Tiny Devices Research Articles

A microscale soft lithium-ion battery for tissue stimulation

AbstractAdvances in the development of tiny devices with sizes below a few cubic millimeters require a corresponding decrease in the volume of driving power sources. To be minimally invasive, prospective power sources in biomedical devices must be fabricated from soft materials. Previous endeavors with droplet-based devices have produced promising miniature power sources; however, a droplet-based rechargeable battery has remained out of reach. Here we report a microscale soft flexible lithium-ion droplet battery (LiDB) based on the lipid-supported assembly of droplets constructed from a biocompatible silk hydrogel. Capabilities such as triggerable activation, biocompatibility and biodegradability and high capacity are demonstrated. We have used the LiDB to power the electrophoretic translocation of charged molecules between synthetic cells and to mediate the defibrillation and pacing of ex vivo mouse hearts. By the inclusion of magnetic particles to enable propulsion, the LiDB can function as a mobile energy courier. Our tiny versatile battery will thereby enable a variety of biomedical applications.

RFID Occupied Advanced Framework for Specialized Vehicle Access, Operating System, and Wireless Data Transmission Without Human Intervention

RFID tags are tiny electronic devices that include a distinctive code that can be detected by a reader that supports RFID. The string of characters is used for recognizing and monitoring the object to which the tag is connected. RFID tags interact with RFID readers using radio frequency. While the tag comes into close proximity to the scanner, that device sends a radio signal that triggers the tag. The tag then responds by sending its unique identity. RFID pinpointing ability is verified experimentally in two different techniques, one utilizing radar and the other a photosensitive transition. The initial phase is intended to test if the continuously changing location provided from RFID tags equals the precise position recorded by sonar. It is also excellent for getting into parking lots, private spaces, professional parking spots, and offices quickly and easily. The implementation of RFID will bring a revolutionary transformation in the authorization, administration, correspondence, computerization, and security systems for specialized vehicle Access, Operating System, and Wireless Data Transmission without Human Intervention using an internet based wireless communication system. RFID vehicular registration is an authentication system which employs RFID readers to give authorization for automobiles. The technique normally consists of installing a reader with RFID technology at the entrance/exit entrance to an enclosed or a limited region. RFID scanning can deliver efficient techniques for transportation administration, making the process straightforward, dependable, as well as secure.

Polysaccharide Based Self-Driven Tubular Micro/Nanomotors as a Comprehensive Platform for Quercetin Loading and Anti-inflammatory Function.

Quercetin (QR) is a natural flavonoid with strong anti-inflammatory properties, but it suffers from poor water solubility and bioavailability. Micro/nanomotors (NMs) are tiny devices that convert external energy or chemical fuels into an autonomous motion. They are characterized by their small size, rapid movement, and self-assembly capabilities, which can enhance the delivery of bioactive ingredients. The study synthesized natural polysaccharide-based nanotubes (NTs) using a layer-by-layer self-assembly method and combined with urease (Ure), glucose oxidase (GOx), and Fe3O4 to create three types of NMs. These NMs were well-dispersed and biocompatible. In vitro experiments showed that NMs-Fe3O4 has excellent photothermal conversion properties and potential for use in photothermal therapy. Cellular inflammation model results demonstrated that QR-loaded NMs were not only structurally stable but also improved bioavailability and effectively inhibited the release of inflammatory mediators such as IL-1β and IL-6, providing a safe and advanced carrier system for the effective use of bioactive components in food.

Research on Improved Algorithms for Cone Bucket Detection in Formula Unmanned Competition.

The model network based on YOLOv8 for detecting race cones and buckets in the Formula Unmanned Competition for Chinese university students needs help with problems with complex structure, redundant number of parameters, and computation, significantly affecting detection efficiency. A lightweight detection model based on YOLOv8 is proposed to address these problems. The model includes improving the backbone network, neck network, and detection head, as well as introducing knowledge distillation and other techniques to construct a lightweight model. The specific improvements are as follows: firstly, the backbone network for extracting features is improved by introducing the ADown module in YOLOv9 to replace the convolution module used for downsampling in the YOLOv8 network, and secondly, the FasterBlock in FasterNet network was introduced to replace the fusion module in YOLOv8 C2f, and then the self-developed lightweight detection head was introduced to improve the detection performance while achieving lightweight. Finally, the detection performance was further improved by knowledge distillation. The experimental results on the public dataset FSACOCO show that the improved model's accuracy, recall, and average precision are 92.7%, 84.6%, and 91%, respectively. Compared with the original YOLOv8n detection model, the recall and average precision increase by 2.7 and 1.2 percentage points, the memory is half the original, and the model computation is 51%. The model significantly reduces the misdetection and leakage of conical buckets in real-vehicle tests and, at the same time, ensures the detection speed to satisfy the deployment requirements on tiny devices. Satisfies all the requirements for deployment of tiny devices in the race car of the China University Student Driverless Formula Competition. The improved method in this paper can be applied to conebucket detection in complex scenarios, and the improved idea can be carried over to the detection of other small targets.

