Sentence examples for samples even when from inspiring English sources

The methods of synthesis can generate oriented samples, even when the materials are poorly crystalline.

These new additives present better results than the commercially available polymer-based inhibitors in all tested samples, even when applied at smaller concentrations, compared to many other polymer-based inhibitors reported in the literature.

Our results revealed that well trained dogs were able to detect human cadaveric blood samples even when very low concentrations of blood were stored in the tubes, showing high levels of olfactory sensitivity and to discriminate the target odor even when the non-target odor was orders of magnitude higher in concentrations.

False positive probability, defined as the probability of failure to meet the minimum necessary requirement in future samples even when the tested sample satisfies the acceptance criteria in fact were used to evaluate the adequacy of the sampling regimen and acceptance criteria.

The molecular set for Paenibacillus sp. detected the bacterium attached to Steinernema diaprepesi suspended in water or added to nematodes recovered from soil samples but its detection decreased markedly in the soil samples, even when a nested PCR protocol was employed.

I have to be able to take samples even when conditions aren't optimal.

Furthermore, the STF hermetically sealed the pierced sample, even when the tyre mockup was inflated at high internal pressure.

Important differences are revealed in comparisons between the estimated asymptotic variances derived assuming random and clustered sampling, even when there are only a few observations per cluster.

The HI values are slightly superior for Ph-PMO-PrSO3H solid than for SBA-15-PrSO3H samplevenen when trimethylsilyl groups are incorporated in this latter catalyst.

This implies that we can detect the background sample, even when the background model is not robust or large.

As for the robustness of the proposed scheme to identity drift, it mainly benefits from the binary classifier based on sparse representation, which can effectively identify the background sample even when the background model is not well trained.