In the final tally there were assays for eight branches of the phylogeny, with assays specific to the following prominent isolates/clades and related isolates: B. abortus 2308, B. abortus 2308 + S19, B. melitensis 16 M, B. melitensis biovar 1, and

selleck compound B. suis 1330. From our diverse isolate collection we had the following distribution of calls for the branches, with the most derived call taking precedence over more ancestral calls: A = 1, B = 23, C = 8, D = 22, E = 7, F = 0, G = 15, H = 91, I = 33, J = 17, no derived call (all isolates not in species B. abortus, B. melitensis, or B. suis/ canis) = 25, no call for any assay = 7, ancestral within B. abortus = 12, ancestral within B. melitensis = 68, ancestral within B. suis = 11. Discussion Our assays show clear distinctions within and among B. abortus B. melitensis, and B. suis. Our CUMA assays targeted clade-specific SNPs that can be incorporated into most other genotyping assays such as TaqMan learn more Real-time PCR for increased

sensitivity [18, 19]. We have identified several important targets that should prove useful for clinical, epidemiological, and forensic purposes. For example, the assays targeting branches A, D, and I are specific to isolates closely related to B. abortus 2308 and B. abortus 9–941, and Selleck GSK2245840 B. suis 1330, respectively. The assays for F and G target the same branch and identify B. melitensis 16 M and closely related isolates. Isolates from B. abortus 2308 and 9–941, B. suis 1330, and B. melitensis 16 M are from common, genetically monomorphic clades of Brucella and the SNP assays developed here are a

reliable and useful way of identifying these four common groups. Branch E is particularly interesting in terms of Brucella taxonomy. The clade that this branch defines includes isolates from B. abortus biovars 1, 2, and 4. Potential issues with biovar and phylogenetic correspondence from in B. abortus have been noted previously [20]. Upon closer evaluation of the whole genomes used in our analyses, the apparent paraphyly within B. abortus biovar 1, since isolates from biovar 2 are within the biovar 1 clade, does not hold true when all the genomes are included. However, CUMA assays indicate that at least four isolates from other B. abortus biovars (3 of biovar 4, 1 of biovar 2) fall onto the B/C branch. This would suggest that either biovar 1 is paraphyletic or there have been issues with biovar determination. SNP-based approaches also enable assessment of errors in genome sequences. Whole genome comparisons of the region associated with SNP10621, which were intended to target branch J in B. suis/ B. canis, also share a SNP allele with B. abortus 9–941. Taken at face value, this would suggest homoplasy at this locus. Yet, in our CUMA assays B. abortus 9–941 did not group with B. suis, likely indicating sequencing error.

Figure 4 Localization

of expression of the TβR-II, Smad2, Smad3, Smad4, Smad7 and Staurosporine phosphorylated Smad2 in CNE2 cells. (A) The TβR-II was located mainly in the cell membrane, and positive staining Smad2, Smad3, Smad4, was found in regions of both cytoplasm and nucleus, while the staining of Smad7 was mainly in the area of nucleus. (B) Phosphorylated Smad2 was undetectable in CNE2 cells without TGF-β1, after stimulation with TGF-β1, phosphorylated Smad2 could be detected in the cytoplasm of CNE2 cells, while Smad7 located originally in nuclear BIBW2992 concentration without TGF-β1, and it could be detected in the cytoplasm after stimulation of TGF-β1. TGF-β1 inducing activation and translocation of Smad proteins in NPC cells To determine whether Smad is activated and translocated in response to TGF-β1 stimulation in CNE2 cells, we assessed the subcellular distribution of the phosphorylated (activated) Smad2/3 by immunocytochemistry staining. No phosphorylated Smad2/3 staining was exhibited in CNE2 cells without TGF-β1 this website stimulation, however, a very strong staining of phosphorylated Smad2/3 was found in regions of both the cytoplasm and nucleus of the CNE2 cells after TGF-β1 treatment compared to untreated cells. This result indicated that Smad2