Developing Innovative Secured Protocol For M Health Application In Wireless Body Area Networks

In today's technologically advanced society, cell phones and related devices are essential for managing peoples' individual demands. This is a smart device that determines and regulates how we use our free time, communicate, shop, and engage with the outside world. It still requires a lot of fixes to be effective and strong. The development of tiny devices, or sensors, gave mHEALTH greater adaptability and a fresh perspective. The majority of currently available mobile applications are widely accessible by users, who can use them to take vital signs and evaluate their own physical state. In an effort to increase public acceptability, some mobile companies have developed health applications for smartphones. People use these programs to monitor changes in bodily parameters such as body temperature, heart rate, blood pressure, and pulse rate in order to assess their physical conditions. Using the MSE-BAN architecture, this study has created a stable mHEALTH architecture. The scheduling plan for the study project was created by taking the importance of the essential signals into account. The public health care system that has been suggested can shorten hospital stays and enable clinical staff to continuously monitor patients.

On-device training of artificial intelligence models on microcontrollers

<p><span>Numerous studies are currently training artificial intelligence (AI) models on tiny devices constrained by computing power and memory limitations by implementing model optimization algorithms. The question arises whether implementing traditional AI models directly on small devices like micro-controller units (MCUs) is feasible. In this study, a library has been developed to train and predict the artificial neural network (ANN) model on common MCUs. The evaluation results on the regression problem indicate that, despite the extensive training time, when combined with multitasking programming on multi-core MCUs, the training does not adversely affect the system's execution. This research contributes an additional solution that enables the direct construction of ANN models on MCU systems with limited resources.</span></p>

A Bird’s Eye View of Nanorobotics and Assembly Automations: A Revolutionary Convergence

Over the past few decades, technology has advanced quickly, leading to amazing advances in a wide range of sectors. A new and exciting area called nanorobotics has emerged from the combination of nanotechnology and robotics. The development of tiny devices with nanoscale performance is the goal of this field. Among these, assembly automation and nanorobotics stand out as cutting-edge fields with enormous promise to revolutionize business, healthcare, and daily living. The development and control of robots at the nanoscale, usually between 1 and 100 nanometers, is known as nanorobotics. Conversely, assembly automations describe the employment of robots and automated systems to build things with little to no human involvement. Due to the growing need for accuracy and efficiency in industrial processes, assembly automation has advanced significantly at the same time as robotics. This article delves into the complexities of nanorobotics and assembly automation, along with their synergies, applications, problems, and prospects.

Energy Efficient Data Aggregation in Wireless Sensor Networks Using Meta Heuristic Based Feed Forward Back Propagation Neural Network Approach

Sensor nodes are low-cost, low-power, tiny devices that make up the majority of WSNs, or distributed, self-organizing systems. These sensor nodes are able to exchange, perceive, and interpret data. The sensor nodes are equipped with a wide variety of sensors, such as chemical, touch, motion, temperature, and weather sensors. Because of its adaptability, sensors are used in a variety of applications such as automation, tracking, monitoring, and surveillance. Despite the enormous number of sensor applications, WSNs continue to suffer from common challenges like as low memory, slow processing speed, and short network lifetime. The feed forward back propagation neural network mode (FFBPNN) based on meta heuristics aims to create many paths for effective data aggregation in wireless sensor networks. This model handled the process of identifying and selecting the optimum route path. The distributed sensor nodes are utilized to create the various route paths. In this research paper, data aggregation is done using meta-heuristic firefly algorithm that helped in identifying an optimal route from among the found routes. After selecting the operative ideal route choice, the data aggregation procedure practices a rank-based approach to accomplish lower latency and a better packet delivery ratio(PDR). In addition to throughput, simulation was done to improve and measure performance in terms of packet delivery ratio, energy consumption, and end-to-end latency.

