Sniffing Bacteria with a Carbon-Dot Artificial Nose

Volatile organic compounds (VOCs) detection for the identification of bacterial infections in clinical wound samples

Early detection of wound infections is critical for timely intervention and prevention of possible complications since prompt treatment can help lower pathogen spread and enhance faster healing. Early detection also helps reduce the risk of serious infections requiring extensive medical interventions or life-threatening diseases such as sepsis. Culture-based approaches currently used for bacterial identification have limited sensitivity and specificity. At the same time, they are time-consuming, resulting in delays in therapy and, therefore, having a negative impact on the treatment outcomes. Quantifying the volatile organic compounds (VOCs) released by bacteria residing in wounds is a promising, non-invasive option for detecting infections at early stages. This method allows for continuous monitoring without requiring invasive procedures, thereby reducing patient discomfort and the risk of further complications. Spectroscopy methods and sensors are the primary VOC detection and quantification approaches, but sensors are more rapid, cost-effective, non-invasive, and precise. This review highlights the significance of the early detection of wound infection to enable timely intervention and prevent complications, emphasizing the limitations of culture-based approaches. It also explores the potential of quantifying VOCs using different methods and discusses the correlation between their levels and the rate of bacterial infections in wounds. Additionally, the review evaluates current VOC-based monitoring methods for wound management, identifies gaps in the field, and advocates for further research to advance wound care and enhance patient outcomes.

Artificial intelligence in bacterial diagnostics and antimicrobial susceptibility testing: Current advances and future prospects

Recently, artificial intelligence (AI) has emerged as a transformative tool, enhancing the speed, accuracy, and scalability of bacterial diagnostics. This review explores the role of AI in revolutionizing bacterial detection and antimicrobial susceptibility testing (AST) by leveraging machine learning models, including Random Forest, Support Vector Machines (SVM), and deep learning architectures such as Convolutional Neural Networks (CNNs) and transformers. The integration of AI into these methods promises to address the current limitations of traditional techniques, offering a path toward more efficient, accessible, and reliable diagnostic solutions. In particular, AI-based approaches have demonstrated significant potential in resource-limited settings by enabling cost-effective and portable diagnostic solutions, reducing dependency on specialized infrastructure, and facilitating remote bacterial detection through smartphone-integrated platforms and telemedicine applications. This review highlights AI's transformative role in automating data analysis, minimizing human error, and delivering real-time diagnostic results, ultimately improving patient outcomes and optimizing healthcare efficiency. In addition, we not only examine the current advances in machine learning and deep learning but also review their applications in plate counting, mass spectrometry, morphology-based and motion-based microscopic detection, holographic microscopy, colorimetric and fluorescence detection, electrochemical sensors, Raman and Surface-Enhanced Raman Spectroscopy (SERS), and Atomic Force Microscopy (AFM) for bacterial diagnostics and AST. Finally, we discuss the future directions and potential advancements in AI-driven bacterial diagnostics.

Triboelectric Nanogenerator for Self-Powered Gas Sensing

With the continuous acceleration of industrialization, gas sensors are evolving to become portable, wearable and environmentally friendly. However, traditional gas sensors rely on external power supply, which severely limits their applications in various industries. As an innovative and environmentally adaptable power generation technology, triboelectric nanogenerators (TENGs) can be integrated with gas sensors to leverage the benefits of both technologies for efficient and environmentally friendly self-powered gas sensing. This paper delves into the basic principles and current research frontiers of the TENG-based self-powered gas sensor, focusing particularly on innovative applications in environmental safety monitoring, healthcare, as well as emerging fields such as food safety assurance and smart agriculture. It emphasizes the significant advantages of TENG-based self-powered gas sensor systems in promoting environmental sustainability, achieving efficient sensing at room temperature, and driving technological innovations in wearable devices. It also objectively analyzes the technical challenges, including issues related to performance enhancement, theoretical refinement, and application expansion, and provides targeted strategies and future research directions aimed at paving the way for continuous progress and widespread applications in the field of self-powered gas sensors.

Carbon dots-based fluorescent probe for detection of foodborne pathogens and its potential with microfluidics

Concern about food safety triggers demand on rapid, accurate and on-site detection of foodborne pathogens. Among various fluorescent probes for detection, carbon dots (CDs) prepared by carbonization of carbon-rich raw materials show extraordinary performance for their excellent and tailorable photoluminescence property, as well as their facilely gained specificity by surface customization and modification. CDs-based fluorescent probes play a crucial role in many pathogenic bacteria sensing systems. In addition, microfluidic technology with characteristics of portability and functional integration is expected to combine with CDs-based fluorescent probes for point-of-care testing (POCT), which can further enhance the detection property of CDs-based fluorescent probes. Here, this paper reviews CDs-based bacterial detection methods and systems, including the structural modulation of fluorescent probes and pathogenic bacteria detection mechanisms, and describes the potential of combining CDs with microfluidic technology, providing reference for the development of novel rapid detection technology for pathogenic bacteria in food.

Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array

As information acquisition terminals for artificial olfaction, chemiresistive gas sensors are often troubled by their cross-sensitivity, and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing area. Pattern recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas sensors. It is crucial to choose an appropriate pattern recognition method for enhancing data analysis, reducing errors and improving system reliability, obtaining better classification or gas concentration prediction results. In this review, we analyze the sensing mechanism of cross-sensitivity for chemiresistive gas sensors. We further examine the types, working principles, characteristics, and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing arrays. Additionally, we report, summarize, and evaluate the outstanding and novel advancements in pattern recognition methods for gas identification. At the same time, this work showcases the recent advancements in utilizing these methods for gas identification, particularly within three crucial domains: ensuring food safety, monitoring the environment, and aiding in medical diagnosis. In conclusion, this study anticipates future research prospects by considering the existing landscape and challenges. It is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications.

Recent advances and perspectives of functionalized carbon dots in bacteria sensing

Bacterial infectious diseases are severe threats to human health and increase substantial financial burdens. Nanomaterials have shown great potential in timely and accurate bacterial identification, detection, and monitoring to improve the cure rate and reduce mortality. Recently, carbon dots have been evidenced to be ideal candidates for bacterial identification and detection due to their superior physicochemical properties and biocompatibility. This review outlines the detailed recognition elements and recognition strategies with functionalized carbon dots (FCDs) for bacterial identification and detection. The advantages and limitations of different kinds of FCDs-based sensors will be critically discussed. Meanwhile, the ongoing challenges and perspectives of FCDs-based sensors for bacteria sensing are put forward.

Enzymatic Catalysis in Size and Volume Dual-Confined Space of Integrated Nanochannel-Electrodes Chip for Enhanced Impedance Detection of Salmonella

Nanochannel-based confinement effect is a fascinating signal transduction strategy for high-performance sensing, but only size confinement is focused on while other confinement effects are unexplored. Here, a highly integrated nanochannel-electrodes chip (INEC) is created and a size/volume-dual-confinement enzyme catalysis model for rapid and sensitive bacteria detection is developed. The INEC, by directly sandwiching a nanochannel chip (60 µm in thickness) in nanoporous gold layers, creates a micro-droplet-based confinement electrochemical cell (CEC). The size confinement of nanochannel promotes the urease catalysis efficiency to generate more ions, while the volume confinement of CEC significantly enriches ions by restricting diffusion. As a result, the INEC-based dual-confinement effects benefit a synergetic enhancement of the catalytic signal. A 11-times ion-strength-based impedance response is obtained within just 1 min when compared to the relevant open system. Combining this novel nanoconfinement effects with nanofiltration of INEC, a separation/signal amplification-integrated sensing strategy is further developed for Salmonella typhimurium detection. The biosensor realizes facile, rapid (<20 min), and specific signal readout with a detection limit of 9 CFU mL-1 in culturing solution, superior to most reports. This work may create a new paradigm for studying nanoconfined processes and contribute a new signal transduction technique for trace analysis application.

Recent progress on performances and mechanisms of carbon dots for gas sensing

Carbon dots (CDs), as an attractive zero-dimensional carbon nanomaterial with unique photoluminescent merits, have recently exhibited significant application potential in gas sensing as a result of their excellent optical/electronic characteristics, high chemical/thermal stability, and tunable surface states. CDs exhibit strong light absorption in the ultraviolet range and tunable photoluminescence characteristics in the visible range, which makes CDs an effective tool for optical sensing applications. Optical gas sensor based on CDs have been investigated, which generally responds to the target gas by corresponding changes in optical absorption or fluorescence. Moreover, electrical gas sensor and quartz crystal microbalance sensor whose sensing layer involves CDs have also been designed. Electrical gas sensor exhibits an increase or a decrease in electrical current, capacitance, or conductance once exposed to the target gas. Quartz crystal microbalance sensor responds to the target gas with a frequency shift. CDs greatly promote the absorption of the target gas and improve the sensitivity of both sensors. In this review, we aim to summarize different types of gas sensors involving CDs, and sensing performances of these sensors for monitoring diverse gases or vapors, as well as the mechanisms of CDs in different types of sensors. Moreover, this review provides the prospect of the potential development of CDs based gas sensors.

