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Quantifying uncertainty in forest assessments is challenging because of the number of sources of error and the many possible approaches to quantify and propagate them. The uncertainty in allometric equations has sometimes been represented by propagating uncertainty only in the prediction of individuals, but at large scales with large numbers of trees uncertainty in model fit is more important than uncertainty in individuals. We compared four different approaches to representing model uncertainty: a formula for the confidence interval, Monte Carlo sampling of the slope and intercept of the regression, bootstrap resampling of the allometric data, and a Bayesian approach. We applied these approaches to propagating model uncertainty at four different scales of tree inventory (10 to 10,000 trees) for four study sites with varying allometry and model fit statistics, ranging from a monocultural plantation to a multi-species shrubland with multi-stemmed trees. We found that the four approaches to quantifying uncertainty in model fit were in good agreement, except that bootstrapping resulted in higher uncertainty at the site with the fewest trees in the allometric data set (48), because outliers could be represented multiple times or not at all in each sample. The uncertainty in model fit did not vary with the number of trees in the inventory to which it was applied. In contrast, the uncertainty in predicting individuals was higher than model fit uncertainty when applied to small numbers of trees, but became negligible with 10,000 trees. The importance of this uncertainty source varied with the forest type, being largest for the shrubland, where the model fit was most poor. Low uncertainties were observed where model fit was high, as was the case in the monoculture plantation and in the subtropical jungle where hundreds of trees contributed to the allometric model. In all cases, propagating uncertainty only in the prediction of individuals would underestimate allometric uncertainty. It will always be most correct to include both uncertainty in predicting individuals and uncertainty in model fit, but when large numbers of individuals are involved, as in the case of national forest inventories, the contribution of uncertainty in predicting individuals can be ignored. When the number of trees is small, as may be the case in forest manipulation studies, both sources of allometric uncertainty are likely important and should be accounted for. 

We used deep-learning-based models to automatically obtain elastic moduli from resonant ultrasound spectroscopy (RUS) spectra, which conventionally require user intervention of published analysis codes. By strategically converting theoretical RUS spectra into their modulated fingerprints and using them as a dataset to train neural network models, we obtained models that successfully predicted both elastic moduli from theoretical test spectra of an isotropic material and from a measured steel RUS spectrum with up to 9.6% missing resonances. We further trained modulated fingerprint-based models to resolve RUS spectra from yttrium–aluminum-garnet (YAG) ceramic samples with three elastic moduli. The resulting models were capable of retrieving all three elastic moduli from spectra with a maximum of 26% missing frequencies. In summary, our modulated fingerprint method is an efficient tool to transform raw spectroscopy data and train neural network models with high accuracy and resistance to spectra distortion.

 

This study aimed to explore lignin as a naturally occurring aromatic precursor for the synthesis of LIG and further fabrication of ultrasensitive strain sensors for the detection of small deformations. One-step direct laser writing (DLW) induced high quality porous graphene, so called laser induced graphene (LIG), from kraft lignin under the conditions optimized for laser power, focus distance, and lignin loading. An electrode based on the resulting LIG was facilely fabricated by transferring LIG onto an elastomeric substrate ( i.e. , Dragon Skin™). The novel LIG transfer was facilitated by spin coating followed by water lifting, leading to the full retention of porous graphene onto the elastomeric substrate. The strain sensor was shown to be highly sensitive to small human body motions and tiny deformations caused by vibrations. It had a working range of up to 14% strain with a gauge factor of 960 and showed high stability as evidenced by repetitive signals over 10 000 cycles at 4% strain. The sensor was also successfully demonstrated for detecting human speaking, breath, seismocardiography (SCG), and movement of pulse and eye. Overall, the lignin-derived LIG can serve as excellent piezoresistive materials for wearable, stretchable, and ultrasensitive strain sensors with applications in human body motion monitoring and sound-related applications. 

A suspended nanowire is used to track both the electrical and mechanical activities in cells. 

The ever-increasing demand for novel polymers with superior properties requires a deeper understanding and exploration of the chemical space. Recently, data-driven approaches to explore the chemical space for polymer design have emerged. Among them, inverse design strategies for designing polymers with specific properties have evolved to be a significant materials informatics platform by learning hidden knowledge from materials data as well as smartly navigating the chemical space in an optimized way. In this review, we first summarize the progress in the representation of polymers, a prerequisite step for the inverse design of polymers. Then, we systematically introduce three data-driven strategies implemented for the inverse design of polymers, i.e. , high-throughput virtual screening, global optimization, and generative models. Finally, we discuss the challenges and opportunities of the data-driven strategies as well as optimization algorithms employed in the inverse design of polymers.