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Molecular imaging enables us to non-invasively visualize cellular functions and biological processes in living subjects, allowing accurate diagnosis of diseases at early stages. For successful molecular imaging, a suitable contrast agent with high sensitivity is required. To date, various nanoparticles have been developed as contrast agents for medical imaging modalities. In comparison with conventional probes, nanoparticles offer several advantages, including controllable physical properties, facile surface modification and long circulation time. In addition, they can be integrated with various combinations for multimodal imaging and therapy. In this opinion piece, we highlight recent advances and future perspectives of nanomaterials for molecular imaging. This article is part of the themed issue ‘Challenges for chemistry in molecular imaging’.

A new fluorescent indolizine-based scaffold was developed using a straightforward synthetic scheme starting from a pyrrole ring. In this fluorescent system, an N,N-dimethylamino group in the aryl ring at the C-3 position of indolizine acted as an electron donor and played a crucial role in inducing a red shift in the emission wavelength based on the ICT process. Moreover, various electron-withdrawing groups, such as acetyl and aldehyde, were introduced at the C-7 position of indolizine, to tune and promote the red shift of the emission wavelength, resulting in a color range from blue to orange (462–580 nm). Furthermore, the ICT effect in indolizine fluorophores allowed the design and development of new fluorescent pH sensors of great potential in the field of fluorescence bioimaging and sensors.

Various bioorthogonal chemistries have been used for fluorescent imaging owing to the advantageous reactions they employ. Recent advances in bioorthogonal chemistry have revolutionized labeling strategies for fluorescence imaging, with inverse electron demand Diels–Alder (iEDDA) reactions in particular attracting recent attention owing to their fast kinetics and excellent specificity. One of the most interesting features of the iEDDA labeling strategy is that tetrazine-functionalized dyes are known to act as fluorogenic probes. In this review, we will focus on the synthesis, molecular-design strategies, and bioimaging applications of tetrazine-functionalized fluorogenic probes. Traditional Pinner reaction and “Pinner-like” reactions for tetrazine synthesis are discussed here, as well as metal-catalyzed C–C bond formations with convenient tetrazine intermediates and the fabrication of tetrazine-conjugated fluorophores. In addition, four different quenching mechanisms for tetrazine-modified fluorophores are presented.

Implantation of biodegradable wafers near the brain surgery site to deliver anti-cancer agents which target residual tumor cells by bypassing the blood-brain barrier has been a promising method for brain tumor treatment. However, further improvement in the prognosis is still necessary. We herein present novel materials and device technologies for drug delivery to brain tumors, i.e., a flexible, sticky, and biodegradable drug-loaded patch integrated with wireless electronics for controlled intracranial drug delivery through mild-thermic actuation. The flexible and bifacially-designed sticky/hydrophobic device allows conformal adhesion on the brain surgery site and provides spatially-controlled and temporarily-extended drug delivery to brain tumors while minimizing unintended drug leakage to the cerebrospinal fluid. Biodegradation of the entire device minimizes potential neurological side-effects. Application of the device to the mouse model confirms tumor volume suppression and improved survival rate. Demonstration in a large animal model (canine model) exhibited its potential for human application. There is a need to further improve the efficacy of biodegradable wafers used in surgically treated brain tumors. Here, the authors report a flexible, biodegradable wireless device capable of adhesion to surgical site for optimal drug delivery upon mild-thermic actuation and report therapeutic efficacy in mouse and canine tumor models.

The construction of a small molecule library that includes compounds with medium-sized rings is increasingly essential in drug discovery. These compounds are essential for identifying novel therapeutic agents capable of targeting “undruggable” targets through high-throughput and high-content screening, given their structural complexity and diversity. However, synthesizing medium-sized rings presents notable challenges, particularly with direct cyclization methods, due to issues such as transannular strain and reduced degrees of freedom. This review presents an overview of current strategies in synthesizing medium-sized rings, emphasizing innovative approaches like ring-expansion reactions. It highlights the challenges of synthesis and the potential of these compounds to diversify the chemical space for drug discovery, underscoring the importance of medium-sized rings in developing new bioactive compounds.

Considering the potential bioactivities of natural product and natural product-like compounds with highly complex and diverse structures, the screening of collections and small-molecule libraries for high-throughput screening (HTS) and high-content screening (HCS) has emerged as a powerful tool in the development of novel therapeutic agents. Herein, we review the recent advances in divergent synthetic approaches such as complexity-to-diversity (Ctd) and biomimetic strategies for the generation of structurally complex and diverse indole-based natural product and natural product-like small-molecule libraries.

