Expansion-limited aggregation of nanoclusters in a single-pulse laser-produced plume

Formation of carbon nanoclusters in a single-laser-pulse created ablation plume was studied both in vacuum and in a noble gas environment at various pressures. The developed theory provides cluster radius dependence on combination of laser parameters, properties of ablated material, and type and pressure of an ambient gas in agreement with experiments. The experiments were performed on carbon nanoclusters formed by laser ablation of graphite targets with 12 picosecond 532 nm laser pulses at MHz-range repetition rate in a broad range of ambient He, Ar, Kr, and Xe gas pressures from 2× 10-2 to 1500 Torr. The experimental results confirmed our theoretical prediction that the average size of the nanoparticles depends weakly on the type of the ambient gas used, and is determined exclusively by the single laser pulse parameters even at the repetition rate as high as 28 MHz with the time gap 36 ns between the pulses. The most important finding relates to the fact that in vacuum the cluster size is mainly determined by hydrodynamic expansion of the plume while in the ambient gas it is controlled by atomic diffusion in the gas. We demonstrate that the ultrashort pulses can be used for production of clusters with the size less than the critical value, which separates the particles with properties drastically different from those of a material in a bulk. The presented results of experiments on formation of carbon nanoclusters are in close agreement with the theoretical scaling. The developed theory is applicable for cluster formation from any monatomic material, such as silicon for example

Cluster formation through the action of a single picosecond laser pulse

We demonstrate experimentally and describe theoretically the formation of carbon nanoclusters created by single picosecond laser pulses. We show that the average size of a nanocluster is determined exclusively by single laser pulse parameters and is independent of the gas fill (He, Ar, Kr, Xe) and pressure in a range from 20mTorr to 200 Torr. Simple kinetic theory allows estimates to be made of the cluster size, which are in qualitative agreement with the experimental data. We conclude that the role of the buffer gas is to induce a transition between thin solid film formation on the substrate and foam formation by diffusing the clusters through the gas, with no significant effect upon the average cluster size

Picosecond high-repetition-rate pulsed laser ablation of dielectrics: the effect of energy accumulation between pulses

We report experiments on the ablation of arsenic trisulphide and silicon using high-repetition-rate (megahertz) trains of picosecond pulses. In the case of arsenic trisulphide, the average single pulse fluence at ablation threshold is found to be >100 times lower when pulses are delivered as a 76-MHz train compared with the case of a solitary pulse. For silicon, however, the threshold for a 4.1-MHz train equals the value for a solitary pulse. A model of irradiation by high-repetition-rate pulse trains demonstrates that for arsenic trisulphide energy accumulates in the target surface from several hundred successive pulses, lowering the ablation threshold and causing a change from the laser-solid to laser-plasma mode as the surface temperature increases

Origin of magnetic moments in carbon nanofoam

A range of carbon nanofoam samples was prepared by using a high-repetition-rate laser ablation technique under various Ar pressures. Their magnetic properties were systematically investigated by dc magnetization measurements and continuous wave (cw) as well as pulsed EPR techniques. In all samples we found very large zero-field cooled-field-cooled thermal hysteresis in the susceptibility measurements extending up to room temperature. Zero-field cooled (ZFC) susceptibility measurements also display very complex behavior with a susceptibility maximum that strongly varies in temperature from sample to sample. Low-temperature magnetization curves indicate a saturation magnetization MS ≈0.35 emu g at 2 K and can be well fitted with a classical Langevin function. MS is more than an order of magnitude larger than any possible iron impurity, proving that the observed magnetic phenomena are an intrinsic effect of the carbon nanofoam. Magnetization measurements are consistent with a spin-glass type ground state. The cusps in the ZFC susceptibility curves imply spin freezing temperatures that range from 50 K to the extremely high value of >300 K. Further EPR measurements revealed three different centers that coexist in all samples, distinguished on the basis of g -factor and relaxation time. Their possible origin and the role in the magnetic phenomena are discussed

Design of TKR Tibial Insert for Bowlegged Gait

One of the causes associated with total knee replacement (TKR) surgery is abnormal gait. In these gait cases, however, the TKR does not solve the ultimate issue because abnormal gait still occurs, and similar excessive forces still occur on the knee. It is likely that the TKR will experience accelerated wear, and the individual may have to undergo a second TKR sometime in the future. For our purposes, bowleggedness is either the external rotation of the hip or excessive varus of the knee. It was hypothesized that the inability to internally rotate the hip increases adduction moment and medial compartment stresses. In order to test this hypothesis, we created an analytical model to determine forces and moments at the knee. Results supported our hypothesis. In attempt to decrease the elevated stresses in the medial compartment, we created several models which modified the tibial plastic of the ADVANCE® Medial Pivot Knee. We performed stress analyses in ABAQUS and conducted experiments on each of the models. Based on our results we recommend the thickened anterior medial cusp implant for those with external rotation and the angled tray implant for those with varus deformity

