The role of green and low-carbon energy (gLE) resources in realizing the envisaged future decarbonized energy generation and supply cannot be overemphasized. The world has witnessed growing attention to the application of green energy (gE) sources such as solar, wind, hydro, geothermal, and biomass (energy crops, biogas, biodiesel, etc.). There is also the existence of low-carbon energy (LE) resources such as power-to-X, power-to-fuel, power-to-gas, e-fuel, waste-to-energy, etc., which possess huge potential for delivering sustainable energy, thus facilitating a pathway for achieving the desired environmental sustainability. In addition, the evolution of the cyber-physical power systems and the need for strengthening capacity in advanced energy materials are among the key factors that drive the deployment of gLE technologies around the world. This paper, therefore, presents the recent global developments in gLE resources, including the trends in their deployments for different applications in commercial premises. The study introduces different conceptual technical models and configurations of energy systems; the potential of multi-energy generation in a microgrid (m-grd) based on the gLE resources is also explored using the System Advisor Model (SAM) software. The m-grd is being fueled by solar, wind, and fuel cell resources for supplying a commercial load. The quantity of carbon emissions avoided by the m-grd is evaluated compared to a purely conventional m-grd system. The paper presents the cost of energy and the net present cost of the proposed m-grid; it also discusses the relevance of carbon capture and storage and carbon sequestration technologies. The paper provides deeper insights into the understanding of clean and unconventional energy resources.

This study investigates the radiation tolerance of a Si0.5Ge0.5 source vertical tunnel field effect transistor (VTFET) under heavy ion-induced single event effects (SEEs). Single event effects (SEEs) occur when high-energy particles interact with semiconductor devices, leading to unintended behavior. The effect of high energy ions on the VTFET is examined for various linear energy transfer (LET) values and at multiple ion hit locations. In this study, it is found that the VTFET exhibits a maximum drain current of 277 at LET = 10 MeV·cm /mg, slightly above the ON-state current of 273 indicating minimal susceptibility to radiation-induced perturbations. Analysis revealed that drift-diffusion dominates charge collection, with the tunneling region being the most vulnerable to ion strikes. Device response was modeled using the Silvaco TCAD framework with an impact ionization model, evaluating a range of linear energy transfer (LET) values and ion hit positions. Key metrics, including transient drain current, charge density, total collected charge, and bipolar gain, were quantified to determine sensitivity. These findings underscore the device's intrinsic robustness and offer valuable insights for the design of radiation-hardened semiconductor components.

Dielectric Modulated Field-Effect Transistors (DMFETs) have emerged as promising candidates for label-free bioanalyte detection. However, the inherent short-channel effects in conventional DMFETs increase their static power dissipation significantly and limit their scalability and sensitivity. Therefore, FETs based on alternate conduction mechanism such as tunneling (TFETs), which are immune to the short-channel effects, appear to be a lucrative alternative to the MOSFETs for biosensing application. In this work, we propose a novel Dual Cavity Dielectric Modulated Nanotube Tunnel FET (DCDM NTTFET)-based label-free biosensor consisting of a Ge source and nanocavities within the core as well as a shell gate stack, which not only outperforms the conventional MOSFET and advanced nanowire (NW) TFET-based biosensors in terms of energy-efficiency and scalability but also exhibits a significantly high drain current sensitivity (SION = 2.9 × 108) and a threshold voltage sensitivity (SVth = 0.85), and a considerably high selectivity of more than 6 orders of magnitude. We also perform a comprehensive design space exploration for the proposed DCDM NTTFET and provide necessary design guidelines to further improve its performance considering the practical artifacts such as steric hindrance.

The basic building block of neural network is a device, which can mimic the neural behavior. The spiking neural network (SNN) is an efficient methodology in terms of power and area. Due to the excess energy consumption and larger area, various spintronic neural devices are unfit for neuron applications. In this article, we have implemented Ge source based Tunnel FET (TFET) for ultralow energy spike generation using TCAD simulator. It is seen that Ge source TFET has signature spiking frequency in THz range versus input voltage curve of an artificial biological neuron. The simulated device deploy the leaky integrate and fire (LIF) technique for generation of neurons. The simulation result highlights that the energy of device is 1.08 aJ/spike, which is several order less than existing neural based FET devices in literature.

