This paper describes an electrowetting on dielectric (EWOD) digital microfluidic-based lab-on-a-chip (LOC) integrated with on-chip electrochemical microsensor by IC compatible fabrication process, and its application for the entire online biosensing process capable of fully automatic analysis for ferrocenemethanol (FcM) and dopamine (DA). In this work, we made full use of the parallel-plate structure of the EWOD digital microfluidic device to fabricate the microfluidic module on the bottom plate and the three-microelectrode-system-integrated electrochemical cell together with patterned ground electrode on the top plate. The proposed LOC possesses the multifunction of: (1) creating, merging and transporting of microliter-level sample droplets, (2) online biosensing, and (3) droplets recycling. The three-electrodeintegrated microsensor not only reveals a sensitive electrochemical detection for FcM in a wide concentration range (10 μM-1.0 mM), but also shows good stability, selectivity and reproducibility for surface-controlled detection of DA. The calibration of DA was linear for concentration from 1.0 to 50.0 μM with a high sensitivity of 2145 nA μM −1 cm −2 (R 2 = 0.9933) and estimated detection limit of 0.42 μM (signal/noise ratio of 3). This work shows the promise of state-of-the-art digital microfluidic biosensors for fully automatic online bioanalysis in a future LOC to perform on-chip biomedical protocols in vitro diagnostic assays.
Creativity research often relies on human raters to judge the novelty of participants’ responses on open-ended tasks, such as the Alternate Uses Task (AUT). Albeit useful, manual ratings are subjective and labor intensive. To address these limitations, researchers increasingly use automatic scoring methods based on a natural language processing technique for quantifying the semantic distance between words. However, many methodological choices remain open on how to obtain semantic distance scores for ideas, which can significantly impact reliability and validity. In this project, we propose a new semantic distance-based method, maximum associative distance (MAD), for assessing response novelty in AUT. Within a response, MAD uses the semantic distance of the word that is maximally remote from the prompt word to reflect response novelty. We compare the results from MAD with other competing semantic distance-based methods, including element-wise-multiplication—a commonly used compositional model—across three published datasets including a total of 447 participants. We found MAD to be more strongly correlated with human creativity ratings than the competing methods. In addition, MAD scores reliably predict external measures such as openness to experience. We further explored how idea elaboration affects the performance of various scoring methods and found that MAD is closely aligned with human raters in processing multi-word responses. The MAD method thus improves the psychometrics of semantic distance for automatic creativity assessment, and it provides clues about what human raters find creative about ideas.
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