18/08: Cute 'lil kitty!
Is a cute 'lil scientific kitty!
Can I take him home?
Can I take him home?
03/06: Lego Printer
I just saw a Lego Printer!
It is very cool. I especially like the horse and palm leaves... It was originally posted here.
It is very cool. I especially like the horse and palm leaves... It was originally posted here.
24/05: My publications...
Having just had my last paper come out, I thought it would be a good idea to list all papers of which I'm an author...
Click through for the abstracts...
- Rowles, M. R. & Madsen, I. C. 2010, 'Whole-Pattern Profile Fitting of Powder Diffraction Data Collected in Parallel-Beam Flat-Plate Asymmetric Reflection Geometry', Journal of Applied Crystallography, vol. 43, no. 3, pp. 632-634.
- Rowles, M. R. & O'Connor, B. H. 2009, 'Chemical and Structural Microanalysis of Aluminosilicate Geopolymers Synthesized by Sodium Silicate Activation of Metakaolinite', Journal of the American Ceramic Society, vol. 92, no. 10, pp. 2354-2361.
- Carter, G. A., Rowles, M. R., Ogden, M. I., Hart, R. D. & Buckley, C. E. 2009, 'Industrial Precipitation of Zirconyl Chloride: The Effect of pH and Solution Concentration on Calcination of Zirconia', Materials Chemistry and Physics, vol. 116, no. 2-3, pp. 607-614.
- Carter, G. A., Hart, R. D., Rowles, M. R., Ogden, M. I. & Buckley, C. E. 2009, 'Industrial Precipitation of Yttrium Chloride and Zirconyl Chloride: Effect of pH on Ceramic Properties for Yttria Partially Stabilised Zirconia', Journal of Alloys and Compounds, vol. 480, no. 2, pp. 639-644.
- Scarlett, N. V. Y., Madsen, I. C., Evans, J. S. O., Coelho, A. A., McGregor, K., Rowles, M. R., Lanyon, M. R. & Urban, A. J. 2009, 'Energy Dispersive Diffraction Studies of Inert Anodes', Journal of Applied Crystallography, vol. 42, pp. 502-512.
- Carter, G. A., Hart, R. D., Rowles, M. R., Buckley, C. E. & Ogden, M. I. 2009, 'The Effect of Processing Parameters on Particle Size in Ammonia-Induced Precipitation of Zirconyl Chloride under Industrially Relevant Conditions', Powder Technology, vol. 191, no. 1-2, pp. 218-226.
- Rowles, M. R., Hanna, J. V., Pike, K. J., Smith, M. E. & O'Connor, B. H. 2007, '29Si, 27Al, 1H and 23Na MAS NMR Study of the Bonding Character in Aluminosilicate Inorganic Polymers', Applied Magnetic Resonance, vol. 32, no. 4, pp. 663-689.
- Rowles, M. R. 2004, The Structural Nature of Aluminosilicate Inorganic Polymers: A Macro to Nanoscale Study, PhD, Curtin University of Technology.
- Rowles, M. & O'Connor, B. 2003, 'Chemical Optimisation of the Compressive Strength of Aluminosilicate Geopolymers Synthesised by Sodium Silicate Activation of Metakaolinite', Journal of Materials Chemistry, vol. 13, no. 5, pp. 1161-1165.
Click through for the abstracts...
It's a bit of a mouthful, but my paper just came out.
Whole-pattern profile fitting of powder diffraction data collected in parallel-beam flat-plate asymmetric reflection geometry
Matthew R. Rowles & Ian C Madsen
Journal of Applied Crystallography, 2010, Volume 43, pages 632-634
Abstract:
A simple, physically based model that allows the whole-pattern profile fitting of diffraction data collected in parallel-beam flat-plate asymmetric reflection geometry is presented. In this arrangement, there is a fixed angle between the incident beam and the sample, resulting in a fixed-length beam footprint. The use of a wide-angle detector for the simultaneous detection of the data precludes the use of any diffracted beam optics. Therefore, the observed peak widths are a function of the length of the beam footprint on the sample. The model uses up to three refinable parameters, depending on the intensity profile of the beam, to calculate the effect of diffraction angle on the width of all diffracted peaks. The use of this model reduces the total number of parameters required to fit the observed peak widths and shapes, hence leading to increased stability in the profile analysis. Implementations of the model are provided for both fundamental parameters and empirical approaches.
.
You can download it here, or go to the journal website.
