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Thermoplastic polyamide elastomers (TPAEs) have emerged as important thermoplastic elastomer materials with significant potentials owing to their excellent low‐temperature flexibility, easy processability, lightweight, good thermal stability and mechanical properties, etc. Over the last few decades, TPAEs have rapidly developed into a large variety of kinds of TPAEs. Striking advancements have been achieved in this research field. Unfortunately, there is not yet a review to summarize the progress. The present paper summarizes the major advancements dealing with TPAEs, in which synthesis, structures/properties as well as applications are introduced in detail. Moreover, current challenges and main developmental directions dealing with TPAEs are also presented. This review may motivate more interest in TPAEs, further facilitating their all‐side research and more large‐scale applications.

Thermoplastic polyamide elastomer (TPAE) is a kind of high‐performance elastomers prepared from nylon hard segments and polyether or polyester soft segments. The hard segments endow TPAE with excellent mechanical properties, while the soft segments provide the desired elasticity. Therefore, the development of TPAE as a high‐performance foam material has broad application prospects. In this work, ethylene‐vinyl acetate copolymer/polyamide‐1012 elastomer (EVA/TPAE1012) composite materials with different compositions were prepared, using ethylene‐vinyl acetate /maleic anhydride graft copolymer (EVA‐g‐MAH) as compatibilizer. Then, EVA/TPAE foamed materials were fabricated by chemical foaming method and batch foaming process, with azodicarbonamide as blowing agent. The resulting composite foams were tested in terms of density, cell properties hardness, resilience, compression recovery, and mechanical strength. The EVA/TPAE1012 foam has a low density (0.14 g cm−3), small cell size (approximately 62.1 μm), and a high cell density (3.08 × 107 cells cm−3). Compared with pure EVA foam, the composite foam not only has an increase in specific strength, resilience and tearing strength, but also has good toughness, which greatly improves the resulting foams' expansion ratio and elongation at break.

Objective: Sleep-stage scoring is important for sleep-quality evaluation and the diagnosis of related diseases. In this study, an automatic sleep-stage scoring method using photoplethysmographic (PPG) signals was proposed. Approach: To construct the classification model, we extracted 14 time-domain features, 17 frequency-domain features, and 20 pulse rate variability (PRV) features along with four SpO2 features from PPG signals. An artificial neural network classifier was used to integrate the results of ten binary support vector machine classifiers and realise sleep-stage classification. Leave-one-subject-out validation was applied to evaluate our proposed model. Main results: Thirty-one subjects were enrolled in the study, in which 21 subjects were with high sleep quality (sleep efficiencies ⩾85%). Our model achieved accuracies of 57% (κ = 0.39), 62% (κ = 0.41), and 78% (κ = 0.54) for the classification of five sleep stages (wake, N1, N2, N3, and rapid eye movement (REM) sleeps), four sleep stages (wake, light, deep, and REM sleeps) and three sleep stages (wake, non-rapid eye movement (NREM), and REM sleeps), respectively. For the remaining ten subjects with poor sleep quality, the results came to 55% (κ = 0.39), 62% (κ = 0.43), and 75% (κ = 0.52). Significance: The satisfactory performance of our proposed model reveals the potential of PPG signals for sleep-stage scoring, which may contribute to automatic sleep monitoring in the home environment.