Microfluidic carbon cloth-based enzymatic glucose biofuel cell for sustainably powering a microelectronic circuit *

Enhancing enzymatic microfluidic biofuel cells (EBFCs) devices has garnered significant attention due to the development of microfluidic ultra-low power energy-gathering techniques. To facilitate the ability to create microfluidic EBFCs, a carbon cloth (CC) has been considered since they are effective renewable energy sources and utilized as the ideal paper-based substitute for traditional power supplies for a variety of tiny devices due to their inherent qualities and exceptional performance. The developed microfluidic EBFC utilized glucose as a fuel, carbon cloth as the bioelectrode, Glucose oxidase for the anode, and laccase for the cathode. The maximum stable open circuit voltage of CC-EBFC was measured to be 475 mV with a peak power density of 85 µW cm−2 at 300 mV and a current density of 484 µA cm−2. The power performance of the device was improved by bovine serum albumin and a booster circuit, which was also coated and connected to the load to stabilize the performance. The novelty of the work is that using a flexible substrate of carbon cloth, with a microfluidic channel, has an added advantage in the biofuel cell. LTC3108EDE DC–DC booster was used to increase energy and attain a high charging voltage of 5 V to operate a digital watch up to 3 V. With minimal weight and flexibility; this minuscule device opens up new possibilities to sustainably power wearable and portable microelectronic devices.

P-260 In Vitro Fertilization (IVF) using magnetotactic sperm cells and their prospects for assisted in vivo reproduction

Abstract Study question Are bovine Magnetotactic Sperm Cells (MSC) able to fertilize an oocyte in-vitro and Which would be the main requirements for translating this technology to in-vivo? Summary answer IVF using highly purified MSC result in embryo development until blastocyst stage. We demonstrate different alternatives to control multiple MSC, individually and as a swarm. What is known already Medical microrobots have appeared as the new generation of drug delivery vehicles. They are tiny devices in the order of a few micrometers, able to move in biological environments by different means, physical, chemical, or biohybrid. They are functionalized to deliver targeted therapies for drugs, cells, or other payloads. In assisted reproduction, sperm-hybrid microcarriers have been pioneered by our group, and various in vitro demonstrations have been done to assist sperm cells with motion deficiencies or low count and to deliver anticancer drugs for treating, for example, cervical and ovarian cancer, but so far in-vitro fertilization has never been shown. Study design, size, duration Experimental quantitative study design. A total of 520 bovine cumulus-oocyte complexes were used for in vitro fertilization. The analyzed sample groups were using bovine frozen sperm cells, MSC, or by evaluating parthenogenetic activation. A power of 0.98 with an a-error of 0.05 was used for the IVF experiments. Sperm samples in the control as well as the micromotor groups have been analyzed for different sperm parameters. Participants/materials, setting, methods Bovine MSC are created by thawing frozen sperm cells and adding polystyrene microparticles. Different sperm parameters were evaluated to check the effects of the particles in the cells. After developing a purification method for the MSC, bovine ovaries were collected from a slaughterhouse nearby and, after in vitro maturation, were used for the IVF experiments. Ultrasound, and photoacoustic imaging setups with a set of electromagnets were employed for real-time manipulation and visualization of MSC. Main results and the role of chance Sperm viability was significantly higher in the MSC group compared to the control group 6h after formation (72.3 ± 5.0% vs 35.0 ± 4.1%), and after 24 hours, both viabilities decreased drastically to 16.6 ± 3.9% and 7.7 ± 1.7% respectively, probably a possible local antioxidant effect by the microparticles through the formation of a protein corona. In the case of motility, the MSC group presented significantly higher motility up to 6h (64.0 ± 1.5%), and in all the measured times, the motility of the MSC group was higher than the control group. Purification of the micromotor sample was performed, but an average of 63 free sperm was still on the MSC sample. IVF experiments were performed, and blastocyst development was observed in IVF control (2x106 sperm/ml), medium concentration (same concentration as MSC group), and the MSC groups (275.000 sperm/ml). No blastocyst formation was observed in the parthenogenetic, parthenogenetic + particles, and low-concentration groups (number of free sperm). The MSC group was significantly different in cleavage and blastocyst rates compared to the low concentration groups, with p-values of 0.04552 and 0.005865, respectively, which suggest that fertilization may have occurred by the micromotors instead by the free sperm in the sample. Limitations, reasons for caution More studies of these MSC in the different gynecological tissues and on embryo development are needed to ensure the safety of future human translation. In addition, developing a detection system for micromotors in deep tissue is needed to perform in vivo experiments. Wider implications of the findings MSC show an alternative to efficiently guide efficiently sperm cells in different biological environments and can be a promising alternative method for assisted fertilization, and the treatment of a variety of gynecological diseases. They can also be used as exploratory tools to learn more about sperm migration in-vivo, among others Trial registration number European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program No. 853609