was phosphorylated and activated after TGF-β1 stimulation. Furthermore, we investigated the inhibitory Smad-Smad 7 protein in response to TGF-β1 stimulation in CNE2 cells. The results indicated that the positive staining of Smad 7 initially was localized in the region of the nucleus before TGF-β1 treatment. However, positive staining of Smad 7 was observed in the cytoplasm after TGF-β1 treatment, which implied that Smad 7 translocated from the nucleus to the cytoplasm in response to the TGF-β1 stimulation (Figure 4B). Discussion TGF-β1 is a very potent inhibitor of many epithelial tumors, however, the role of TGF-β1 in nasopharyngeal Carcinoma progression is ambiguous. In the present study herein, we demonstrated for the first time that CNE2 cells have lost the sensitivity to growth suppression by TGF-β1 (Figure 1). Interestingly, rather than a defective TGF-β/Smad

signaling pathway which leads to a loss of response to the growth suppression effect of TGF-β1, our results indicate that the TGF-β/Smad signaling is functional in the CNE2 cell after Mannose-binding protein-associated serine protease treatment TGF-β1. The TβR-II is expressed normally, while Smads 2, Smads 3, Smads 4 are significantly increased at the mRNA level and the protein level compared to the levels observed in the normal nasopharyngeal epithelial cells (Figure 2, 3). The mRNA and protein expression of Smad7 remains unchanged in the CNE2 cells. Immunocytochemistry demonstrated that the transmembrane receptor TβR-II and the intracellular component Smads are also detectable (Figure 4A), where pretreatment of CNE2 cells with TGF-β1 causes activation of the Smad 2 protein, and the inhibitory Smad 7 translocates from the nucleus into the cytoplasm (Figure 4B).

Gel image data were converted into characteristics data sets. Cluster analysis of Neighbor-joining tree (N-J) was carried out using the categorical similarity coefficient

and the Ward method. A minimum spanning tree was inferred using characteristic data from cluster analysis. The polymorphism of each locus was represented by Nei’s diversity index [27], calculated as DI = 1-∑(allelic frequency)2. Reproducibility and stability of 12 VNTR loci via in-vitro passage Twenty clinical mTOR inhibitor strain genomes from China and Japan were amplified and multiple DNA samples from each strain yielded PCR products with identical sizes at all loci. Chongqing26 and Tibet36 each yielded no product at one locus, possibly because of MM-102 chemical structure mutations or poor quality DNA samples. The stabilities of the VNTR loci were investigated in a long-term experiment in which the 20 test H. pylori isolates used were sub-cultured into fresh Skirrow medium 30 times by serial passages at two or three day intervals. The DNA from the strains cultivated in each passage was extracted and subjected to MLVA analysis. The results of

the VNTR analysis demonstrated no difference in tandem repeat numbers (data not shown). Acknowledgements We thank Prof Chihiro Sasakawa of Institute of Medical Science University for Epacadostat providing 15 H. pylori strains of Tokyo. This work was supported by the fund of Chinese National Natural Science Foundation project Meloxicam (No. 31070445); Major State Basic Research Development Program of China (973 Program) (No. 2009CB522606). References 1. Ouakaa-Kchaou A, Elloumi H, Gargouri D, Kharrat J, Ghorbel A: Helicobacter pylori and gastric cancer. Tunis Med 2010,88(7):459–461.PubMed 2. Shin CM, Kim N, Yang HJ, Cho SI, Lee HS, Kim JS, Jung HC, Song IS: Stomach cancer risk in gastric cancer relatives: interaction between Helicobacter pylori infection and family history of gastric cancer for the risk of stomach cancer. J Clin Gastroenterol 2010,44(2):e34–39.PubMedCrossRef 3. Abdullah M, Ohtsuka H, Rani AA, Sato T, Syam AF, Fujino MA: Helicobacter pylori infection and gastropathy:

A comparison between Indonesian and Japanese patients. World J Gastroentero 2009,15(39):4928–4931.CrossRef 4. Ernst PB, Peura DA, Crowe SE: The translation of Helicobacter pylori basic research to patient care. Gastroenterology 2006,130(1):188–206. quiz 212–183PubMedCrossRef 5. Ben-Darif E, De Pinna E, Threlfall EJ, Bolton FJ, Upton M, Fox AJ: Comparison of a semi-automated rep-PCR system and multilocus sequence typing for differentiation of Salmonella enterica isolates. J Microbiol Methods 2010,81(1):11–16.PubMedCrossRef 6. Do T, Gilbert SC, Clark D, Ali F, Fatturi Parolo CC, Maltz M, Russell RR, Holbrook P, Wade WG, Beighton D: Generation of diversity in Streptococcus mutans genes demonstrated by MLST. PLoS One 2010,5(2):e9073.PubMedCrossRef 7.