Evaluating methods for the analysis of rare variants in sequence data

A number of rare variant statistical methods have been proposed for analysis of the impending wave of next-generation sequencing data. To date, there are few direct comparisons of these methods on real sequence data. Furthermore, there is a strong need for practical advice on the proper analytic strategies for rare variant analysis. We compare four recently proposed rare variant methods (combined multivariate and collapsing, weighted sum, proportion regression, and cumulative minor allele test) on simulated phenotype and next-generation sequencing data as part of Genetic Analysis Workshop 17. Overall, we find that all analyzed methods have serious practical limitations on identifying causal genes. Specifically, no method has more than a 5% true discovery rate (percentage of truly causal genes among all those identified as significantly associated with the phenotype). Further exploration shows that all methods suffer from inflated false-positive error rates (chance that a noncausal gene will be identified as associated with the phenotype) because of population stratification and gametic phase disequilibrium between noncausal SNPs and causal SNPs. Furthermore, observed true-positive rates (chance that a truly causal gene will be identified as significantly associated with the phenotype) for each of the four methods was very low (<19%). The combination of larger than anticipated false-positive rates, low true-positive rates, and only about 1% of all genes being causal yields poor discriminatory ability for all four methods. Gametic phase disequilibrium and population stratification are important areas for further research in the analysis of rare variant data

Translating HbA1c measurements into estimated average glucose values in pregnant women with diabetes

Aims/hypothesis This study aimed to examine the relationship between average glucose levels, assessed by continuous glucose monitoring (CGM), and HbA1c levels in pregnant women with diabetes to determine whether calculations of standard estimated average glucose (eAG) levels from HbA1c measurements are applicable to pregnant women with diabetes. Methods CGM data from 117 pregnant women (89 women with type 1 diabetes; 28 women with type 2 diabetes) were analysed. Average glucose levels were calculated from 5–7 day CGM profiles (mean 1275 glucose values per profile) and paired with a corresponding (±1 week) HbA1c measure. In total, 688 average glucose–HbA1c pairs were obtained across pregnancy (mean six pairs per participant). Average glucose level was used as the dependent variable in a regression model. Covariates were gestational week, study centre and HbA1c. Results There was a strong association between HbA1c and average glucose values in pregnancy (coefficient 0.67 [95% CI 0.57, 0.78]), i.e. a 1% (11 mmol/mol) difference in HbA1c corresponded to a 0.67 mmol/l difference in average glucose. The random effects model that included gestational week as a curvilinear (quadratic) covariate fitted best, allowing calculation of a pregnancy-specific eAG (PeAG). This showed that an HbA1c of 8.0% (64 mmol/mol) gave a PeAG of 7.4–7.7 mmol/l (depending on gestational week), compared with a standard eAG of 10.2 mmol/l. The PeAG associated with maintaining an HbA1c level of 6.0% (42 mmol/mol) during pregnancy was between 6.4 and 6.7 mmol/l, depending on gestational week. Conclusions/interpretation The HbA1c–average glucose relationship is altered by pregnancy. Routinely generated standard eAG values do not account for this difference between pregnant and non-pregnant individuals and, thus, should not be used during pregnancy. Instead, the PeAG values deduced in the current study are recommended for antenatal clinical care

Evaluating methods for combining rare variant data in pathway-based tests of genetic association

Analyzing sets of genes in genome-wide association studies is a relatively new approach that aims to capitalize on biological knowledge about the interactions of genes in biological pathways. This approach, called pathway analysis or gene set analysis, has not yet been applied to the analysis of rare variants. Applying pathway analysis to rare variants offers two competing approaches. In the first approach rare variant statistics are used to generate p-values for each gene (e.g., combined multivariate collapsing [CMC] or weighted-sum [WS]) and the gene-level p-values are combined using standard pathway analysis methods (e.g., gene set enrichment analysis or Fisher’s combined probability method). In the second approach, rare variant methods (e.g., CMC and WS) are applied directly to sets of single-nucleotide polymorphisms (SNPs) representing all SNPs within genes in a pathway. In this paper we use simulated phenotype and real next-generation sequencing data from Genetic Analysis Workshop 17 to analyze sets of rare variants using these two competing approaches. The initial results suggest substantial differences in the methods, with Fisher’s combined probability method and the direct application of the WS method yielding the best power. Evidence suggests that the WS method works well in most situations, although Fisher’s method was more likely to be optimal when the number of causal SNPs in the set was low but the risk of the causal SNPs was high