Theodorus Jonathan Wijaya

Multiplexed gas sensor arrays combined with machine learning have unlocked previously inaccessible applications for scent-based sensing. Current platforms are limited by overlapping sensing materials with similar compositions, leading to highly correlated responses, or multistep deposition processes that hinder scalability. In this work, we developed a 16-element monolithic chip with fully distinct sensing layers, enabling a truly heterogeneous array. The system consists of highly sensitive carbon nanotube field effect transistors that are functionalized through a single-step microdispensing method compatible with automated pipetting systems. The resulting chip produces characteristic signal patterns in response to object-specific scent profiles and, when combined with machine learning algorithms, can perform automated object identification. We demonstrate the classification of 16 different objects, including food spoilage and nut allergens, with a 92.6% overall prediction accuracy.

Carbon nanotubes (CNTs) possess exceptional optoelectronic properties, including broadband absorption and high absorption coefficients, making them promising candidates for photodetector applications. However, conventional designs often rely on single or aligned nanotubes, which restrict device scalability and require complex fabrication techniques. In this work, we address these limitations by utilizing CNT-suspended solutions to fabricate large-area photodetectors based on CNT networks via vacuum filtration and direct laser scribing. CNT networks are uniformly formed onto low-thermal-conductance polymeric filter papers that serve as substrates and mitigate the need for suspended structures, reducing the fabrication complexity and cost. The CNT films are patterned into single devices and 49-pixel linear arrays by using a laser-cutting process, enabling scalable and cost-effective production. The resulting devices operate as bolometers and exhibit broad spectral sensitivity extending from visible to midwave infrared, showing stable operation at room temperature. We further demonstrate proof-of-concept IR imaging using linear arrays.

Spectral machine vision collects spectral and spatial information as three-dimensional hypercubes and digitally processes them, which causes a data bottleneck, limiting power efficiency, frame rate, and spectral-spatial resolution. This work introduces spectral kernel machines (SKMs) to overcome these bottlenecks. SKM directly compresses spectral analysis through the output photocurrent and learns from example objects to identify and classify new samples in a “sniff-and-seek” mode. We experimentally demonstrated SKMs with electrically tunable bipolar black phosphorus–molybdenum disulfide (bP-MoS ₂ ) photodiodes in the near- and mid-infrared band and silicon photoconductors in the visible band, performing versatile intelligent tasks from chemometrics to semiconductor metrology. This architecture consumed substantially less power and was more than an order of magnitude faster than existing solutions for hyperspectral image analysis, defining an intelligent imaging and sensing paradigm with intriguing possibilities.

The mid-wave infrared (MWIR) spectral range (λ = 3–8 μm) enables important sensing and imaging applications, including non-invasive bioimaging, night vision, and autonomous navigation. Commercial MWIR photodetectors are limited to rigid imagers based on heteroepitaxial materials. There is an emerging need for mechanically flexible MWIR imagers to broaden their functionality and practicality. Recently, photodetectors using van der Waals (vdW) black phosphorus (BP) flakes have demonstrated highly sensitive room-temperature photodetection. Additionally, vdW materials are solution-processable, facilitating scalable processing and flexible device fabrication. In this work, we present flexible MWIR imagers consisting of photodiodes fabricated on thin plastic substrates using BP ink films. We demonstrate mechanically robust responsivity up to 2.5-mm bending radii and after 5000 bending cycles. Leveraging this flexibility, we achieve full-azimuthal imaging, detecting directional light sources with precision. These results establish a scalable approach for large-area, conformable MWIR imaging and pave the way for integration with flexible electronics.

Flexible organic photodiodes (OPDs) are used to detect light in system‐scale demonstrations of skin‐conformable devices. However, the detectivity of OPDs deteriorates under various environmental conditions, such as light irradiation, air exposure, and heating. This decrease in detectivity is observed in OPDs with a widely used sol–gel ZnO (ZnO SG) electron transport layer (ETL), where the dark current at the reverse bias increased by several orders of magnitude. In this study, a low dark current and stable detectivity with respect to the aforementioned external changes are achieved. The enhanced stability stems from the suppression of the increase in dark current realized by using a mixture of an organic polymer, polyethyleneimine (PEIE), and inorganic crystals (ZnO nanoparticles) to create a nanoparticle‐based, Zn‐chelated PEIE (PEI‐Zn NP) as the ETL of the OPDs. The detectivities of OPDs with PEI‐Zn NP are 89%, 84%, and 93% of their original values after light irradiation, air storage, and thermal heating, respectively. In contrast, their ZnO SG counterparts exhibited stabilities of only 9.9%, 55%, and 2.6%, respectively, in the same tests. Furthermore, the use of PEI‐Zn NP ETL in ultraflexible OPDs is demonstrated by the maintained detectivity after 5000 cycles of device bending.

Organic photodetectors (OPDs) are key devices for monitoring vital signs, such as heart rate and blood oxygen level. For realizing the long-term measurement of biosignals, stable operation is essential. To improve the stability of OPDs, it is important to analyze each layer to understand the degradation mechanism. We developed detachable top electrode (DTE)-OPDs to enable both surface analysis of the active layer and device characterization to be performed nondestructively and repeatedly within a single device. The substrate of the top electrode is a 2 μm thick elastomer sheet reinforced with polyurethane nanofibers. The DTE-OPDs showed a D_sh* of 1.4 × 10¹² Jones by attaching the top electrodes to the bottom stack without external pressure. The DTE-OPDs showed no degradation after 1000 attaching/detaching cycles of the top electrodes. Using the DTE-OPDs, we successfully observed the relationship between dark current increase and oxidization of the active layer.