino acid difference to the major circulating form of GLP-1, GLP-1(7-37), an arginine replaces a lysine at position 34), an additional modification is the addition of palmitic acid attached via a glutamic acid spacer at position 26. The molecular weight of Liraglutide is 3751.2 Da. Each standard dose contains 1.8mg of Liraglutide (equivalent to 480 nmol). Dosing is as a once daily subcutaneous (s.c.) injection.
Liraglutide has a plasma half-life of ~13 hr (far longer than the 2 min half life of the natural GLP-1 peptide). Slowing the fast degradation of GLP-1 is the basis of the therapeutic mechanism of the gliptin class of DPP-IV inhibitors). The absolute bioavailability of Liraglutide, after s.c. dosing is 55%, and has high plasma protein binding > 98%. The volume of distribution Vd is 0.07L/kg, with a clearance of 1.2 L/h.
Liraglutide has a boxed warning (risk of thyroid C-cell tumours).
Victoza is marketed by Novo Nordisk and the product website is http://www.victoza.com.


Prentice Hall College Div 1997 | ISBN: 0137463146 | 514 pages | Djvu | 3,4 MB
During the (D2 - π -D2)n period the intensity of lines with short T2 (broad lines) diminishes much more quickly than that for lines with long T2 (sharp lines). The CPMG sequence is therefore useful for enhancing the sharp features in a spectrum by suppressing the broad features. This is demonstrated in the figure below. The top panel of the figure shows a portion of a conventional 500 MHz 1H NMR spectrum of a polymer sample contaminated with small amounts of smaller molecules. The broad lines (truncated in the figure) are due to the polymer whereas the much smaller sharp lines are due to the impurities. The bottom panel of the figure shows the CPMG spectrum of the same sample with D2 = 4 msec and n = 32. One can see that the broad polymer lines are greatly suppressed and the smaller sharp lines are much more obvious. 
------->(1)ΔQ = heat input or heat lost, Jh = heat transfer coefficient, W/(m2K)A = heat transfer surface area, m2ΔT = difference in temperature between the solid surface and surrounding fluid area, KΔt = time period, sThe overall heat transfer coefficient U is a measure of the overall ability of a series of conductive and convective barriers to transfer heat applied to heat exchangers.
R = Resistance(s) to heat flow in pipe wall (K/W)But more complex relationships exist, for example when heat transfer takes place by different routes in parallel.And the thermal resistance due to the pipe wall is calculated as follows-
x = the wall thickness (m)k = the thermal conductivity of the material (W/(m·K))A = the total area of the heat exchanger (m2) The following relationship is often used:
= 
Upre = overall heat transfer coefficient based on calculated or measured ("clean heat exchanger") data,
Rf = thermal resistance due to fouling,
In general, the heat transfer coefficients, h inner and h outer are also taken in consideration for heat exchanger tubes. And it's very important for a chemical engineer to calculate the surface area of heat transfer.