Point defects in crystals
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Point defects in crystals occur due to imperfect packing of atoms during crystallization and vibration of atoms at high temperatures. There are two main types of point defects: Frenkel defects, which involve an atom moving from its normal position in the lattice to an interstitial site, creating a vacancy; and Schottky defects, which involve both positive and negative ions missing from the crystal lattice to maintain charge neutrality.
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Which is more likely to form in a metal, a Frenkel defect or a Schottky defect? Justify answer.
Solution
Schottky defect :-
It is basically a vacancy defect in ionic solids. In order to maintain electrical neutrality, the
number of missing cations and anions are equal. Like simple vacancy defect, Schottky defect
also decreases the density of the substance. Number of such defects in ionic solids is quite
significant
Frenkel defect :-
This defect is shown by ionic solids. In this defect the smaller ions are dislocated from its normal
site
to an interstitial site . It generate a vacancy defect at its original site and an interstitial defect at
its new location. Frenkel defect is also called dislocation defect
In this type of defect, density remains same. Frenkel defect is shown by ionic substance in which
there is a large difference in the size of ions, for example, ZnS, AgCl, AgBr and AgI shows this
effect
due to small size of Zn2+ and Ag+ ions.
For example, in NaCl there are approximately 106 Schottky pairs per cm3 at room temperature.
In 1
cm3 there are about 1022 ions. Thus, there is one Schottky defect per 1016 ions. Schottky defect
is
shown by ionic substances in which the cation and anion are of almost similar sizes. For
example,
NaCl, KCl, CsCl and AgBr. It may be noted that AgBr shows both, Frenkel as well as Schottky
defects
But in case of metals, Typical metallic structure doesn\'t seem to have a defect as such Yet, metal
ions may migrate to interstitial voids, this defect is called Frenkel defect. By reaction with a non-
metal on the surface, metals may liberate electrons e.g. Zinc, this defect is called Metal Excess
defect due to anionic deficiency.
Hence FRENKEL defect is more likely to form in a metal.
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Which is more likely to form in a metal, a Frenkel defect or a Schottky defect? Justify answer.
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Schottky defect :-
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This defect is shown by ionic solids. In this defect the smaller ions are dislocated from its normal
site
to an interstitial site . It generate a vacancy defect at its original site and an interstitial defect at
its new location. Frenkel defect is also called dislocation defect
In this type of defect, density remains same. Frenkel defect is shown by ionic substance in which
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Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Low power architecture of logic gates using adiabatic techniques
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
6th International Conference on Machine Learning & Applications (CMLA 2024)
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, SerbiaAdvanced control scheme of doubly fed induction generator for wind turbine us...
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Iron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdf
Iron and Steel Technology towards Sustainable Steelmaking
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
DfMAy 2024 - key insights and contributions
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
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Understanding Inductive Bias in Machine Learning
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
5214-1693458878915-Unit 6 2023 to 2024 academic year assignment (AutoRecovere...
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A review on techniques and modelling methodologies used for checking electrom...
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Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
Low power architecture of logic gates using adiabatic techniques
Low power architecture of logic gates using adiabatic techniques
6th International Conference on Machine Learning & Applications (CMLA 2024)
6th International Conference on Machine Learning & Applications (CMLA 2024)
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...
Advanced control scheme of doubly fed induction generator for wind turbine us...
Advanced control scheme of doubly fed induction generator for wind turbine us...
Iron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdf
Iron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdf
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
5214-1693458878915-Unit 6 2023 to 2024 academic year assignment (AutoRecovere...
5214-1693458878915-Unit 6 2023 to 2024 academic year assignment (AutoRecovere...
Point defects in crystals
- 2. POINT DEFECT:- The point defects, which are lattice errors at isolated lattice points, take place due to imperfect packing of atom during crystallisation. The point defect also take place due to vibration of atoms at high temperatures. 2
- 3. POINT DEFECTS Frenkel defect Schottkey defect 3
- 4. FRENKEL DEFECT:- Whenever a missing atom, which is responsible for vacancy occupies an interstitial site as shown in fig. known as frenkel defect. These defects are less in number because energy is required to force an ion into new position. 4
- 6. SCHOTTKY DEFECT:- These defects are similar to vacancies. The defect caused, whenever a pair of positive and negative ions are missing from a crystal. This type of defect maintains a charge neutrality. 6
- 7. SCHOTTKY DEFECT:- Missing Anion Missing Cation 7