Feridun Ay

The growing demand for high-energy supercapacitors necessitates electrode–electrolyte systems that can operate across wide voltage windows without compromising stability. Here, we report symmetric supercapacitors based on termination-free Mo₂C MXene films synthesized via chemical vapor deposition (CVD) and paired with ionic liquid (IL) electrolytes. Unlike conventionally etched MXenes, the CVD-grown Mo₂C features a clean, termination-free surface that enables a stable and compatible interface with ILs. Using [BMIM]BF₄, the devices deliver energy densities of 114-121 Wh kg⁻¹ and power densities up to 1.8 MW kg⁻¹ within a voltage window of ~2.2-2.3 V, while retaining up to 96% capacitance after 10,000 cycles. When paired with STFSI, the devices achieve the highest specific power density, reaching 2.9 MW kg⁻¹. These results highlight the large surface area of Mo₂C, its excellent electrical conductivity, and strong compatibility with ionic liquid electrolytes. Comparative studies with filter paper and Celgard separators further reveal a trade-off between ionic accessibility and long-term durability, underscoring the critical role of electrolyte-separator-electrode synergy. Collectively, this work establishes termination-free CVD-grown Mo₂C MXene as a promising design pathway for next-generation IL-based high-energy supercapacitors.

With recent developments, nanophotonics has emerged as a field of intense interest offering improvements in the performance of photonic integrated circuits (PICs). By significantly increasing data rates, transmission speed, and bandwidth and reducing both transmission loss and energy consumption, nanophotonic technologies present significant advantages that could redefine the efficiency and capabilities of PICs. In this study, the design of photonic crystal power dividers is addressed using a two-stage deep learning strategy with Deep Convolutional Generative Adversarial Networks (DCGANs). The study primarily aims for high-resolution designs compared to the existing methods. This approach expands the solution set, allowing for simulation results that more closely align with real-world performance while achieving higher transmission efficiency and output rates within practical computational limits. Previous studies were limited by low-resolution designs and the necessity for extensive optimization processes. Using the DCGAN architecture, high-resolution patterns (200 × 200 pixels) with transmission efficiencies exceeding 95% are achieved. This approach highlights the effectiveness of deep learning in discovering complex nonlinear relationships, successfully producing power dividers capable of arbitrary energy distributions. The innovative method positions the role of deep learning in advancing photonic devices and emphasizes their potential impact on neurocomputing and future hardware development.

In this work, we report on the development of ALD-grown ultralow loss host Al₂O₃ planar waveguides on Si and then successful realization of ALD-grown Er³⁺ doped on-chip Al₂O₃ waveguide-based high-gain amplifier devices. Both thermal and plasma enhanced ALD processes have been employed in the optimization processes. Optimization of both Al₂O₃ and Er₂O₃ growth conditions is performed for fine-tuning the optical properties of respective films. Systematic characterization of both films has been carried out by making use of spectroscopic ellipsometry, XRD, FTIR, and XPS techniques. The Er₂O₃ layers are sandwiched between the Al₂O₃ layers to achieve controlled erbium ion doping with the homogenous distribution. The effects of the growth parameters of Al₂O₃ and Er₂O₃ including deposition temperature, RF plasma power, TMA pulse time, Er(thd)3 pulse time, and O₂ pulse time are optimized. With this work, we propose a method for precise control of doping ion concentration and its distribution. As a demonstrator, single-mode ridge waveguide amplifiers are realized by lithography from the active planar structures. ALD deposited chip-scale singlemode Er³⁺ doped Al₂O₃ ridge waveguide amplifiers with a small-signal gain of more than 13 dB/cm over the C band are realized. The reported ALD based growth process can further be adapted for active ion doping of different oxide systems with precise concentration and profile control, thus offering new opportunities for active on-chip applications.

A phototransistor built by the assembly of 2D colloidal semiconductor quantum wells (CQWs) on a single layer of 2D transition metal dichalcogenide (TMD) is displayed. This hybrid device architecture exhibits high efficiency in Förster resonance energy transfer (FRET) enabling superior performance in terms of photoresponsivity and detectivity. Here, a thin film of CdSe/CdS CQWs acts as a sensitizer layer on top of the MoS₂ monolayer based field‐effect transistor, where this CQWs–MoS₂ structure allows for strong light absorption in CQWs in the operating spectral region and strong dipole‐to‐dipole coupling between MoS₂ and CQWs resulting in enhanced photoresponsivity of one order of magnitude (11‐fold) at maximum gate voltage (V_BG = 2 V) and two orders of magnitude (≈ 5 × 10²) at V_BG = −1.5 V, and tenfold enhanced specific detectivity. The illumination power‐dependent characterization of this hybrid device reveals that the thin layer of CQWs dominates the photogating mechanism compared to the photoconductivity effect on detection performance. Such hybrid designs hold great promise for 2D‐material based photodetectors to reach high performance and find use in optoelectronic applications.