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Detection of mycotoxins, especially aflatoxin M1 (AFM1), in milk is crucial to be able to guarantee food quality and safety. In recent years, biosensors have been emerging as a fast, reliable and low-cost technique for the detection of this toxin. In this work, flexible biosensors were fabricated using dispense-printed electrodes, which were functionalized with single-walled carbon nanotubes (SWCNTs) and subsequently coated with specific antibodies to improve their sensitivity. Next, the immunosensor was tested for the detection of AFM1 in buffer solution and a spiked milk sample using a chronoamperometric technique. Results showed that the working range of the sensors was 0.01 µg/L at minimum and 1 µg/L at maximum in both buffer and spiked milk. The lower limit of detection of the SWCNT-functionalized sensor was 0.02 µg/L, which indicates an improved sensitivity compared to the sensors reported so far. The sensitivity and detection range were in accordance with the limitation values imposed by regulations on milk and its products. Therefore, considering the low fabrication cost, the ease of operation, and the rapid read-out, the use of this sensor could contribute to safeguarding consumers’ health.

Strawberry is one of the most popular fruits in the market. To meet the demanding consumer and market quality standards, there is a strong need for an on-site, accurate and reliable grading system during the whole harvesting process. In this work, a total of 923 strawberry fruit were measured directly on-plant at different ripening stages by means of bioimpedance data, collected at frequencies between 20 Hz and 300 kHz. The fruit batch was then splitted in 2 classes (i.e. ripe and unripe) based on surface color data. Starting from these data, six of the most commonly used supervised machine learning classification techniques, i.e. Logistic Regression (LR), Binary Decision Trees (DT), Naive Bayes Classifiers (NBC), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Multi-Layer Perceptron Networks (MLP), were employed, optimized, tested and compared in view of their performance in predicting the strawberry fruit ripening stage. Such models were trained to develop a complete feature selection and optimization pipeline, not yet available for bioimpedance data analysis of fruit. The classification results highlighted that, among all the tested methods, MLP networks had the best performances on the test set, with 0.72, 0.82 and 0.73 for the F 1 , F 0.5 and F 2 -score, respectively, and improved the training results, showing good generalization capability, adapting well to new, previously unseen data. Consequently, the MLP models, trained with bioimpedance data, are a promising alternative for real-time estimation of strawberry ripeness directly on-field, which could be a potential application technique for evaluating the harvesting time management for farmers and producers.

Tetracycline (TC) is a widely known antibiotic used worldwide to treat animals. Its residues in animal-origin foods cause adverse health effects to consumers. Low-cost and real-time measuring systems of TC in food samples are, therefore, extremely needed. In this work, a three-electrode sensitive and label-free sensor was developed to detect TC residues from milk and meat extract samples, using CO2 laser-induced graphene (LIG) electrodes modified with gold nanoparticles (AuNPs) and a molecularly imprinted polymer (MIP) used as a synthetic biorecognition element. LIG was patterned on a polyimide (PI) substrate, reaching a minimum sheet resistance (Rsh) of 17.27 ± 1.04 Ω/sq. The o-phenylenediamine (oPD) monomer and TC template were electropolymerized on the surface of the LIG working electrode to form the MIP. Surface morphology and electrochemical techniques were used to characterize the formation of LIG and to confirm each modification step. The sensitivity of the sensor was evaluated by differential pulse voltammetry (DPV), leading to a limit of detection (LOD) of 0.32 nM, 0.85 nM, and 0.80 nM in buffer, milk, and meat extract samples, respectively, with a working range of 5 nM to 500 nM and a linear response range between 10 nM to 300 nM. The sensor showed good LOD (0.32 nM), reproducibility, and stability, and it can be used as an alternative system to detect TC from animal-origin food products.

We present a gas sensitive thin-film transistor (TFT) based on an amorphous Indium–Gallium–Zinc–Oxide (a-IGZO) semiconductor as the sensing layer, which is fabricated on a free-standing flexible polyimide foil. The photo-induced sensor response to NO2 gas at room temperature and the cross-sensitivity to humidity are investigated. We combine the advantages of a transistor based sensor with flexible electronics technology to demonstrate the first flexible a-IGZO based gas sensitive TFT. Since flexible plastic substrates prohibit the use of high operating temperatures, the charge generation is promoted with the help of UV-light absorption, which ultimately triggers the reversible chemical reaction with the trace gas. Furthermore, the device fabrication process flow can be directly implemented in standard TFT technology, allowing for the parallel integration of the sensor and analog or logical circuits.

Remote communication in small-scale robotics offers a powerful way to enhance their capabilities, introducing options for state monitoring, multi-agent collaboration, and autonomous operation. Integrating common remote communication tools, such as antennas, into microrobots is challenging with conventional design and manufacturing techniques. We propose a concept that integrates shape-reconfigurable soft microrobots with flexible electronics, leveraging their elastic mechanical properties to enable remote communication. This approach, based on photolithography processes, is scalable and adaptable to various sensing applications. As a proof of concept, we present a microrobot, which integrates a thermoresponsive magnetic hydrogel, an anisotropic support structure, and a flexible dipole antenna into a cohesive three-layered design. The microrobot can morph from a helical shape at low-temperatures to a planar shape at high-temperatures. This shape transformation can be remotely detected by external radio communication receivers, enabling shape-state recognition and environmental temperature sensing. Furthermore, we show that the collective behavior of multiple microrobots enhances the recognition performance by amplifying the signal. The concept represents a significant advancement in co-engineering smart materials and flexible electronics, illustrating an approach of microrobotic embodied intelligence by integrating environmental monitoring, magnetic navigation, and remote signaling. Real-time recognition of microrobot state remains challenging due to their small size and imaging limitations. The authors integrate flexible RF antennas into shape-reconfigurable soft microrobots, enabling remote detection of shape transformations.