Table 3 SNP location, primers and PCR designed for pyrosequencing analysis PCR primer sequence (5′ → 3′) Geneª SNP locationª PCRb Amplicon (bp)b Forwardb Reverseb dnaA:dnaN 1977 Multiplex 1 131 [M13] – TGAGAAGCTCTACGGTTGTTGTTCG TTTCACCTCACGATGAGTTCGATCC (Rv0001:Rv0002) Rv0260c 311613 114 CACCACTGTTGCCACGATGTTCTT [M13] – GGCGACTTGCTACGCGTCCTAC icd2 (Rv0066c) 74092 Multiplex 2 88 [M13] – GACGGTCCGAATTGCCTTGG GACCAGGAGAAGGCCATCAAAGAG phoT (Rv0820) 913274 141 GCAATCGCCGTGCAACC [M13] – CTGCATGTTATGGGTGACGATGAC Rv0095c 105139 Multiplex 3 94 ATAACGTCGGGCACTGACAAAGAG [M13]-TCCCGTATCAACTCGTAGGATCTGG

Rv0197 232574 81 CCACGGCGGGGACAAGAT [M13] -AGAAAGGCGCCGCTGTAGG qcrB (Rv2196) 2460626 Multiplex 4 120 [M13] this website – GGGCTCGCAGCCAGACTTC ATGATCACGGCGACCCAGAC leuB (Rv2995c) 3352929 108 [M13] – TCGACGTCCGGGTAGCATTC HSP inhibitor GCGTCGCAAGCATCTGACATT gyrA (Rv0006) codon 95 Simplex 320 CAGCTACATCGACTATGCGA [M13] – GGGCTTCGGTGTACCTCAT         Universal primer           [M13]: CGCCAGGGTTTTCCCAGTCACGAC   aGene name and SNP location in

M. tuberculosis H37Rv genome map (http://​tuberculist.​epfl.​ch/​). One gene is listed when SNP location is situated in that gene and two genes are listed when SNP is intergenic. bPCR name, amplicon expected size, and primers used. Results We analysed the MTC strain family distribution of 173 GSK1904529A isolates collected in 2010 from across Aragon (Table 1). Within this set and according with the spoligotyping analysis, the Haarlem genotype was the most frequent genotype (23.6%), followed by the T “ill defined” family (19.6%), U (15%) and LAM (13.8%). Other genotypes showing a defined SIT (9.8%) grouped in smaller groups. Those isolates showing a pattern with no SIT assigned Urease in the spolDB4 database corresponded to 17.9%. Among the 173 isolates, 91 isolates were included in the T, U and no SIT groups representing the 52.6% of the isolates. Accepting those with the same RFLP-IS6110 genotype as clone-related isolates and therefore belonging to the same family or lineage, only one isolate of each RFLP-IS6110 genotype, 101 isolates, were analysed by pyrosequencing (Figure 1). Once tested for the presence of the nine SNPs, we could confirm that those

isolates with the same spoligopattern held into the same SCG. For further analysis one isolate for each spoligopattern was selected resulting a sample of 75 different MTC strains. Seven of the 75 strains according with their SNPs in gyrA and katG genes were found to belong to PGG-1, 52 were included in PGG-2 and 16 were grouped in PGG-3. The strains in PGG-1 shared the SNPs for SCG-7, SCG-1, SCG-2 and SCG-3a. The SCG-3b, SCG-3c and SCG-5 met the feature for PGG-2. Finally, PGG-3 embraced the isolates in SCG-6a and a new SCG that from now on it will be mentioned as “SCG-6c”. The described SCG-6b pattern was only observed for the isolate of H37Rv used as a control. The distribution of these results is drawn and shown in Figure 2 and Table 4.