Whole-pattern profile fitting of powder diffraction data collected in parallel-beam flat-plate asymmetric reflection geometry
Matthew R. Rowles & Ian C Madsen
Journal of Applied Crystallography, 2010, Volume 43, pages 632-634
Abstract:
A simple, physically based model that allows the whole-pattern profile fitting of diffraction data collected in parallel-beam flat-plate asymmetric reflection geometry is presented. In this arrangement, there is a fixed angle between the incident beam and the sample, resulting in a fixed-length beam footprint. The use of a wide-angle detector for the simultaneous detection of the data precludes the use of any diffracted beam optics. Therefore, the observed peak widths are a function of the length of the beam footprint on the sample. The model uses up to three refinable parameters, depending on the intensity profile of the beam, to calculate the effect of diffraction angle on the width of all diffracted peaks. The use of this model reduces the total number of parameters required to fit the observed peak widths and shapes, hence leading to increased stability in the profile analysis. Implementations of the model are provided for both fundamental parameters and empirical approaches.
.
You can download it here, or go to the journal website.
30/03: How awesome is this photo?
29/03: My car was written off!
It's just been picked up by the car transporter.
The car was out in the hail storm. We eventually got around to booking it into the insurance assessors to get the damage looked at. I was driving in thinking "I hope the repair work costs enough more than the excess so that I feel OK about spending the excess money."
After the assessment, the guy said "I'm going to write it off."
I couldn't believe it!
As a consequence, we went car shopping on Saturday. We bought (subject to finance and the insurance company actually writing off the car) a Toyota Corolla hatch. It should be in in a couple of weeks. We got a good deal on the car (red tag sale) and on the finance. We're pretty happy!
The car was out in the hail storm. We eventually got around to booking it into the insurance assessors to get the damage looked at. I was driving in thinking "I hope the repair work costs enough more than the excess so that I feel OK about spending the excess money."
After the assessment, the guy said "I'm going to write it off."
I couldn't believe it!
As a consequence, we went car shopping on Saturday. We bought (subject to finance and the insurance company actually writing off the car) a Toyota Corolla hatch. It should be in in a couple of weeks. We got a good deal on the car (red tag sale) and on the finance. We're pretty happy!
Why is it so hard to find out how to combine errors that arise from the experiment (the meter can only be read so well, the ruler is only marked in centimetres...) with statistical variation (20 measurements and the mean and standard deviation)???
All the textbooks I've looked at so far gloss over that point that the 10 values they have to create their mean and SD each have their own experimental error. (They may say that the experimental errors are too small to worry about, or not mention them at all.)
What do I do?
I can calculate an error by summing the values (and the same to the errors) and dividing by N (and the same thing for the errors) and get an average with an error attached.
I can also calculate the mean and the "Standard error of the mean" from a statistical standpoint.
What do I do if the two error values (experimental and statistical) are of the same order (eg 1.3 vs 1.5)? What if they're wildly different (eg 1.5 vs 50 or the other way around)?
Is there a "correct" way to combine the two sources of error/uncertainty in a measurement? Do I just have to use my gut when deciding on the cutoff of when to use statistical uncertainties or experimental errors?
All the textbooks I've looked at so far gloss over that point that the 10 values they have to create their mean and SD each have their own experimental error. (They may say that the experimental errors are too small to worry about, or not mention them at all.)
What do I do?
I can calculate an error by summing the values (and the same to the errors) and dividing by N (and the same thing for the errors) and get an average with an error attached.
I can also calculate the mean and the "Standard error of the mean" from a statistical standpoint.
What do I do if the two error values (experimental and statistical) are of the same order (eg 1.3 vs 1.5)? What if they're wildly different (eg 1.5 vs 50 or the other way around)?
Is there a "correct" way to combine the two sources of error/uncertainty in a measurement? Do I just have to use my gut when deciding on the cutoff of when to use statistical uncertainties or experimental errors?
13/03: I can post from an iPhone!
Just a quick post to show that this can work!
I love technology...
I love technology...
13/03: Water tanks
It's a nice fine day, but I still can't go and do the concreting for my water tanks.
The hole that I dug for everything to go in was filled with water several times over the last week's storms, and now when I walk on the ground, I sink in... :(
I've been relegated to cleaning the garage and kitchen.
The hole that I dug for everything to go in was filled with water several times over the last week's storms, and now when I walk on the ground, I sink in... :(
I've been relegated to cleaning the garage and kitchen.
12/03: Bingle in the shower
I just got sent this picture of a bingle in the shower...
:)
:)