Lithium-ion batteries have recently been in the spotlight as the main energy source for the energy storage devices used in the renewable energy industry. The main issues in the use of lithium-ion batteries are satisfaction with the design life and safe operation. Therefore, battery management has been required in practice. In accordance with this demand, battery state indicators such as the state-of-charge (SOC), state-of-health (SOH), state-of-function (SOF), and state-of-temperature (SOT) have been widely applied. The use of these indicators ensures safe operation without overcharging and over-discharging. In addition, it can also help satisfy the design life. This paper presents a literature review of battery state indicators over the last three years and proposes the requirement of state-of-the-art battery state indicators. It also suggests future developments for battery management system (BMS) in stationary energy storage systems (ESSs).

One of the hugely focused areas of research for addressing the world’s energy and environmental challenges is electrochemical water oxidation. Morphological modulation of nanomaterials is essential for producing efficient electrocatalysts to achieve the required results. The purpose can be achieved by controlling synthesis parameters, and this is a key factor which greatly influences the oxygen evolution reaction (OER) performance during electrochemical water splitting. In this study, synthesis of cobalt molybdate (CoMoO4) through a simple and low-cost hydrothermal/solvothermal strategy with tunable morphology is demonstrated. Different morphologies, namely rods-like, buds-like, and sheets-like, referred to as R-CMO, B-CMO, and S-CMO, respectively, have been obtained by systematically varying the solvent media. Their catalytic activity towards OER was investigated in 1.0 M aqueous KOH medium. R-CMO nanoparticles synthesized in an aqueous medium demonstrated the lowest overpotential value of 349 mV to achieve a current density of 10 mA cm−2 compared with other as-prepared catalysts. In contrast, the B-CMO and S-CMO exhibited overpotential values of 369 mV and 384 mV, respectively. Furthermore, R-CMO demonstrated an exceptional electrochemical stability for up to 12 h.

To enhance the efficiency of an energy storage system, it is important to predict and estimate the battery state, including the state of charge (SOC) and state of health (SOH). In general, the statistical approaches for predicting the battery state depend on historical data measured via experiments. The statistical methods based on experimental data may not be suitable for practical applications. After reviewing the various methodologies for predicting the battery capacity without measured data, it is found that a joint estimator that estimates the SOC and SOH is needed to compensate for the data shortage. Therefore, this study proposes an integrated model in which the dual extended Kalman filter (DEKF) and autoregressive (AR) model are combined for predicting the SOH via a statistical model in cases where the amount of measured data is insufficient. The DEKF is advantageous for estimating the battery state in real-time and the AR model performs better for predicting the battery state using previous data. Because the DEKF has limited performance for capacity estimation, the multivariate AR model is employed and a health indicator is used to enhance the performance of the prediction model. The results of the multivariate AR model are significantly better than those obtained using a single variable. The mean absolute percentage errors are 1.45% and 0.5183%, respectively.

Grading of gliomas is critical information related to prognosis and survival. We aimed to apply a radiomics approach using various machine learning classifiers to determine the glioma grading. We considered 285 (high grade  = 210, low grade  = 75) cases obtained from the Brain Tumor Segmentation 2017 Challenge. Manual annotations of enhancing tumors, non-enhancing tumors, necrosis, and edema were provided by the database. Each case was multi-modal with T1-weighted, T1-contrast enhanced, T2-weighted, and FLAIR images. A five-fold cross validation was adopted to separate the training and test data. A total of 468 radiomics features were calculated for three types of regions of interest. The minimum redundancy maximum relevance algorithm was used to select features useful for classifying glioma grades in the training cohort. The selected features were used to build three classifier models of logistics, support vector machines, and random forest classifiers. The classification performance of the models was measured in the training cohort using accuracy, sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic curve. The trained classifier models were applied to the test cohort. Five significant features were selected for the machine learning classifiers and the three classifiers showed an average AUC of 0.9400 for training cohorts and 0.9030 (logistic regression 0.9010, support vector machine 0.8866, and random forest 0.9213) for test cohorts. Glioma grading could be accurately determined using machine learning and feature selection techniques in conjunction with a radiomics approach. The results of our study might contribute to high-throughput computer aided diagnosis system for gliomas.