Micrometric thermal electronic nose able to detect and quantify individual gases in a mixture

Recent urbanization and environmental problems urge for networks of sensors that can monitor air quality. Small, inexpensive, and smart sensors are one of the key components enabling the realization of such networks. Chemoresistive sensors are the ideal candidate, but they greatly lack selectivity, and for this reason, they are usually combined in arrays to create electronic noses, whose dimensions, however, make them not miniaturizable and cannot be integrated into portable devices. To overcome this inconvenience, we present a thermal electronic nose consisting of identical resistive sensors working at different temperatures so that the whole device is simple to make and tiny. The device contains two sensor arrays based on tin oxide nanowires decorated with Ag and Pt nanoparticles, respectively. The five sensors in each array are identical, but their response is differentiated by different temperatures locally generated by an on-chip integrated heater. This innovative approach allows the tiny array of five sensors together with the integrated heater to occupy only approximately 50 × 200 μm2 and consume only 120 μW. The tiny and portable device can estimate the concentration of H2 and NH3 in a mixture with a root mean square error of 6.1 ppm and 13.3 ppm, respectively, and it still works well after two months. The performance analysis of the double partial least squares regression used for concentration estimation also allows for feedback on which sensors are the most sensitive to which gas so that the electronic nose can be engineered for specific applications using the most suitable sensors. The size of the thermal electronic nose allows it to be integrated into portable and wearable devices, and its performance makes it suitable for any gas detection application. For example, a smartphone with an integrated sensor could carry out breath analysis and act as medical pre-screening or be used to evaluate the freshness of agri-food products in a rapid and non-invasive way.

TyBox: An Automatic Design and Code Generation Toolbox for TinyML Incremental On-Device Learning

Incremental on-device learning is one of the most relevant and interesting challenges in the field of Tiny Machine Learning (TinyML). Indeed, differently from traditional TinyML solutions where the training is typically carried out on the Cloud and inference only occurs on the tiny devices (e.g., embedded systems or Internet-of-Things units), incremental on-device TinyML allows both the inference and the training of TinyML models directly on tiny devices. This ability paves the way for TinyML-enabled intelligent devices that can learn directly on the field and adapt to evolving environments, different working conditions, or specific users. The literature in this field is quite limited with very few solutions focusing only on the incremental fine-tuning of machine learning models, whereas a general solution encompassing algorithms and code generation for incremental on-device TinyML is still perceived as missing. The aim of this article is to introduce, to the best of our knowledge for the first time in the literature, a toolbox called TyBox for the automatic design and code generation of incremental on-device TinyML classification models. In more detail, starting from a “static” TinyML model, TyBox is able to (i) automatically design the “incremental” on-device version of the TinyML model that has been suitably designed to take into account the technological constraint on the RAM memory of the target tiny device, and (ii) autonomously provide the C++ codes and libraries to support the inference and learning of the incremental on-device TinyML model directly on the tiny devices. TyBox has been extensively compared with a state-of-the-art incremental learning solution for TinyML and tested on an off-the-shelf tiny device (i.e., the Arduino Nano 33 BLE) in three relevant TinyML application tasks and scenarios: binary image classification, multi-class image classification, and ultra-wide-band human activity recognition. In addition, TyBox is released to the scientific community as a public repository.