Color-mixed (cm) light-emitting diodes (LEDs) are theoretically the most efficient white light emitters, projected to improve white light luminous efficacy by 34% compared to incumbent phosphor converted LEDs. Since white light technology is pervasive and essential, small improvements in LED technology can result in energy savings. However, cm-LEDs are not yet realized due to poor efficacy in green and amber emitting materials, a spectral region colloquially referred to as the Green Gap. ZnGeN2 is nearly isostructural and closely lattice-matched to GaN and can be heteroepitaxially integrated with existing GaN devices; ZnGeN2/GaN hybrid structures are theorized to emit green (~530 nn) light with a spontaneous emission rate 4.6–4.9 times higher than traditional InGaN LEDs when incorporated into III-N LED structures. In this report we demonstrate the molecular beam epitaxy (MBE) growth of GaN and ZnGeN2 superlattices, an important step towards realizing multiple quantum well structures required for efficient LEDs. Elemental analysis, including atom probe tomography, shows that Ga and Ge are observed in both ZnGeN2 and GaN layers, degrading the structural uniformity. The lack of elemental abruptness also leads to increased defect luminescence and reabsorption of band edge luminescence. The source of unintentional Ga distributed throughout the ZnGeN2 layers was identified as excess flux escaping from around the closed MBE shutter. The source of unintentional Ge, which tended to incorporate as a single delta-doped layer in GaN, was identified as Ge riding along the cyclical metal-rich Ga adlayer used for high quality GaN, incorporating during subsequent nitrogen-rich growth step. Modifying the growth strategy results in improved structural quality, elemental abruptness, and luminescence response. This realization of structurally and elementally abrupt interfaces demonstrates the potential of heteroepitaxially integrated binary and ternary nitrides for energy-relevant devices.

Investigation of optical parameters in Ge source SELBOX tunnel FET under visible spectrum

Raised Ge-Source with n+ pocket and recessed drain line TFET: A proposal for biosensing applications

In the recent days automated plant species recognition systems are developed to help the ordinary people in identification of the different species. But the automatic analysis of plant species by the computer is difficult as compared to the human interpretation. The research has been carried out in this field for the better recognition of plant species. Still these approaches lack with exact classification of the plant species. The problem is due to the inappropriate classification algorithm. Especially when we consider the recognition of medicinal plant species, the accuracy will be the primary criteria. The proposed system in this research adopts the deep learning method to obtain the high accuracy in classification and recognition process using computer vision techniques. This system uses the Convolutional Neural Network (CNN) and the machine learning algorithms for deep learning of medicinal plant images. This research work has been carried out on the leaf dataset of FLAVIA from source forge website. This data is fed as the training dataset for the CNN and machine learning based proposed system. An accuracy of 98% has been achieved in the recognition of the medicinal plant species

We performed Ge doping of α-Ga2O3 thin films grown on m-plane sapphire substrates using mist chemical vapor deposition. Although the typical growth rate was high at 4 μm/h, the resultant α-Ga2O3 thin films exhibited high crystallinity. We controlled the carrier density in the range of 8.2 × 1016–1.6 × 1019 cm−3 using bis[2-carboxyethylgermanium(IV)]sesquioxide as the Ge source. The highest mobility achieved was 66 cm2 V−1 s−1 at a carrier concentration of 6.3 × 1017 cm−3. Through secondary ion mass spectrometry analysis, a linear relationship between the Ge concentration in the α-Ga2O3 thin films and the molar ratio of Ge to Ga in the source solution was established. The quasi-vertical Schottky barrier diode fabricated using the Ge-doped α-Ga2O3 thin films exhibited an on-resistance of 7.6 mΩ cm2 and a rectification ratio of 1010. These results highlight the good performance of the fabricated device and the significant potential of Ge-doped α-Ga2O3 for power-device applications.

Ge/Si(001) heteroepitaxial layers were grown by HW CVD and in situ doped with Ga or Sb using a separate resistively heated Ge source containing one of these impurities. Sublimation of the germanium source gave a concentration of ~1 × 1019 cm–3 gallium atoms in the layers. The mode of introduction of this impurity into the epitaxial layers was investigated as a function of hot filament (Ta) temperature and growth temperature using C–V and Hall effect. To increase the maximum concentration of charge carriers in the Ge/Si(001) layers, a melt zone was formed on the Ge source during the growth of the layers, which made it possible to increase the concentration of impurities in the Ge layer by almost an order of magnitude.

