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The direct coupling between headspace solid phase microextraction and quartz tube-atomic absorption spectrometry detection has been evaluated for speciation of methylmercury in seafood after volatilization of its hydride or chloride derivative. Following variable optimisation in both procedures, a critical influence of the fibre sorbent material was observed. Liquid-phase coatings such as polydimethylsiloxane (PDMS) and 'solid' phase coatings such as polydimethylsiloxane-divinylbenzene (PDMS/DVB) or fused silica were compared. Best LODs were provided by the silica fibre for CH 3 HgH (0.4 ng mL 21 ) and by the PDMS/DVB fibre for CH 3 HgCl (0.06 ng mL 21 ). In general, repeatability was about 6% for the preconcentration of both Hg derivatives in the different fibre coatings. The affinity to sorption of CH 3 HgH was slightly improved when using silica fibres loaded with L-cysteine or palladium nitrate. Sorption/desorption studies with fused silica fibres allowed us to hypothesise that CH 3 HgH formed upon reaction of MeHg 1 with NaBH 4 -NaOH suffered from decomposition onto the fibre to yield Hg 0 like direct reaction between Hg 21 and NaBH 4 -NaOH. Generation of the chloride derivative showed an enhanced selectivity as compared with generation of the hydride. Nevertheless, experimental evidence of decomposition of the chloride derivative upon heating is also given. For methylmercury determination in seafood, hydride or chloride generation and headspace SPME were applied after ultrasound assisted extraction of methylmercury in 2 mol dm 23 HCl. Validation was carried out against CRM DORM-2 (dogfish muscle) and CRM 464 (Tuna fish), demonstrating good trueness of the methods proposed. A better LOD of MeHg 1 is obtained as compared with other SPME couplings described in the literature.

Several variables associated with motivation toward science learning in preservice elementary teachers, such as relevance to personal goals and self‐efficacy, are important factors in preservice preparation. The main objective is to analyze the relationships of these motivational variables with the emotions of boredom and enjoyment, and with engagement in science learning. Data were obtained with a self‐report questionnaire completed by 871 preservice elementary teachers and analyzed using the structural equation model (SEM) methodology. The first step determined the adequacy of the measurement model and, in the second step, SEM showed that the variables associated with motivation for science learning significantly predicted the variance of boredom and enjoyment as 27% and 52%, respectively. The variables associated with motivation for science learning and emotions explained 67% of the variance in science learning engagement. Relevance to personal goals and self‐efficacy in science learning predicted the emotional variables. Emotional variables mediated the impact of motivational variables in science‐learning engagement. The fundamental role of emotions in motivation and science‐learning engagement in preservice elementary teachers is observed.