Novel Cu-based Semiconducting Photocathode Configurations for Efficient Solar Harvesting and Photoelectrochemical Water Splitting

This thesis focuses on advancing photoelectrochemical (PEC) water splitting using Cu-based semiconducting materials for solar-driven hydrogen production. It incorporates strategies such as heterojunction formation, plasmonic enhancement, co-catalyst integration, doping, and cation substitution to enhance PEC performance. Emphasis is placed on studying and understanding the underlying charge transfer mechanisms and material behavior to gain insights that guide further improvements. The work demonstrates enhanced light absorption, charge separation, and catalytic efficiency, using earth-abundant materials. These findings support scalable PEC systems aligned with global goals for carbon neutrality and clean, sustainable hydrogen energy solutions.</p

Ultrahigh-Speed Near-Infrared Electrodynamic Solid-State Trans-Memory

Emerging information technology necessitates the well-controlled manipulation of light transmission while maintaining memory behavior; therefore, achieving dynamic optical properties of a solid-state material is crucial. However, despite the vital role of solid-state architecture in photonic sensors, communication, and memory storage, the realization of adjustable optical transmittance across a thin film remains a challenging task, since it is primarily governed by intrinsic material stoichiometry. Here, we developed a proof-of-concept solid-state copper oxide-based device in which optical transmittance, particularly the near-infrared range, can alert reversibly in various levels, ranging from 76 to 36%, by fine-tuning short (∼1 ms) electric pulses. The device maintained its flipped transmittance value even when the illumination intensity remained constant, offering nonvolatile multilevel memory. Current–Voltage curves show a stable analog hysteresis loop opening, and based on the valence band spectroscopy measurement, the underlying working mechanism is explained by the kinetics of oxygen vacancy migration-induced change in the stoichiometry of copper oxide. Furthermore, an array was built and trained to transmit the well-controlled optical intensity over a selective area. Tuning the optical property with an electric field opens an avenue for the development of reconfigurable thin-film-based area-selective optical devices for a variety of applications, including display, optical window, and electrooptical coatings

Adaptive Memory and <i>In Materia</i> Reinforcement Learning Enabled by Flexoelectric-like Response from Ultrathin HfO₂

Reinforcement learning (RL) is a mathematical framework of neural learning by trial and error that revolutionized the field of artificial intelligence. However, until now, RL has been implemented in algorithms with the compatibly of traditional complementary metal-oxide-semiconductor-based von Neumann digital platforms, which thus limits performance in terms of latency, fault tolerance, and robustness. Here, we demonstrate that nanocolumnar (∼12 nm) HfO2 structures can be used as building blocks to conduct the RL within the material by combining its stress-adjustable charge transport and memory functions. Specifically, HfO2 nanostructures grown by the sputtering method exhibit self-assembled vertical nanocolumnar structures that generate voltage depending on the impact of stress under self-biased conditions. The observed results are attributed to the flexoelectric-like response of HfO2. Further, multilevel current (>10–3 A) modulation with touch and controlled suspension (∼10–12 A) with a short electric pulse (100 ms) were demonstrated, yielding a proof-of-concept memory with an on/off ratio greater than 109. Utilizing multipattern dynamic memory and tactile sensing, RL was implemented to successfully solve a maze game using an array of 6 × 4. This work could pave the way to do RL within materials for a variety of applications such as memory storage, neuromorphic sensors, smart robots, and human–machine interaction systems

Adaptive Memory and <i>In Materia</i> Reinforcement Learning Enabled by Flexoelectric-like Response from Ultrathin HfO₂

Reinforcement learning (RL) is a mathematical framework of neural learning by trial and error that revolutionized the field of artificial intelligence. However, until now, RL has been implemented in algorithms with the compatibly of traditional complementary metal-oxide-semiconductor-based von Neumann digital platforms, which thus limits performance in terms of latency, fault tolerance, and robustness. Here, we demonstrate that nanocolumnar (∼12 nm) HfO2 structures can be used as building blocks to conduct the RL within the material by combining its stress-adjustable charge transport and memory functions. Specifically, HfO2 nanostructures grown by the sputtering method exhibit self-assembled vertical nanocolumnar structures that generate voltage depending on the impact of stress under self-biased conditions. The observed results are attributed to the flexoelectric-like response of HfO2. Further, multilevel current (>10–3 A) modulation with touch and controlled suspension (∼10–12 A) with a short electric pulse (100 ms) were demonstrated, yielding a proof-of-concept memory with an on/off ratio greater than 109. Utilizing multipattern dynamic memory and tactile sensing, RL was implemented to successfully solve a maze game using an array of 6 × 4. This work could pave the way to do RL within materials for a variety of applications such as memory storage, neuromorphic sensors, smart robots, and human–machine interaction systems

Additional file 1 of Oligomers of hepatitis A virus (HAV) capsid protein VP1 generated in a heterologous expression system

Additional file 1: Figure S1. Association of His-GST-VP1 with bacterial chaperone GroEL determined by LC–MS/MS (A) LC–MS/MS analysis showing mass spectra of trypsin digested, purified protein (B) Peptide mass fingerprinting followed by database search confirmed the presence of GroEL, VP1 and GST

