Additional file 1 of Highly stretchable strain sensors with improved sensitivity enabled by a hybrid of carbon nanotube and graphene

Additional file 1: Figure S1. SEM image of a graphene and b MWCNT coated on a glass substrate. c–e Column charts of size statistics of graphene (c), length and diameter statistics of MWCNT (d,e). The statistical results show that the size of graphene sheet is mostly distributed around 20 μm2. In addition, the dominant length and diameter of MWCNT are mostly around 0.5-1 μm and 60-70 nm, respectively. Figure S2. Photographs and optical microscope images of a MWCNT-, b graphene-, and c MWCNT/graphene hybrid-based sensor at 0% and 550% strain. No obvious cracks are observed in the MWCNT-based sensor under 550% strain, but significant cracks are formed once the graphene is added to the sensing layer. This can be due to the easy separation of graphene under large stretching. It is noted that the size of cracks is smaller in the MWCNT/graphene-based sensor than in the graphene-based sensor, which might be due to the high aspect ratio of MWCNTs that bridge the graphene flakes. Figure S3. a Temperature change of the sensor surface for seven cycles of UV sensing. The temperature of the sensor surface increased from 24.7°C to 36.57°C owing to heat transfer from the UV lamp. b Relative current change of the MWCNT-, graphene-, and MWCNT/graphene hybrid-based sensors during the temperature increased from 23.75°C to 40°C. Table S1. Relative current change of MWCNT-, graphene-, and MWCNT/graphene hybrid-based sensors caused by the temperature changes during each UV on-off cycle shown in Fig. 4a

Results for different lightweight networks on object detection.

Results for different lightweight networks on object detection.</p

The network structure of improved MobileNetV2.

The network structure of improved MobileNetV2.</p

The original Bottleneck module and the improved Bottleneck module.

A: original Bottleneck module. B:depthwise convolution. C: improved Bottleneck module.</p

TOP-1 accuracies on on ImageNet-1K dataset.

TOP-1 accuracies on on ImageNet-1K dataset.</p

The detail network structure of improved Bottleneck module with the attention mechanism.

The detail network structure of improved Bottleneck module with the attention mechanism.</p

Object detection based on different lightweight networks.

Object detection based on different lightweight networks.</p

Results on CIFAR-100.

Results on CIFAR-100.</p

Results on IMAGENET-100.

Results on IMAGENET-100.</p

Results for different position of improved bottleneck in network.

Results for different position of improved bottleneck in network.</p