Nanomaterials are endowed with unique features and essential properties suitable for employing in the field of nanomedicine. The nanomaterials can be classified as 0D, 1D, 2D, and 3D based on their dimensions. The nanomaterials can be malleable and ductile and they can be drawn into wires and sheets. Examples of nanomaterials are quantum dots (0D), nanorods, nanowires (1D), nanosheets (2D), and nanocubes (3D). These nanomaterials can be synthesized using top-down and bottom-up approaches. The achievements of 0D and 1D nanomaterials are used to detect trace heavy metal (e.g., Pb²⁺) and have higher sensitivity with the order of five as compared to conventional sensors. The achievements of 2D and 3D nanomaterials are used as diagnostic and therapeutic agents with multifunctional ability in imaging systems such as PET, SPECT, etc. These imaging modalities can be used to track the drug in living tissues. This review comprises the state-of-the-art of the different dimensions of the nanomaterials employed in theranostics. The nanomaterials with different dimensions have unique physicochemical properties that can be utilized for therapy and diagnosis. The multifunctional ability of the nanomaterials can have a distinct advantage that is used in the field of theranostics. Different dimensions of the nanomaterials would have more scope in the field of nanomedicine.

Chebulic Myrobalan is the main ingredient in the Ayurvedic formulation Triphala, which is used for kidney and liver dysfunctions. Herein, natural nitrogen-doped carbon dots (NN-CDs) were prepared from the hydrothermal carbonization of Chebulic Myrobalan and were demonstrated to sense heavy metal ions in an aqueous medium. Briefly, the NN-CDs were developed from Chebulic Myrobalan by a single-step hydrothermal carbonization approach under a mild temperature (200 °C) without any capping and passivation agents. They were then thoroughly characterized to confirm their structural and optical properties. The resulting NN-CDs had small particles (average diameter: 2.5 ± 0.5 nm) with a narrow size distribution (1–4 nm) and a relatable degree of graphitization. They possessed bright and durable fluorescence with excitation-dependent emission behaviors. Further, the as-synthesized NN-CDs were a good fluorometric sensor for the detection of heavy metal ions in an aqueous medium. The NN-CDs showed sensitive and selective sensing platforms for Fe³⁺ ions; the detection limit was calculated to be 0.86 μM in the dynamic range of 5–25 μM of the ferric (Fe³⁺) ion concentration. Moreover, these NN-CDs could expand their application as a potential candidate for biomedical applications and offer a new method of hydrothermally carbonizing waste biomass.

Herein, Sargassum coreanum (marine algae)-mediated silver nanoparticles (AgNPs) were successfully synthesized by a simple reduction method. The synthesized AgNPs were characterized using ultraviolet-visible spectroscopy, attenuated total reflection Fourier transformed infrared spectroscopy, X-ray diffractometry, field emission scanning electron microscopy (FESEM) with energy-dispersive X-ray (EDX) spectroscopy, and high-resolution transmission electron microscopy (HR-TEM) analysis. The acquired colloidal AgNPs were strongly absorbed around 420 nm and displayed brown color under visible light. The XRD pattern of AgNPs exposed their face-centered cubic geometry along with crystalline nature. The HRTEM images of synthesized AgNPs confirmed the mean particle size of 19 nm with a distorted spherical shape, and the calculated interlayer distance (d-spacing value) was about 0.24 nm. Further, the catalytic degradation of methylene blue using sodium borohydride and AgNPs was monitored using UV–vis spectroscopy. The result revealed that AgNPs performed as a superior catalyst, which completely degraded MB in 20 min. The rate constant for MB degradation was calculated to be 0.106 min⁻¹, demonstrating that the marine algae-mediated AgNPs had outstanding catalytic activity. This approach is easy and environmentally benign, which can be applied for environmental-based applications such as dye degradation and pollutant detoxification.

In this study, we speculate that the hydroxyl-containing benzo[b]thiophene analogs, 1-(3-hydroxybenzo[b]thiophen-2-yl) ethanone (BP) and 1-(3-hydroxybenzo[b]thiophen-2-yl) propan-1-one hydrate (EP), might possess antiproliferative activity against cancer cells. Hydroxyl-containing BP and EP show selectivity towards laryngeal cancer cells (HEp2), with IC₅₀ values of 27.02 ± 1.23 and 35.26 ± 2.15 µM, respectively. The hydroxyl group present in the third position is responsible for the anticancer activity and is completely abrogated when the hydroxyl group is masked. BP and EP enhance the antioxidant enzyme activity and reduce the ROS production, which are correlated with the antiproliferative effect in HEp-2 cells. An increase in the BAX/BCL-2 ratio occurs during the BP and EP treatment and activates the caspase cascade, resulting in apoptosis stimulation. It also arrests the cells in the Sub-G1 phase, indicating the induction of apoptosis. The molecular docking and simulation studies predicted a strong interaction between BP and the CYP1A2 protein, which could aid in combinational therapy by enhancing the bioavailability of the drugs. BP and EP possess an antioxidant property with low antiproliferative effects (~5.18 µg/mL and ~7.8 µg/mL) as a standalone drug, therefore, they can be combined with other drugs for effective chemotherapy that might trigger the effect of pro-oxidant drug on healthy cells.

In recent years, fuel cell (FC) technology has seen a promising increase in its proportion in stationary power production. Several pilot projects are in operation across the world, with the number of running hours steadily rising, either as stand-alone units or as part of integrated gas turbine–electric energy plants. FCs are a potential energy source with great efficiency and zero emissions. To ensure the best performance, they normally function within a confined temperature and humidity range; nevertheless, this makes the system difficult to regulate, resulting in defects and hastened deterioration. For diagnosis, there are two primary approaches: restricted input information, which gives an unobtrusive, rapid yet restricted examination, and advanced characterization, which provides a more accurate diagnosis but frequently necessitates invasive or delayed tests. Artificial Intelligence (AI) algorithms have shown considerable promise in providing accurate diagnoses with quick data collecting. This work focuses on software models that allow the user to evaluate many different possibilities in the shortest amount of time and is a vital method for proper and dynamic analysis of such entities. The artificial neural network, genetic algorithm, particle swarm optimization, random forest, support vector machine, and extreme learning machine are common AI approaches discussed in this review. This article examines the modern practice and provides recommendations for future machine learning methodologies in fuel cell diagnostic applications. In this study, these six AI tools are specifically explained with results for a better understanding of the fuel cell diagnosis. The conclusion suggests that these approaches are not only a popular and beneficial tool for simulating the nature of an FC system, but they are also appropriate for optimizing the operational parameters necessary for an ideal FC device. Finally, observations and ideas for future research, enhancements, and investigations are offered.