Waste to value transformation: Converting Carica papaya seeds into green fluorescent carbon dots for simultaneous selective detection and degradation of tetracycline hydrochloride in water

Rampant use of antibiotics has resulted in their seepage into groundwater and ultimately ending up in the food chain, causing antimicrobial resistance. To address this issue, it is imperative to not only quantitatively detect but eliminate them from water. An eco-friendly, one-step microwave-induced pyrolysis of waste papaya seeds (PS) with ethylenediamine (EDA) for just 5min gave green fluorescent nitrogen-doped carbon dots (PS-CDs), which are capable of detecting and photocatalytically degrading TC. The fluorescence properties of PS-CDs displayed that it has high sensitivity and selectivity towards sensing of TC with a detection limit as low as 120 nM. Also, the method gave satisfactory recovery results when extrapolated to determine TC in spiked milk, orange juice, tap water, and honey samples. On the other hand, PS-CDs alone potentially function as an efficient photocatalyst for the degradation of TC. PS-CDs' dual functionality provides an effectual method for the simultaneous detection and degradation of TC by a single nanoprobe.

Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics

The world today is witnessing the significant role and huge demand for molecular detection and screening in healthcare and medical diagnosis, especially during the outbreak of COVID-19. Surface-enhanced spectroscopy techniques, including Surface-Enhanced Raman Scattering (SERS) and Infrared Absorption (SEIRA), provide lattice and molecular vibrational fingerprint information which is directly linked to the molecular constituents, chemical bonds, and configuration. These properties make them an unambiguous, nondestructive, and label-free toolkit for molecular diagnostics and screening. However, new issues in molecular diagnostics, such as increasing molecular species, faster spread of viruses, and higher requirements for detection accuracy and sensitivity, have brought great challenges to detection technology. Advancements in artificial intelligence and machine learning (ML) techniques show promising potential in empowering SERS and SEIRA with rapid analysis and automatic data processing to jointly tackle the challenge. This review introduces the combination of ML and SERS/SEIRA by investigating how ML algorithms can be beneficial to SERS/SEIRA, discussing the general process of combining ML and SEIRA/SERS, highlighting the molecular diagnostics and screening applications based on ML-combined SEIRA/SERS, and providing perspectives on the future development of ML-integrated SEIRA/SERS. In general, this review offers comprehensive knowledge about the recent advances and the future outlook regarding ML-integrated SEIRA/SERS for molecular diagnostics and screening.

A Lab-Made E-Nose-MOS Device for Assessing the Bacterial Growth in a Solid Culture Medium

The detection and level assessment of microorganisms is a practical quality/contamination indicator of food and water samples. Conventional analytical procedures (e.g., culture methods, immunological techniques, and polymerase chain reactions), while accurate and widely used, are time-consuming, costly, and generate a large amount of waste. Electronic noses (E-noses), combined with chemometrics, provide a direct, green, and non-invasive assessment of the volatile fraction without the need for sample pre-treatments. The unique olfactory fingerprint generated during each microorganism's growth can be a vehicle for its detection using gas sensors. A lab-made E-nose, comprising metal oxide semiconductor sensors was applied, to analyze solid medium containing Gram-positive (Enterococcus faecalis and Staphylococcus aureus) or Gram-negative (Escherichia coli and Pseudomonas aeruginosa) bacteria. The electrical-resistance signals generated by the E-nose coupled with linear discriminant analysis allowed the discrimination of the four bacteria (90% of correct classifications for leave-one-out cross-validation). Furthermore, multiple linear regression models were also established allowing quantifying the number of colony-forming units (CFU) (0.9428 ≤ R² ≤ 0.9946), with maximum root mean square errors lower than 4 CFU. Overall, the E-nose showed to be a powerful qualitative-quantitative device for bacteria preliminary analysis, being envisaged its possible application in solid food matrices.

The Transformation of 0-D Carbon Dots into 1-, 2- and 3-D Carbon Allotropes: A Minireview

Carbon dots (CDs) represent a relatively new type of carbon allotrope with a 0-D structure and with nanoparticle sizes < 10 nm. A large number of research articles have been published on the synthesis, characteristics, mechanisms and applications of this carbon allotrope. Many of these articles have also shown that CDs can be synthesized from “bottom-up” and “top-down” methods. The “top-down” methods are dominated by the breaking down of large carbon structures such as fullerene, graphene, carbon black and carbon nanotubes into the CDs. What is less known is that CDs also have the potential to be used as carbon substrates for the synthesis of larger carbon structures such as 1-D carbon nanotubes, 2-D or 3-D graphene-based nanosheets and 3-D porous carbon frameworks. Herein, we present a review of the synthesis strategies used to convert the 0-D carbons into these higher-dimensional carbons. The methods involve the use of catalysts or thermal procedures to generate the larger structures. The surface functional groups on the CDs, typically containing nitrogen and oxygen, appear to be important in the process of creating the larger carbon structures that typically are formed via the generation of covalent bonds. The CD building blocks can also ‘aggregate’ to form so called supra-CDs. The mechanism for the formation of the structures made from CDs, the physical properties of the CDs and their applications (for example in energy devices and as reagents for use in medicinal fields) will also be discussed. We hope that this review will serve to provide valuable insights into this area of CD research and a novel viewpoint on the exploration of CDs.