087 (0.871, 1.302) 0.109 (-0.209, 0.427) 1.073 (0.890) doxorubicin 101 1.074 (0.445) 1.074 (0.884, 1.265) 0.095 (-0.187, 0.376) 1.064 (0.902) 5-fluorouracil 108 1.365 (10.154) 1.366 * (1.130, 1.601) 0.436 * (0.164, 0.708) 1.344 (1.145) cyclophosphamide 110 0.791 (5.894) 0.790 (0.655, 0.925) -0.342 (-0.612, -0.073) 0.788 (0.673) The total number of co-occurrences with mild hypersensitivity reactions was 43,288. N: the

number of co-occurrences of each anticancer agent out of 43,288 pairs, PRR: the www.selleckchem.com/products/idasanutlin-rg-7388.html proportional reporting ratio, ROR: the reporting odds ratio, IC: the information component, EBGM: the empirical Bayes geometric mean. *: signal detected, see “”Methods”" for the detection criteria. Table 3 Signal detection for anticancer agent-associated severe hypersensitivity reactions   N PRR (χ2) ROR (95% two-sided CI) IC (95% two-sided CI) EBGM (95% one-sided CI) paclitaxel 79 2.273 * (55.041) Adavosertib 2.278 * (1.826,

2.730) 1.151 * (0.833, 1.469) 2.174 (1.803) docetaxel 18 0.588 (4.805) 0.587 (0.370, 0.805) -0.773 (-1.431, -0.115) 0.591 (0.401) doxorubicin 41 1.036 (0.021) 1.036 (0.762, 1.309) 0.032 (-0.408, 0.471) 1.014 (0.782) 5-fluorouracil 44 1.320 (3.102) 1.321 (0.982, 1.659) 0.374 (-0.051, 0.799) 1.276 (0.994) GDC 0068 cyclophosphamide 51 0.871 (0.851) 0.871 (0.661, 1.080) -0.209 (-0.604, 0.185) 0.862 (0.683) The total number of co-occurrences with severe hypersensitivity reactions was 18,255. N: the number of co-occurrences of each anticancer agent out of 18,255 pairs, PRR: the proportional reporting ratio, ROR: the reporting odds ratio, IC: the information component, EBGM: the empirical Bayes geometric mean. *: signal detected, see “”Methods”" for the detection criteria. Table 4 Signal detection for anticancer agent-associated lethal hypersensitivity ID-8 reactions   N PRR (χ2) ROR (95% two-sided CI) IC (95% two-sided CI) EBGM (95% one-sided CI) paclitaxel 12 2.623 * (10.495) 2.631 * (1.492,

3.770) 1.165 * (0.363, 1.967) 1.992 (1.237) docetaxel 17 4.224 * (38.715) 4.247 * (2.635, 5.858) 1.800 * (1.121, 2.478) 3.268 * (2.062) doxorubicin 9 1.728 (2.086) 1.731 (0.900, 2.563) 0.614 (-0.305, 1.533) 1.401 (0.819) 5-fluorouracil 10 2.281 * (5.977) 2.286 * (1.228, 3.344) 0.964 * (0.089, 1.838) 1.735 (1.037) cyclophosphamide 9 1.169 (0.083) 1.170 (0.608, 1.731) 0.127 (-0.792, 1.046) 1.047 (0.613) The total number of co-occurrences with lethal hypersensitivity reactions was 2,397. N: the number of co-occurrences of each anticancer agent out of 2,397 pairs, PRR: the proportional reporting ratio, ROR: the reporting odds ratio, IC: the information component, EBGM: the empirical Bayes geometric mean. *: signal detected, see “Methods” for the detection criteria. Discussion The AERS database covers several million case reports on adverse events. Pharmacovigilance analysis aims to search for previously unknown patterns and automatically detect important signals, i.e., drug-associated adverse events, from such a large database.

