Atomic force microscopy (AFM) quantitatively maps viscoelastic parameters of polymers on the nanoscale by several methods. The loss tangent, the ratio between dissipated and stored energy, was measured on a blend of thermoplastic polymer materials by a dynamic contact method, contact resonance, and by a recently developed loss tangent measurement by amplitude modulation AFM. Contact resonance measurements were performed both with dual AC resonance tracking and band excitation (BE), allowing for a reference-free measurement of the loss tangent. Amplitude modulation AFM was performed where a recent interpretation of the phase signal under certain operating conditions allows for the loss tangent to be calculated. The loss tangent measurements were compared with values expected from time–temperature superposed frequency-dependent dynamical mechanical curves of materials and reveal that the loss tangents determined from the BE contact resonance method provide the most accurate values.

Bimodal atomic force microscopy (AFM) is a recently developed technique of dynamic AFM where a higher eigenmode of the cantilever is simultaneously excited along with the fundamental eigenmode. The effects of different operating parameters while imaging an impact copolymer blend of polypropylene (PP) and ethylene-propylene (E-P) rubber in bimodal mode are explored through experiments and numerical simulations. The higher mode amplitude and phase contrasts between the two components of the sample reverse at different points as the free amplitude of the higher eigenmode is increased. Three different regimes are identified experimentally depending on the relative contrast between the PP and the E-P rubber. It is observed that the kinetic energy and free air drive input energy of the two cantilever eigenmodes play a role in determining the regimes of operation. Numerical simulations conducted with appropriate tip-sample interaction forces support the experimental results. An understanding of these regimes and the associated cantilever dynamics will guide a rational approach towards selecting appropriate operating parameters.

The authors address two novel and significant challenges in using online text reviews to obtain attribute level ratings. First, they introduce the problem of inferring attribute level sentiment from text data to the marketing literature and develop a deep learning model to address it. While extant bag of words based topic models are fairly good at attribute discovery based on frequency of word or phrase occurrences, associating sentiments to attributes requires exploiting the spatial and sequential structure of language. Second, they illustrate how to correct for attribute selfselection-reviewers choose the subset of attributes to write about-in metrics of attribute level restaurant performance. Using Yelp.com reviews for empirical illustration, they find that a hybrid deep learning (CNN-LSTM) model, where CNN and LSTM exploit the spatial and sequential structure of language respectively provide the best performance in accuracy, training speed and training data size requirements. The model does particularly well on the "hard" sentiment classification problems. Further, accounting for attribute self-selection significantly impacts sentiment scores, especially on attributes that are frequently missing.

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