Abstract This study presents a new dual‐metal dopingless tunnel field effect transistor with a Germanium source (GeS‐DM‐DLT) for label‐free biomolecule detection. Introducing a Ge source and dual‐metal gate provides improved drain current. We have considered an L‐shaped cavity at the top and bottom source metal region for investigating the sensitivity. The biosensor's sensitivity has been measured using the neutral biomolecules' dielectric constants (varying the k‐values in the cavity). The sensor's DC performance is investigated using transfer characteristics, BTBT rate, energy band, and electric field variation for different k‐values. The sensitivity performance of the proposed biosensor is evaluated in terms of different DC parameters (drain current, surface potential, subthreshold swing, interband tunneling rate, electric field) and RF parameters (parasitic capacitance, transconductance, cut‐off frequency, maximum frequency). The suggested biosensor offers a much‐improved SON of 9.86 × 108 and SRATIO of 1.94 × 104 for a dielectric constant of 22.0 at room temperature. Further research has been done to study the effects of dielectric materials, interface trap carriers (ITC), and temperature on drain current, drain current sensitivity, and other sensitivity parameters. The article also includes investigating the influence of the fill factor on sensitivity performance. The GeS‐DM‐DLT sensor performs best in fully‐filled conditions compared to the partially‐filled condition inside the cavity region.

Highly selective separation of germanium from sulfuric solution using an anion exchange D201 × 7 resin with tartaric acid

Abstract Germanium (Ge), as a critical metal, is in high demand due to its growing usage in emerging industries and green technologies. The Sichuan-Yunnan-Guizhou Zn-Pb metallogenic region, located on the southwestern margin of the Yangtze block, is one of the most important producers of Ge in China. The Maoping Zn-Pb deposit in the Sichuan-Yunnan-Guizhou region contains Ge-bearing sphalerite, whose crystal chemistry and process of Ge incorporation are poorly resolved. Sphalerite occurring in two hydrothermal stages (Sp-II and Sp-III) is recognized in this deposit. Laser ablation inductively coupled plasma mass spectrometry was used to map the concentrations of key elements (including Mn, Fe, Cu, Ga, Ge, As, Ag, Cd, In, Sn, Sb, Hg, and Pb) in Sp-II and Sp-III, and their distributions were qualitatively compared, followed by a quantitative assessment through application of the structural similarity index. The results suggest that Ge positively correlates with Cu in Sp-II, but with Ag in Sp-III, differences that may be related to the temperature of formation. The metamorphic basement is the main source of Ge in the Maoping deposit. Additionally, coal seams in this deposit could potentially be important contributors to Ge enrichment. A model for Ge mineralization was proposed in which the mixing of the Ge-bearing metamorphic fluids with the Ge-bearing basin brines precipitated sphalerite, and the Ge was incorporated into Sp-II and Sp-III via 2Cu+ + Ge4+ ↔ 3Zn2+ and 2Ag+ + Ge4+ ↔ 3Zn2+, respectively, under medium sulfur fugacity and low oxygen fugacity conditions.

Introducing grass pea genotypes with wide adaptability across diverse environments is important. Dry forage yield of 16 grass pea genotypes, tested in a RCBD design with three replicates across 4 locations over 3 seasons in Iran. The GGE biplot method based on SREG model facilitated a visual evaluation of the best genotypes. The first two principal components accounted for 77 % of the GE interaction and revealed six winning genotypes and four mega-environments. The average location coordinate (ALC) was used to examine both yield performance and stability and indicated IFLA-1913, IFLA-1961, IFLA-1812, and IFLA-2025 were the best genotypes. Based on the ideal-genotype approach, genotype G5 was better than all other genotypes and showed both high forage yield and stability across locations. According to G + GE sources of variations, the genotypes (IFLA-1913, IFLA-1961, IFLA-1812, and IFLA-2025) were the most suitable varieties for the grass pea-producing regions in semi-arid and rain-fed conditions. An ideal location should be both discriminating of the genotypes and representative of the average location, but we could not find such location in this research. Results confirmed that G5 (IFLA-1961) has high stability and high yield performance (4.92 t ha-1), and could introduce as favorable genotype for commercial variety release.