MLSA has shown that all isolates from Greece form a distinct lineage related to pathogens of kiwifruit check details (P. syringae pv. actinidiae; Pan[4], a.k.a. Psa[5]) and plum (P. syringae pv. morsprunorum; Pmp) in phylogroup 1. This phylogroup also includes a large number of pathogens of herbaceous plants, including the well-studied P. syringae pv. tomato strain Pto DC3000. In contrast, Italian isolates collected during outbreaks in the 1990s cluster together in phylogroup 2, along with pathogens of peas, cereals, and other plants, including the well-studied P.

syringae pv. syringae strain Psy B728a. More recent outbreaks of hazelnut decline in Italy from 2002–2004 were caused by Pav that phylogenetically clusters with the Greek isolates in phylogroup 1. In order to determine the genetic

changes accompanying the SCH727965 solubility dmso evolution of hazelnut pathogenesis in these two independent lineages, we obtained draft whole genome sequences for the earliest isolate of the hazelnut decline pathogen, Pav BP631, a phylogroup 1 strain isolated from Drama, Greece in 1976 and for Pav Ve013 and Pav Ve037, two strains isolated in Rome, Italy in the early 1990s. The latter two strains represent the extremes of genetic diversity observed in phylogroup 2 Pav strains as determined by the MLSA analysis of find more Wang et al.[6]. This MLSA analysis indicates that Pav Ve037 clusters with pea pathogens (P. syringae pv. pisi; Ppi) while the other strains group with pathogens of beets (P. syringae pv. aptata; Ptt) and barley (P. syringae pv. japonica; Pja) although Hydroxychloroquine with very weak phylogenetic support. We compared these three draft genome sequences to 27 other complete or draft P. syringae genome sequences representing 16 pathovars, including seven phylogroup

1 strains and six phylogroup 2 strains [4, 7–17]. We performed ortholog analysis to identify instances of horizontal gene transfer between the two independent Pav lineages and looked in detail at the evolutionary histories of a number of candidate pathogenicity genes, including the type III secreted effectors (T3SEs) that are translocated into host cells and are important for both suppressing and eliciting defense responses. We show that the two lineages have dramatically different T3SE profiles and that Pav BP631 has undergone extensive secretome remodeling. Results Genome sequencing and assembly 43 million read pairs were generated from the Pav BP631 paired-end library, while the Pav Ve013 and Pav Ve037 paired-end libraries produced 59 million and 35 million read pairs respectively (Table 1). The 82 bp reads for the latter two strains resulted in considerably longer contigs (N50s of 31 kb and 61 kb) than the 38 bp Pav BP631 reads (N50 of 6.4 kb). The read depth of the contigs was very uniform for Pav Ve013 and Pav Ve037, with almost all the contigs centered around a depth of 1000X (Figure 1).

Salmonella serotype Inoculation level (cfu/25 g) Real-time PCRa Salmonella BAX Detection System     Ct-value for Salmonella Ct-value for IAC Final result Final result Infantis 1000 20.05 27.89 Positive Positive   100 21.66 29.09 Positive Positive   10 27.14 28.68 Positive Positive   10 30.59 28.95 Positive Positive   10 24.92 28.89 Positive Positive   5 29.42 29.09 Positive Positive   5 26.57 28.81 Positive Positive   5 26.29 27.66 Positive

Positive OICR-9429   5 26.63 28.79 Positive Positive   2 27.70 28.42 Positive Positive   2 25.68 28.08 Positive Positive   2 27.86 28.56 Positive Positive   2 27.20 28.90 Positive Positive Agona 1000 22.47 28.97 Positive Positive   100 24.70 27.93 Positive Positive   10 > 36 29.21 Negative Negative   10 > 36 29.07 Negative Negative   10 26.04 28.93 Positive