Heterocyclic amine histamine is a well-known foodborne toxicant (mostly linked to “scombroid poisoning”) synthesized from the microbial decarboxylation of amino acid histidine. In this work, we report the fabrication of a flexible screen-printed immunosensor based on a silver electrode coated with single-walled carbon nanotubes (SWCNTs) for the detection of histamine directly in fish samples. Biosensors were realized by first spray depositing SWCNTs on the working electrodes and by subsequently treating them with oxygen plasma to reduce the unwanted effects related to their hydrophobicity. Next, anti-histamine antibodies were directly immobilized on the treated SWCNTs. Histamine was detected using the typical reaction of histamine and histamine-labeled with horseradish peroxidase (HRP) competing to bind with anti-histamine antibodies. The developed immunosensor shows a wide linear detection range from 0.005 to 50 ng/mL for histamine samples, with a coefficient of determination as high as 98.05%. Average recoveries in fish samples were observed from 96.00% to 104.7%. The biosensor also shows good selectivity (less than 3% relative response for cadaverine, putrescine, and tyramine), reproducibility, mechanical and time stability, being a promising analytical tool for the analysis of histamine, as well as of other food hazards.

Aptamers are chemically synthesized single-stranded DNA or RNA oligonucleotides widely used nowadays in sensors and nanoscale devices as highly sensitive biorecognition elements. With proper design, aptamers are able to bind to a specific target molecule with high selectivity. To date, the systematic evolution of ligands by exponential enrichment (SELEX) process is employed to isolate aptamers. Nevertheless, this method requires complex and time-consuming procedures. In silico methods comprising machine learning models have been recently proposed to reduce the time and cost of aptamer design. In this work, we present a new in silico approach allowing the generation of highly sensitive and selective RNA aptamers towards a specific target, here represented by ammonium dissolved in water. By using machine learning and bioinformatics tools, a rational design of aptamers is demonstrated. This “smart” SELEX method is experimentally proved by choosing the best five aptamer candidates obtained from the design process and applying them as functional elements in an electrochemical sensor to detect, as the target molecule, ammonium at different concentrations. We observed that the use of five different aptamers leads to a significant difference in the sensor’s response. This can be explained by considering the aptamers’ conformational change due to their interaction with the target molecule. We studied these conformational changes using a molecular dynamics simulation and suggested a possible explanation of the experimental observations. Finally, electrochemical measurements exposing the same sensors to different molecules were used to confirm the high selectivity of the designed aptamers. The proposed in silico SELEX approach can potentially reduce the cost and the time needed to identify the aptamers and potentially be applied to any target molecule.

One of the major challenges to the widespread adoption of plasmonic and nano-optical devices in real-life applications is the difficulty to mass-fabricate nano-optical antennas in parallel and reproducible fashion, and the capability to precisely place nanoantennas into devices with nanometer-scale precision. In this study, we present a solution to this challenge using the state-of-the-art ultraviolet nanoimprint lithography (UV-NIL) to fabricate functional optical transformers onto the core of an optical fiber in a single step, mimicking the ‘campanile’ near-field probes. Imprinted probes were fabricated using a custom-built imprinter tool with co-axial alignment capability with sub <100 nm position accuracy, followed by a metallization step. Scanning electron micrographs confirm high imprint fidelity and precision with a thin residual layer to facilitate efficient optical coupling between the fiber and the imprinted optical transformer. The imprinted optical transformer probe was used in an actual NSOM measurement performing hyperspectral photoluminescence mapping of standard fluorescent beads. The calibration scans confirmed that imprinted probes enable sub-diffraction limited imaging with a spatial resolution consistent with the gap size. This novel nano-fabrication approach promises a low-cost, high-throughput, and reproducible manufacturing of advanced nano-optical devices.

Furaneol is a widely used flavoring agent, which can be naturally found in different products, such as strawberries or thermally processed foods. This is why it is extremely important to detect furaneol in the food industry using ultra-sensitive, stable, and selective sensors. In this context, electrochemical biosensors are particularly attractive as they provide a cheap and reliable alternative measurement device. Carbon nanotubes (CNTs) and silver nanoparticles (AgNPs) have been extensively investigated as suitable materials to effectively increase the sensitivity of the biosensors. However, a comparison of the performance of biosensors employing CNTs and AgNPs is still missing. Herein, the effect of CNTs and AgNPs on the biosensor performance has been thoughtfully analyzed. Therefore, disposable flexible and screen printed electrochemical aptasensor modified with CNTs (CNT-ME), or AgNPs (AgNP-ME) have been developed. Under optimized conditions, CNT-MEs showed better performance compared to AgNP-ME, yielding a linear range of detection over a dynamic concentration range of 1 fM–35 μM and 2 pM–200 nM, respectively, as well as high selectivity towards furaneol. Finally, our aptasensor was tested in a real sample (strawberry) and validated with high-performance liquid chromatography (HPLC), showing that it could find an application in the food industry.

Stabilization of silicon micro-resonators is a key requirement for their inclusion in larger photonic integrated circuits. In particular, thermal refractive index shift in non-linear applications can detune devices from their optimal working point. A cavity stabilization scheme using a micro-processor-based feedback control loop is presented based on a local thermal heater element on-chip. Using this method, a silicon π -phase shifted grating with a cavity Q-factor of 40k is demonstrated to operate over an ambient temperature detuning range of 40 ∘ C and injection wavelength range of 1.5 nm, nearly 3 orders of magnitude greater than the resonant cavity linewidth.