TinyM 2 Net-V2: A Compact Low-power Software Hardware Architecture for M ulti m odal Deep Neural Networks

With the evaluation of Artificial Intelligence (AI), there has been a resurgence of interest in how to use AI algorithms on low-power embedded systems to broaden potential use cases of the Internet of Things (IoT). To mimic multimodal human perception, multimodal deep neural networks (M-DNN) have recently become very popular with the classification task due to their impressive performance for computer vision and audio processing tasks. This article presents TinyM 2 Net-V2 —a compact low-power software hardware architecture for m ulti m odal deep neural networks for resource-constrained tiny devices. To compress the models to implement on tiny devices, cyclicly sparsification and hybrid quantization (4-bits weights and 8-bits activations) methods are used. Although model compression techniques are an active research area, we are the first to demonstrate their efficacy for multimodal deep neural networks, using cyclicly sparsification and hybrid quantization of weights/activations. TinyM 2 Net-V2 shows that even a tiny multimodal deep neural network model can improve the classification accuracy more than that of any unimodal counterparts. Parameterized M-DNN model architecture was designed to be evaluated in two different case-studies: vehicle detection from multimodal images and audios and COVID-19 detection from multimodal audio recordings. The most compressed TinyM 2 Net-V2 achieves 92.5% COVID-19 detection accuracy (6.8% improvement from the unimodal full precision model) and 90.6% vehicle classification accuracy (7.7% improvement from the unimodal full precision model). A parameterized and flexible FPGA hardware accelerator was designed as well for TinyM 2 Net-V2 models. To the best of our knowledge, this is the first work accelerating multimodal deep neural network models on low-power Artix-7 FPGA hardware. We achieved energy efficiency of 9.04 GOP/s/W and 15.38 GOP/s/W for case-study 1 and case-study 2, respectively, which is comparable to the state-of-the-art results. Finally, we compared our tiny FPGA hardware implementation results with off-the-shelf resource-constrained devices and showed our implementation is faster and consumed less power compared to the off-the-shelf resource-constrained devices.

Enhancing Field Multiplication in IoT Nodes with Limited Resources: A Low-Complexity Systolic Array Solution

Enhancing Field Multiplication in IoT Nodes with Limited Resources: A Low-Complexity Systolic Array Solution

XimSwap: Many-to-Many Face Swapping for TinyML

The unprecedented development of deep learning approaches for video processing has caused growing privacy concerns. To ensure data analysis while maintaining privacy, it is essential to address how to protect individuals’ identities. One solution is to anonymize data at the source, avoiding the transmission or storage of information that could lead to identification. This study introduces XimSwap, a novel deep learning technique for real-time video anonymization, which can remove facial identification features directly on edge devices with minimal computational resources. Our approach offers a comprehensive solution that guarantees privacy by design. This novel method for implementing face-swapping ensures that the pose and expression of a target face remain unchanged and can be used on embedded devices with very limited computational resources. By incorporating style transfer layers into convolutional ones and optimizing the network’s operation, we achieved a reduction of over 98% in the required operations and parameters compared with state-of-the-art architectures. Our approach also significantly reduces RAM usage, making it possible to implement the anonymization process on tiny edge devices, including microcontrollers, such as the STM32H743.

Molecular Junctions for Terahertz Switches and Detectors.

Molecular electronics targets tiny devices exploiting the electronic properties of the molecular orbitals, which can be tailored and controlled by the chemical structure and configuration of the molecules. Many functional devices have been experimentally demonstrated; however, these devices were operated in the low-frequency domain (mainly dc to MHz). This represents a serious limitation for electronic applications, although molecular devices working in the THz regime have been theoretically predicted. Here, we experimentally demonstrate molecular THz switches at room temperature. The devices consist of self-assembled monolayers of molecules bearing two conjugated moieties coupled through a nonconjugated linker. These devices exhibit clear negative differential conductance behaviors (peaks in the current-voltage curves), as confirmed by ab initio simulations, which were reversibly suppressed under illumination with a 30 THz wave. We analyze how the THz switching behavior depends on the THz wave properties (power and frequency), and we benchmark that these molecular devices would outperform actual THz detectors.

Recent U.S. Government and foreign national government and commercial literature on semiconductors

Semiconductors play a major role in U.S. and international economic activity and in national security policymaking. They are tiny electronic devices based primarily on silicon or germanium, facilitating multiple industrial activities including systems foundational to U.S. technological competitiveness and national security. This industry is a key U.S. exporter with this product’s design and manufacturing being a global enterprise with design, fabrication, assembly, testing, and packing distributed globally. Semiconductors are essential building blocks for emerging technologies including artificial intelligence, autonomous systems, 5 G communications, and quantum computing. This article aspires to inform librarians and others about governmental and commercial literature produced on this subject. It aspires to enhance understanding of this topic and its intersections with economics, consumer goods, technology politics, and international security. (Platzer, Sutter, & Sargeant, 2020; Summary, 1–3).

Polyvinylidene Fluoride Based Piezoelectric Composites with Strong Interfacial Adhesion via Click Chemistry for Self-Powered Flexible Sensors.