Positive   5 28.47 28.76 Positive Positive   5 32.93 28.53 Positive Negative   5 29.84 28.92 Positive Positive   5 32.17 27.90 Positive Positive   2 > 36 28.76 Negative Positive   2 > 36 29.07 Negative Negative   2 33.22 28.77 Positive Positive   2 30.61 27.96 Positive Positive Infantis 1000 19.59 29.01 Positive Positive   100 23.74 28.86 Positive Positive   10 25.55 28.45 Positive Positive   10 24.85 28.40 Positive Positive   10 26.82 28.36 Positive Positive   5 29.82 29.10 Positive Positive   5 29.03 28.16 Positive Positive   5 24.77 28.28 Positive Positive   5 > 36 > 40 Inconclusive Positive Temsirolimus in vivo   2 28.61 27.88 Positive Positive   2 26.24 28.79 Positive Positive   2 26.02 28.82 Positive Positive   2 > 36 28.63 Negative Negative Results from 39 pork meat samples inoculated with salmonella at different levels and analyzed in parallel on-site using the real-time PCR and the Salmonella BAX methods. Cytidine deaminase a Samples with a Ct value > 36 is considered negative if the Ct value for the IAC is

< 40 and inconclusive if a Ct > 40 is obtained for the IAC. According to the Selleckchem MK-0457 Method Directive for the PCR method, re-analysis of the extracted DNA by PCR is then needed. Discussion The real-time PCR method validated in the present study is intended as a diagnostic tool for routine use in the meat industry, and therefore has specific demands on speed, ease of automation as well as robustness and reproducibility. Furthermore, the method must be specific for Salmonella and have detection limit comparable with or better than the culture-based methods in use today as official methods. Using the PCR method, the total time for the analysis of Salmonella in meat samples was decreased from at least 3 days for the standard culture-based method [3] to 14 h for meat samples and 16 h for swabs. The time for analysis is comparable with the fastest validated DNA-based analysis kit (e.g. from Bio-Rad and GeneSystems) on the market for meat samples and 1–3 h shorter for swab samples. For the meat producer, this means that the meat can be released faster, leading to decreased costs for storage and prolonged shelf life at the retailers.

3, p = 0.76) and no significant interaction between condition and time (F = 0.3, Table 1 Heart rate (mean ± SD) in bpm over the 90 minute cycling time-course of 0–5, 15–20, 30–35, 45–50, 60–65, 75–80 and 90 minutes for each of the three experimental conditions Heart rate (bpm) Time (min) 0-5 15-20 30-35 45-50 60-65 75-80 90 CHO 124 ± 10 128 ± 11 131 ± 9 133 ± 11 135 ± 10 137 ± 10 141 ± 12 CHO-PRO 126 ± 9 132 ± 12 136 ± 12 138 ± 12 140 ± 12 141 ± 12 142 ± 13 CHO-PRO-PEP 126 ± 11 131 ± 12 134 ± 11 137 ± 12 138 ± 12 140 ± 11 this website 141 ±10 CHO carbohydrate; CHO-PRO R406 order carbohydrate and protein; CHO-PRO-PEP carbohydrate,

protein and marine peptides. Table 2 Blood glucose and lactate (mean ± SD) profile over the 90 minute cycling time-course of 0–5, 15–20, 30–35, 45–50, 60–65, 75–80 and 90 minutes for each of the three experimental conditions Blood glucose (mmol · L-1) Time (min) 0-5 15-20 30-35 45-50 60-65 75-80 90 CHO 5.5 ± 0.6 5.6 ± 0.5 5.6 ± 0.6 5.5 ± 0.5 5.4 ± 0.4 5.3 ± 0.4 5.1 ± 0.8 CHO-PRO 5.5 ± 0.3 Ubiquitin inhibitor 5.5 ± 0.4 5.5 ± 0.4 5.4 ± 0.3 5.2 ± 0.3 5.2 ± 0.3 5.3 ± 0.4 CHO-PRO-PEP 5.5 ± 0.5 5.6 ± 0.6 5.4 ± 0.8 5.4 ± 0.4