Additional file 4 of Oligomers of hepatitis A virus (HAV) capsid protein VP1 generated in a heterologous expression system

Additional file 4: Figure S4. Size distribution and morphological characterization of VP1 purified from E. coli strain BB1553. (A) Dynamic Light Scattering (DLS) of purified VP1, with the Rhmean, Rhpeak and peak area percentage in a tabular form. (B) Presence of protein aggregate visualized by transmission electron microscopy

Additional file 2 of Oligomers of hepatitis A virus (HAV) capsid protein VP1 generated in a heterologous expression system

Additional file 2: Figure S2. Expression and Purification of His-VP1 from E.coli strain Rosetta (DE3) pLysS cells (A) Elution profile of His-VP1 from a Superdex 200 (10/300) size exclusion column (B) Purified protein from size exclusion chromatography analyzed on 10% SDS-PAGE, showing the presence of both GroEL and His-VP1 at 60 kDa and 33 kDa respectively. Lane 1 represents the protein markers, lane 2 represents Ni–NTA purified fraction prior to SEC, lanes 3 to 7 represent the peak protein fraction from SEC (C) A complex of HisVP1-GroEL visualized by transmission electron microscopy

Additional file 3 of Oligomers of hepatitis A virus (HAV) capsid protein VP1 generated in a heterologous expression system

Additional file 3: Figure S3. Expression and purification of His-VP1 from ArcticExpress (DE3) and BB1553 E.coli strains (A) Purified His-VP1 from ArcticExpress (DE3) cells analyzed on 8% SDS-PAGE. Size exclusion chromatography resulted in co-elution of Cpn60 and His-VP1. Lane 1 represents the protein markers, while lanes 2 to 6 represent the peak fraction from SEC. (B) Expression and purification of His-VP1 from E. coli strain BB1553 deficient in DnaK bacterial chaperone. Ni–NTA purified protein fractions analyzed on 10% SDS-PAGE. Lane 1 represents the protein markers, and lanes 2 to 7 represent protein fractions eluted with 350 mM imidazole. (C) SEC purified fractions of His-VP1 from E. coli strain BB1553 analyzed on 10% SDS-PAGE. Lane 1 represents the protein markers, lane 2 represents Ni–NTA purified fraction prior to SEC, lanes 3 to 7 represent the peak protein fraction from SEC

Pyrolysis of Sugarcane (Saccharum officinarum L.) Leaves and Characterization of Products

The finite nature, regional availability, and environmental problems associated with the use of fossil fuels have forced all countries of the world to look for renewable eco-friendly alternatives. Agricultural waste biomasses, generated through the cultivation of cereal and noncereal crops, are being considered renewable and viable alternatives to fossil fuels. In view of this, there has been a global spurt in research efforts for using abundantly available agricultural wastes as feedstocks for obtaining energy and value-added products through biochemical and thermal conversion routes. In the present work, the thermochemical characteristics and thermal degradation behavior of sugarcane leaves (SCL) and tops were studied. The batch pyrolysis was carried out in a fixed-bed tubular reactor to obtain biochar, bio-oil, and pyrolytic gas. Effects of bed height (4–16 cm), particle size (0.180–0.710 mm), heating rate (15–30 °C/min), and temperature (350–650 °C) were investigated. The maximum yields of bio-oil (44.7%), biogas (36.67%), and biochar (36.82%) were obtained at 550, 650, and 350 °C, respectively, for a 16 cm deep bed of particles of size 0.18–0.30 mm at the heating rate of 25 °C/min. The composition of bio-oil was analyzed using Fourier transform infrared spectroscopy (FTIR), proton nuclear magnetic resonance (1H NMR), and gas chromatography–mass spectrometry (GC–MS) techniques. Several aliphatic, aromatic, phenolic, ketonic, and other acidic compounds were found in the bio-oil. The biochar had a highly porous structure and several micronutrients, making it useful as a soil conditioner. In the middle temperature ranges, biogas had more methane and CO and less hydrogen, but at higher temperatures, hydrogen was predominant

Room-Temperature Quantum Diodes with Dynamic Memory for Neural Logic Operations

The pursuit of high-performance, next-generation nanoelectronics is fundamentally reliant on exploiting quantum phenomena such as tunneling at room temperature. However, quantum tunneling and memory dynamics are governed by two conflicting parameters: the presence or absence of defects. Therefore, the integration of both attributes within a single device presents substantial challenges. Nevertheless, successful integration has the potential to prompt crucial breakthroughs by emulating biobrain-like dynamics, in turn enabling sophisticated in-material neural logic operations. In this work, we demonstrate that a conformal nanolaminate HfO2/ZrO2 structure on silicon enables high-performing (>106 s) Fowler–Nordheim tunneling at room temperature. In addition, the device exhibits unipolar dynamic hysteresis loop opening (on/off ratio >102) with high endurance (>104 cycles) along with negative differential resistance, which is attributed to the collective ferroelectric and capacitive effects and is utilized to emulate synaptic functions. Further, proof-of-concept logic gates based on voltage-dependent plasticity and time-domain were developed using a single device, offering in-material neural-like data processing. These findings pave the way for the realization of high-performing and scalability tunneling devices in advanced nanoelectronics, which mark a promising route toward the development of next-generation, fundamental neural logic computing systems