Firefighting protective clothing is an essential equipment that can protect firefighters from burn injuries during the firefighting process. However, it is still a challenge to detect the damage of firefighting protective clothing at an early stage when firefighters are exposed to excessively high temperature in fire cases. Herein, an ultralight self-powered fire alarm electronic textile (SFA e-textile) based on conductive aerogel fiber that comprises calcium alginate (CA), Fe3O4 nanoparticles (Fe3O4 NPs), and silver nanowires (Ag NWs) was developed, which achieved ultrasensitive temperature monitoring and energy harvesting in firefighting clothing. The resulting SFA e-textile was integrated into firefighting protective clothing to realize wide-range temperature sensing at 100–400 °C and repeatable fire warning capability, which could timely transmit an alarm signal to the wearer before the firefighting protective clothing malfunctioned in extreme fire environments. In addition, a self-powered fire self-rescue location system was further established based on the SFA e-textile that can help rescuers search and rescue trapped firefighters in fire cases. The power in the self-powered fire location system was offered by an SFA e-textile-based triboelectric nanogenerator (TENG). This work provided a useful design strategy for the preparation of ultralight wearable temperature-monitoring SFA e-textile used in firefighting protective clothing.

Pectins are negatively charged polysaccharides employed as stabilizers in acidified milk dispersions, where caseins aggregate because of the low pH and serum separation needs to be prevented. The objective of this research was to study the effect of charge on the stabilizing functionality of the polysaccharide in acid milk drinks. Unstandardized pectins with various charges (as degree of esterification, DE) as well as soybean soluble polysaccharide (SSPS) were tested for their stabilizing behavior as a function of pH and concentration. Skim milk was acidified by glucono-delta-lactone and then homogenized in the presence of polysaccharide at different pH values (in the range from 4.2 to 3.0). Measurements of particle size distribution demonstrated that pectins with a DE of 71.4, 68.6, and 67.4 stabilized milk at pH > 4.0. Pectins with a lower DE (63.9%) needed a higher concentration (0.4%) at the same pH to show a monomodal distribution of particle sizes. Pectins with lower DE (<50%) did not stabilize the dispersions. Although this difference in behavior was attributed mainly to the pectin charge, the efficiency in stabilizing the casein dispersion decreased with decreasing pectin size. For example, the high methoxyl pectin (HMP) with 63.9 DE was smaller in size than the HMPs with a higher charge. Pectins showed a pH-dependent stabilization effect, as at pH < 4.0 the dispersions contained aggregates. When SSPS was used to stabilize acid milk, at pH < 4.0, it showed a better stabilization behavior than HMP. When SSPS and pectin were used in combination, the particle size distribution of the acid milk dispersion was pH-dependent, and results were similar to those for samples containing pectin alone. This suggested that in the mixture, pectin dominated the behavior over SSPS, even when an excess of SSPS was added to the dispersions before homogenization.

Attention-deficit/hyperactivity disorder (ADHD) affects 5% of children world-wide. Of these, two-thirds continue to have impairing symptoms of ADHD into adulthood. Although a large literature implicates structural brain differences of the disorder, it is not clear if adults with ADHD have similar neuroanatomical differences as those seen in children with recent reports from the large ENIGMA-ADHD consortium finding structural differences for children but not for adults. This paper uses deep learning neural network classification models to determine if there are neuroanatomical changes in the brains of children with ADHD that are also observed for adult ADHD, and vice versa. We found that structural MRI data can significantly separate ADHD from control participants for both children and adults. Consistent with the prior reports from ENIGMA-ADHD, prediction performance and effect sizes were better for the child than the adult samples. The model trained on adult samples significantly predicted ADHD in the child sample, suggesting that our model learned anatomical features that are common to ADHD in childhood and adulthood. These results support the continuity of ADHD’s brain differences from childhood to adulthood. In addition, our work demonstrates a novel use of neural network classification models to test hypotheses about developmental continuity.

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