Printed gas sensors

Dai, Jie; Ogbeide, Osarenkhoe; Macadam, Nasiruddin; Sun, Qian; Yu, Wen-Bei; Li, Yu; Su, Bao‐Lian; Hasan, Tawfique; Huang, Xiao; Huang, Wei

doi:10.1039/c9cs00459a

Cited by 259 publications

(221 citation statements)

References 364 publications

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“…[ 7–9 ] Inspired by nature, artificial scent screening systems resembling the mammalian system (known as E‐noses, opto‐noses for olfactory, and E‐tongue for taste) have been developed for disease diagnosis [ 10 ] and detection of environmental contaminants, [ 11,12 ] explosives, [ 13 ] food, and drugs. [ 14–17 ] Cross‐reactive sensing in these artificial systems is achieved using cross‐reactive metal oxide [ 18,19 ] or colorimetric sensor arrays [ 20–23 ] that interact differentially with target molecules to generate a fingerprint pattern. Traditional statistical methods such as linear discriminant analysis (LDA), principal component analysis (PCA) and hierarchical cluster analysis (HCA) are typically used to analyze the fingerprints.…”

Section: Figurementioning

confidence: 99%

Portable Food‐Freshness Prediction Platform Based on Colorimetric Barcode Combinatorics and Deep Convolutional Neural Networks

et al. 2020

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Artificial scent screening systems (known as electronic noses, E‐noses) have been researched extensively. A portable, automatic, and accurate, real‐time E‐nose requires both robust cross‐reactive sensing and fingerprint pattern recognition. Few E‐noses have been commercialized because they suffer from either sensing or pattern‐recognition issues. Here, cross‐reactive colorimetric barcode combinatorics and deep convolutional neural networks (DCNNs) are combined to form a system for monitoring meat freshness that concurrently provides scent fingerprint and fingerprint recognition. The barcodes—comprising 20 different types of porous nanocomposites of chitosan, dye, and cellulose acetate—form scent fingerprints that are identifiable by DCNN. A fully supervised DCNN trained using 3475 labeled barcode images predicts meat freshness with an overall accuracy of 98.5%. Incorporating DCNN into a smartphone application forms a simple platform for rapid barcode scanning and identification of food freshness in real time. The system is fast, accurate, and non‐destructive, enabling consumers and all stakeholders in the food supply chain to monitor food freshness.

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Section: Figurementioning

confidence: 99%

Portable Food‐Freshness Prediction Platform Based on Colorimetric Barcode Combinatorics and Deep Convolutional Neural Networks

et al. 2020

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“…The sector for printed electronics has seen huge growth in the last few decades. Devices such as thin‐film transistors, [1] radio‐frequency identification devices, [2] light emitting diodes (LED) displays, [3] sensors, [4] batteries, [5] solar cells, [6] have become paramount in society and as such, industry leaders are looking for innovative approaches to make their processes more material‐, time‐ and cost‐efficient. One such method of achieving these targets is to deposit the conductive circuits found in these devices using metal containing inks.…”

Section: Introductionmentioning

confidence: 99%

MODs vs. NPs: Vying for the Future of Printed Electronics

Douglas

Mrig

Knapp

2021

Chemistry A European J

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This Minireview compares two distinct ink types, namely metal-organic decomposition (MOD) and nanoparticle (NP) formulations, for use in the printing of some of the most conductive elements: silver, copper and aluminium. Printing of highly conductive features has found purpose across a broad array of electronics and as processing times and temperatures reduce, the avenues of application expand to low-cost flexible substrates, materials for wearable devices and beyond. Printing techniques such as screen, aerosol jet and inkjet printing are scalable, solutionbased processes that historically have employed NP formula-tions to achieve low resistivity coatings printed at high resolution. Since the turn of the century, the rise in MOD inks has vastly extended the range of potentially applicable compounds that can be printed, whilst simultaneously addressing shelf life and sintering issues. A brief introduction to the field and requirements of an ink will be presented followed by a detailed discussion of a wide array of synthetic routes to both MOD and NP inks. Unindustrialized materials will be discussed, with the challenges and outlook considered for the market leaders: silver and copper, in comparison with the emerging field of aluminium inks.

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“…Gas sensors play an important role in our daily life for detecting various gases which have a negative effect on the environment and human safety [1,2]. The applications of such sensors include gas pollutants, evaluation of food safety, medical approaches for recognizing illness at the initial state, human safety (flammable and explosive gases for mines and indoor applications), and the automotive and chemical industries [3][4][5][6][7]. In this field, developing high performance sensors which provide reliable data with high sensitivity is the key goal for many recent research studies [8,9].…”

Section: Introductionmentioning

confidence: 99%

Silicon Nanowires for Gas Sensing: A Review

et al. 2020

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The unique electronic properties of semiconductor nanowires, in particular silicon nanowires (SiNWs), are attractive for the label-free, real-time, and sensitive detection of various gases. Therefore, over the past two decades, extensive efforts have been made to study the gas sensing function of NWs. This review article presents the recent developments related to the applications of SiNWs for gas sensing. The content begins with the two basic synthesis approaches (top-down and bottom-up) whereby the advantages and disadvantages of each approach have been discussed. Afterwards, the basic sensing mechanism of SiNWs for both resistor and field effect transistor designs have been briefly described whereby the sensitivity and selectivity to gases after different functionalization methods have been further presented. In the final words, the challenges and future opportunities of SiNWs for gas sensing have been discussed.

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Printed gas sensors

Abstract: This review presents the recent development of printed gas sensors based on functional inks.

Cited by 259 publications

References 364 publications

Portable Food‐Freshness Prediction Platform Based on Colorimetric Barcode Combinatorics and Deep Convolutional Neural Networks

Portable Food‐Freshness Prediction Platform Based on Colorimetric Barcode Combinatorics and Deep Convolutional Neural Networks

MODs vs. NPs: Vying for the Future of Printed Electronics

Silicon Nanowires for Gas Sensing: A Review

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