How do you explain that ? There's only one operator but the mixed model returns an estimate for the `operator` random effect. Furthermore the `sample` effect is confounded with the interaction `sample:operator`. Below is the R code and SAS give the same results.

    > dd
       sample operator         y
    9      10      SCF 0.9153188
    10     10      SCF 0.9884982
    19    100      SCF 2.0798781
    20    100      SCF 2.0464027
    29   1000      SCF 3.0401590
    30   1000      SCF 3.0114448
    39  10000      SCF 4.1348324
    40  10000      SCF 4.0840063
    49  1e+05      SCF 5.1235795
    50  1e+05      SCF 5.1106381
    59  1e+06      SCF 6.0803404
    60  1e+06      SCF 6.2353263
    > str(dd)
    'data.frame':   12 obs. of  3 variables:
     $ sample  : Factor w/ 6 levels "10","100","1000",..: 1 1 2 2 3 3 4 4 5 5 ...
     $ operator: Factor w/ 1 level "SCF": 1 1 1 1 1 1 1 1 1 1 ...
     $ y       : num  0.915 0.988 2.08 2.046 3.04 ...
    > lmer(y ~ (1|sample)+(1|operator)+(1|sample:operator), data=dd) 
    Linear mixed model fit by REML 
    Formula: y ~ (1 | sample) + (1 | operator) + (1 | sample:operator) 
       Data: dd 
      AIC   BIC logLik deviance REMLdev
     18.6 21.03 -4.302    9.932   8.605
    Random effects:
     Groups          Name        Variance   Std.Dev.
     sample:operator (Intercept) 1.87954740 1.370966
     sample          (Intercept) 1.87954925 1.370967
     operator        (Intercept) 0.00063096 0.025119
     Residual                    0.00283931 0.053285
    Number of obs: 12, groups: sample:operator, 6; sample, 6; operator, 1
    
    Fixed effects:
                Estimate Std. Error t value
    (Intercept)   3.5709     0.7921   4.508

For those who are more familiar with SAS the corresponding code is:

    PROC MIXED DATA=dd;
    CLASS sample operator;
    MODEL y=;
    RANDOM sample operator sample*operator;
    RUN;

This is nothing but the crossed 2-way ANOVA with random effects.