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Selective detection and quantification of biomarkers related to human diseases are essential for preventive healthcare. Surface-enhanced Raman scattering (SERS) spectroscopy is a powerful analytical tool offering high sensitivity. However, the success of this promising analytical tool relies on the ability to effectively fabricate SERS substrate. Herein we have demonstrated a plasmonic paper-based flexible substrate (PPFS) for SERS sensing. In situ growth of silver nanostructures (AgNS) on the paper-based substrate was achieved by using a simple one-step silver mirror reaction (SMR). FESEM and TEM results depicts that the increasing silver ion content influences the morphology (growth of multifacets), as well as size of AgNS. Further, the PPFS substrate was tested with Rhodamine-6G (Rh-6G) dye and an attomole sensitivity with a LOD of 4.54 x 10-18 M was achieved. Further, two biomarkers, lactic acid (LA) and uric acid (UA) were detected on the PPFS substrate, with μM and pM sensitivity, having LOD values of 0.6 x 10-6 and 0.3 x 10-12 M respectively. Above detection levels for UA on PPFS is two orders better than reported values, whereas for LA it is comparable with reported substrates. Finally, UA, LA and their mixtures were tested on PPFS and results compared with commercial substrate. The performance of PPFS were found better in all cases, thus, multifaceted AgNS paper based PPFS offers the potential to be used as a biosensor for detection of various biomarkers from body fluids, responsible for the detection of the critical disease for preventive health care.

Origami architecture (OA) is a fascinating papercraft that involves only a piece of paper with cuts and folds. Interesting geometric structures ‘pop up’ when the paper is opened. However, manually designing such a physically valid 2D paper pop-up plan is challenging since fold lines must jointly satisfy hard spatial constraints. Existing works on automatic OA-style paper pop-up design all focused on how to generate a pop-up structure that approximates a given target 3D model. This paper presents the first OA-style paper pop-up design framework that takes 2D images instead of 3D models as input. Our work is inspired by the fact that artists often use 2D profiles to guide the design process, thus benefited from the high availability of 2D image resources. Due to the lack of 3D geometry information, we perform novel theoretic analysis to ensure the foldability and stability of the resultant design. Based on a novel graph representation of the paper pop-up plan, we further propose a practical optimization algorithm via mixed-integer programming that jointly optimizes the topology and geometry of the 2D plan. We also allow the user to interactively explore the design space by specifying constraints on fold lines. Finally, we evaluate our framework on various images with interesting 2D shapes. Experiments and comparisons exhibit both the efficacy and efficiency of our framework.

Estimation of pH is vital to assess the biochemical and biological processes in a wide variety of applications ranging from water to soil, health, and environment monitoring. This work reports a screen-printed flexible and disposable pH sensor using the impedimetric method. The pH sensor was fabricated by screen printing Graphene-Carbon modified Zinc Oxide based active layer on a paper substrate and shows nearly three orders of change in impedance magnitude in the pH range of 2 – 9. The sensor was carefully designed using COMSOL® Multiphysics software to understand the influence of electrode geometry and the electrical potential developed across the structure. The developed sensor was used for pH monitoring of soil and exhibited high sensitivity of 5.27 kΩ /pH (2-8) with a correlation coefficient (R 2 ) of 0.99. Finally, an IoT-enabled smart pH detection system was implemented for continuous pH monitoring for potential application in digital agriculture. The outcome demonstrates that the presented flexible and disposable pH sensor could open new opportunities for monitoring of water, product process, human health, and chemical (or bio) reactions even using small volumes of samples.

A precoding technique based on polar codes and probabilistic shaping is introduced for dirty paper coding. Two variants of the precoding use multi-level shaping and sign-bit shaping in one dimension. The decoder uses multi-stage successive-cancellation list decoding with list-passing across the bit levels. The approach achieves approximately the same frame error rates as polar codes with multi-level shaping over standard additive white Gaussian noise channels at a block length of 256 symbols and with different amplitude shift keying (ASK) constellations.