A powerful replica molding methodology to transfer on-demand functional topographies to the surface of bacterial cellulose nanofiber textures is presented. With this method, termed guided assembly-based biolithography (GAB), a surface-structured polydimethylsiloxane (PDMS) mold is introduced at the gas-liquid interface of an Acetobacter xylinum culture. Upon bacterial fermentation, the generated bacterial cellulose nanofibers are assembled in a three-dimensional network reproducing the geometric shape imposed by the mold. Additionally, GAB yields directional alignment of individual nanofibers and memory of the transferred geometrical features upon dehydration and rehydration of the substrates. Scanning electron and atomic force microscopy are used to establish the good fidelity of this facile and affordable method. Interaction of surface-structured bacterial cellulose substrates with human fibroblasts and keratinocytes illustrates the efficient control of cellular activities which are fundamental in skin wound healing and tissue regeneration. The deployment of surface-structured bacterial cellulose substrates in model animals as skin wound dressing or body implant further proves the high durability and low inflammatory response to the material over a period of 21 days, demonstrating beneficial effects of surface structure on skin regeneration.

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Evaluating and optimising human comfort within the built environment is challenging due to the large number of physiological, psychological and environmental variables that affect occupant comfort preference. Human perception could be helpful to capture these disparate phenomena and interpreting their impact; the challenge is collecting spatially and temporally diverse subjective feedback in a scalable way. This paper presents a methodology to collect intensive longitudinal subjective feedback of comfort-based preference using micro ecological momentary assessments on a smartwatch platform. An experiment with 30 occupants over two weeks produced 4378 field-based surveys for thermal, noise, and acoustic preference. The occupants and the spaces in which they left feedback were then clustered according to these preference tendencies. These groups were used to create different feature sets with combinations of environmental and physiological variables, for use in a multi-class classification task. These classification models were trained on a feature set that was developed from time-series attributes, environmental and near-body sensors, heart rate, and the historical preferences of both the individual and the comfort group assigned. The most accurate model had multi-class classification F1 micro scores of 64%, 80% and 86% for thermal, light, and noise preference, respectively. The discussion outlines how these models can enhance comfort preference prediction when supplementing data from installed sensors. The approach presented prompts reflection on how the building analysis community evaluates, controls, and designs indoor environments through balancing the measurement of variables with occupant preferences in an intensive longitudinal way.

Labelled human comfort data can be a valuable resource in optimising the built environment, and improving the wellbeing of individual occupants. The acquisition of labelled data however remains a challenge. This paper presents a methodology for the collection of in-situ occupant feedback data using a Fitbit smartwatch. The clock-face application cozie can be downloaded free-of-charge on the Fitbit store and tailored to fit a range of occupant comfort related experiments. In the initial trial of the app, fifteen users were given a smartwatch for one month and were prompted to give feedback on their thermal preferences. In one month, with minimal administrative overhead, 1460 labelled responses were collected. This paper demonstrates how these large data sets of human feedback can be analysed to reveal a range of results from building anomalies, occupant behaviour, occupant personality clustering, and general feedback related to the building. The paper also discusses limitations in the approach and the next phase of design of the platform.

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