The vegetable planting base in the Beixintun area of Zhangjiakou City was selected as the study area, divided into the focus area and regional range as well as the upstream area. A total of 132 surface soil samples, 16 vertical soil profiles, 3 groups of surface profiles, and 4 samples each of colored pepper fruit, purple kale fruit, and corn fruit were collected. From the soil, rock, and crop sample Sc, Cr, Mo, Cd, V, Zn, Sr, Pb, Co Ni, Cu, Ge, and REE on the basis of the referenced germanium (Ge) enrichment standards and enrichment factor levels, it was found that the regional Ge enrichment rate was not high (19.7%), whereas the key area had a high Ge enrichment rate (52%). The spatial distribution of Ge and rare earth elements in the soils of the regional scope and the focal area showed a more obvious consistency, and further comparison of the correlation characteristics of Ge and rare earth elements in soil and crops showed that the ranking of rare earth content was purple kale>colored pepper>maize, whereas the ranking of the Ge element uptake intensity was maize (weak uptake)>purple kale (very weak uptake)>colored pepper (very weak uptake), and there was no obvious synergy between the uptake of Ge and rare earth elements by crops. The PMF and RDA analysis of 28 elements and indicators of soil in the study area showed that the source of regional Ge was dominated by natural geological background factors (66.3%), supplemented by anthropogenic activity-influenced factors (27%) and river deposition factors (6.7%). The source of Ge in the focal area was dominated by natural geological background factors (33.8%) and anthropogenic activity-influenced factors (27.2%), with river sedimentation factors (18.5%) and atmospheric dry and wet deposition factors Ge (20.5%) being supplemented. Soil Ge was positively correlated with rare earth elements and trace elements such as Cd, Zn, Mn, Ni, V, Co, and Cr and negatively correlated with the main elements ČNa2[KG-*2/5]O, SiO2, and K2[KG-*2/5]O and pH. Finally, by combining the analysis of soil lateral profiles with vertical profiles, it was found that the Ge migrating from the source area to the area was primarily in the main river pathway, supplemented by the secondary river and flood flow pathways.

Abstract The importance of positive band offset at the p–n interface of heterojunction thin‐film solar cells has been underlined in the authors' previous work on theoretical study of the role of n‐type layer in determining open‐circuit voltage – and experimental work by other research groups. An n‐type material with low electron affinity has the potential to fulfill the requirements for producing a high‐performance heterojunction thin‐film solar cell. In this current work, a new alternative transparent conductive oxide material for chalcopyrite heterojunction solar cells is investigated by alloying ZnO and GeO2 to grow a Zn‐Ge‐O thin‐film with low electron affinity. The film is grown by metal organic chemical vapor deposition with tetramethoxygermanium as the Ge source. Through film characterization, it is confirmed that higher concentrations of Ge in the film result in lower electron affinities and relatively constant valence band maximums. Different [Ge] / ([Zn] + [Ge]) also affects the film morphology, from polycrystalline at a lower concentration of Ge to aggregation of nanocrystals at a higher concentration of Ge. Electronic characterization and preliminary device application test show promising results for the future of the Zn‐Ge‐O thin‐film as an alternative n‐type layer for chalcopyrite solar cells.

Direct synthesis of group IV-vacancy center-containing nanodiamonds via detonation process using aromatic compound as group IV element source

Analysis on electrical parameters including temperature and interface trap charges in gate overlap Ge source step shape double gate TFET

Improved optical performance in near visible light detection photosensor based on TFET

Based on the land quality geochemical survey results in the southwest cultivated area of Nanyang Basin, the content, spatial distribution, and enrichment characteristics of Ge in surface soil (0-20 cm) and deep soil (150-200 cm) in the eastern mountainous area of Nanyang Basin were studied, and the influencing factors of Ge in the surface soil were analyzed. The results showed that the average ω(Ge) in the surface soil and deep soil were 1.39 mg·kg-1 and 1.45 mg·kg-1, respectively. In the study area, 32.22% of surface soil and 12.77% of surface soil was rich in Ge, and the rich areas of the surface soil Ge were mainly distributed in the metamorphic rock and granite-dominant development areas. The optimal theoretical model of surface soil Ge variogram in the study area was a spherical model, and the nugget effect value was 0.434, indicating that surface soil Ge had moderate spatial correlation due to the joint influence of random factors and structural factors. The enrichment factor showed that 93.61% of Ge sites in the topsoil were mainly affected by natural factors, whereas 6.39% of Ge sites were significantly affected by human factors. The source of Ge in soil in the study area was mainly affected by the parent materials of soil formation, but the enrichment of Ge in surface soil was mainly affected by the Fe, Mn oxides, quartz, and pH in the soil.