5.3 ± 0.2 5.3 ± 0.3 5.4 ± 0.2 Blood lactate (mmol · L -1 ) Time (min) 0-5 15-20 30-35 45-50 60-65 75 -80 90 CHO 2.8 ± 1.0 2.9 ± 1.3 2.5 ± 1.0 2.4 ± 0.8 2.0 ± 0.8 1.8 ± 0.4 1.9 ± 0.5 CHO-PRO 3.0 ± 0.9 3.0 ± 1.1 2.6 ± 2.3 2.3 ± 0.7 2.0 ± 0.6 1.9 ± 0.4 1.7 ± 0.3 CHO-PRO-PEP 2.9 ± 0.9 2.9 ± 1.0 2.4 ± 0.8 2.3 ± 0.8 1.9 ± 0.7 2.1 ± 0.6 2.0 ± 0.7 CHO carbohydrate; CHO-PRO carbohydrate and protein; CHO-PRO-PEP carbohydrate, protein and marine peptides. There was no appreciable overall difference in blood lactate concentrations between conditions (F = 0.8, p = 0.46), however there was a significant

decrease in blood lactate concentration Nutlin-3 ic50 over the 90 min (F = 27.7, p = < 0.001), which was moderated by condition (F = 4.3, p = 0.016). Mean RPE significantly increased from approximately 9 to 12 units over the 90 min (F = 23.6, p = 0.001) and also exhibited a quadratic trend, where the rate of increase in RPE slowed down over time (F = 64.3, p < 0.001). The RPE was very similar across conditions, both as a main effect (F = 0.06, p = 0.94) and as an interaction with time (F = 0.3, p = 0.76).

The pulse results in an increase in voltage on top of the V oc for each cell. PVD

data were smoothed via a moving average, and the half-life of the decay was used as characteristic lifetime. Extracted charge was estimated from the PCD data by integrating the resulting FK228 solubility dmso transient signals. Results and discussion Figure 2a,b,c presents surface scanning electron microscopy (SEM) selleck kinase inhibitor images of the Thin/NR cells at different stages of fabrication. Densely packed nanorods were obtained over the entire deposition area on bare ITO. The 3D conformal nature of the cell surface can be appreciated from the SEM surface images, where the structure of the array can still be observed both after the blend coating (Figure 2b), and Ag contacts were applied (Figure 2c). Figure 2 SEM/STEM characterization. (a) Electrodeposited ZnO nanorod arrays, (b) arrays coated with a thin P3HT:PCBM highly conformal layer, (c) Ag contact evaporated on top of the P3HT:PCBM layer (Thin/NR cells) with arrows indicating a few spots where shadowing from the nanorods prevented Ag deposition, (d) cross-sectional image of a Thin/NR cell, (e, f) cross-sectional images

of different areas of the Thin/NR cell, (g, h) STEM images of cross sections of Thin/NR samples and (i) cross-sectional image of a conventional hybrid cell (Thick/NR). Figure 2d,e,f,g,h presents SEM and STEM cross-sectional images of the Thin/NR cells. Figure 2i shows a conventional find more Thick/NR hybrid cell. It is seen that the nanorods are approximately 800-nm long, being coated by a thin layer of P3HT:PCBM blend (<50 nm as observed from the leading edge of the blend adjacent to the nanorod in Figure 2g, although the exact value was difficult to elucidate and some gradient could be present from the top to the bottom of the nanorods), and <50 nm Ag. The high conformality of the blend coating is best exemplified by Figure 2d,e,f,g,h. Approximately 50 nm is well below the mean free path of both electrons and holes in

a polymer-fullerene blend; thus the blend morphology most likely does not even have to be completely optimised [29]. Although the Ag coating on the ZnO nanorods is less uniform than the blend coating, owing to the fact that Ag preferentially deposits on surfaces selleck screening library exposed to the vapour source (see left-hand side of Figure 2d), the large sample-boat distance in the evaporator (35 cm) ensures a relatively high Ag coverage of the NRs. This is most clearly seen in Figure 2c, where only some small spots in the sample (see arrows in the figure) are not coated by Ag due to shadowing from adjacent rods), and also in Figure 2g where Ag can be seen forming a quasi-conformal coating all over the surface of a ZnO rod. The quasi-conformal Ag coating is found to be important for improving charge extraction and contributing to light trapping in the cell, as will be discussed later. Figure 3a,b shows the EQE and PV data for the best Thin/NR and Thick/NR cells obtained, respectively.

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