Achieving relatively uniform dispersion in organic-inorganic composites with overwhelming differences in surface energy is a perennial challenge. Herein, novel eliminated polyvinylidene fluoride (EPVDF)/EPVDF functionalized barium titanate nanoparticles (EPVDF@BT) flexible piezoelectric nanogenerators (PENGs) with strong interfacial adhesion are developed via thermal stretching following sequential click chemistry. Thanks to the strong interfacial adhesion, the optimal PENGs containing ultra-high β-phase content (97.2%) exhibit not only optimized mechanical and dielectric behaviors but also excellent piezoelectric properties with high piezoelectric output (V = 10.7V, I = 216 nA), reliable durability (8000 cycles), ultrafast response time (20ms), and good sensitivity (2.09 nAkPa-1), far outperforming most reported PVDF-based composites. Furthermore, COMSOL finite element simulations (FEM) confirm that the elevated stress transfer efficiency induced by the strong interfacial adhesion is the main driving force for enhanced piezoelectric performances. For practical applications, self-powered PENGs can simply but stably capture mechanical energy, drive tiny electronic devices, and serve as potential multifunctional and durable sensors for detecting human physiological motions. This work opens a pioneering avenue to break the trade-offs between piezoelectric and other properties, which is of great importance for developing self-powered flexible sensors.

Towards Full Forward On-Tiny-Device Learning: A Guided Search for a Randomly Initialized Neural Network

In the context of TinyML, many research efforts have been devoted to designing forward topologies to support On-Device Learning. Reaching this target would bring numerous advantages, including reductions in latency and computational complexity, stronger privacy, data safety and robustness to adversarial attacks, higher resilience against concept drift, etc. However, On-Device Learning on resource constrained devices poses severe limitations to computational power and memory. Therefore, deploying Neural Networks on tiny devices appears to be prohibitive, since their backpropagation-based training is too memory demanding for their embedded assets. Using Extreme Learning Machines based on Convolutional Neural Networks might be feasible and very convenient, especially for Feature Extraction tasks. However, it requires searching for a randomly initialized topology that achieves results as good as those achieved by the backpropagated model. This work proposes a novel approach for automatically composing an Extreme Convolutional Feature Extractor, based on Neural Architecture Search and Bayesian Optimization. It was applied to the CIFAR-10 and MNIST datasets for evaluation. Two search spaces have been defined, as well as a search strategy that has been tested with two surrogate models, Gaussian Process and Random Forest. A performance estimation strategy was defined, keeping the feature set computed by the MLCommons-Tiny benchmark ResNet as a reference model. In as few as 1200 search iterations, the proposed strategy was able to achieve a topology whose extracted features scored a mean square error equal to 0.64 compared to the reference set. Further improvements are required, with a target of at least one order of magnitude decrease in mean square error for improved classification accuracy. The code is made available via GitHub to allow for the reproducibility of the results reported in this paper.

Excitonic Rydberg States in a Trilayer to Monolayer H2-Aided CVD-Grown Large-Area MoS2 Film with Excellent UV to Visible Broad Band Photodetection Applications.

The diverse nature of optoelectronic properties of few-layer or monolayer MoS2 is generally dominated by A and B excitons. Occasionally, strong Coulombic interactions within the 2D monolayer led to the creation of hydrogen-like Rydberg states of excitons in MoS2 similar to other 2D monolayers. In this paper, a simple process is used to convert trilayer MoS2 films to a monolayer by introducing H2 gas during chemical vapor deposition. Remarkably, alongside the usual A, B excitons, and A- trion, the appearance of the Rydberg states is evidenced by photoluminescence spectra even at room temperature; also, there is an increase in their areal percentage with an increase in H2 content. The s-type excited Rydberg states up to the fourth order (n = 5) and third order (n = 4) of A and B excitons, respectively, have been probed from the photoluminescence spectra at 93 K. Unprecedentedly, the first-order derivative of room-temperature photocurrent spectrum reveals the Rydberg states concurrently and elaboratively. Furthermore, the large-area MoS2 films exhibit photoresponse in a broad UV to visible region with excellent photosensitivity (∼102) toward both UV and visible lights. Not only does this provide a profound understanding of the excitonic Rydberg states but also highlights the considerable potential of large-area monolayer MoS2 overcoming the difficulty of tiny flake-related 2D device endeavors.