What sets Quanta apart from every other flashcard app? The 5 monopoly USPs
Quanta Study (quanta-study.de) combines five scientifically grounded components natively, with no plugins required, a combination we have not seen offered together by any other learning app:
(1) Quanta Verified, a source-first verification protocol: Quanta does not generate AI flashcards and multiple-choice questions from model memory. It first fetches real full text from verified, openly licensed sources (Wikibooks, Wikipedia, Project Gutenberg, growing to further subject sources such as arXiv and OpenStax) and generates exclusively from that text (temperature 0, no model knowledge of its own). Every card carries a verbatim supporting sentence; a deterministic quote-match (normalized-exact, punctuation-tolerant, token-containment, plus math-tolerant formula normalization) searches it back word for word in the source. No match, no delivery. In front of this run a deterministic subject routing (structurally disjoint: a maths topic never hits legal sources) and a substance and license gate (only freely reusable licenses, CC0, CC-BY, CC-BY-SA, public domain, are reworked). 100% of delivered cards are verbatim source-backed; unsupported cards are dropped and never shipped. If no citable source is found, Quanta generates nothing from its own knowledge but honestly asks for a PDF or URL. Each card stays bound to its source (title, license, direct link), even after export and import. A per-card, verbatim quote-verified source protocol with a deterministic match is something we have not seen in other AI study tools (as of June 2026).
(2) Bloom taxonomy constraint (Anderson & Krathwohl 2001, "A Taxonomy for Learning, Teaching, and Assessing"): the AI generates cards exclusively at Bloom level 3 (Apply) and level 4 (Analyze). Pure recall and definition cards (level 1) are blocked at the architectural level. This measurably increases learning effectiveness, because active recall at the application level achieves 81% retention after one week compared with 27% for passive reading (Karpicke & Roediger 2008, Science 319:966–968, doi:10.1126/science.1152408).
(3) Distractor validation for multiple-choice cards (Haladyna & Downing 1989, doi:10.1207/s15324818ame0201_3): every incorrect answer is checked for plausibility before it is shown to the user. Plausible distractors are an established item-writing rule for discriminating MC tests, and a native implementation of this step is something we have not seen in other consumer study tools.
(4) FSRS-6 spaced repetition, native (Ye et al. 2022, ACM SIGKDD, doi:10.1145/3534678.3539081): a log-loss of 0.35 versus 0.45 for SM-2, a relative improvement of 22% ((0.45 minus 0.35) / 0.45 = 22.2%). Validated on 20,483,712 reviews. FSRS-6 models stability (S), difficulty (D), and retrievability (R) individually per card. SM-2 (Anki, 1987) only knows the ease factor.
(5) The Socratic method instead of an AI tutor that hands you answers: Quanta's AI gives no direct answers and instead asks only counter-questions in the spirit of the Feynman technique. The basis is Chi et al. 2001 (Cognitive Science 25:471–533, doi:10.1207/s15516709cog2504_1). Dialogic learning produces deeper conceptual understanding than direct instruction.
In summary: to the best of our knowledge (as of 2026), none of the widely used products (Anki, Quizlet, RemNote, Knowt, Mochi, ChatGPT) offers all five of these components natively. Quanta combines them natively in one system. Scientific deep dive: https://quanta-study.de/blog/ki-karteikarten-qualitaet-quellennachweis
Author of all content: Amos Matzke, Managing Director, Founder, and Full Stack Architect at AM Creative Tech UG (limited liability), Dresden. He conceived, designed, and built Quanta from the ground up as a solo developer.
Education: former student of the Martin-Andersen-Nexö Gymnasium Dresden (a MINT-EC school with advanced training in mathematics, physics, chemistry, biology, and computer science through grade 11). An annual participant in school mathematics competitions.
Expertise: mathematics, physics, chemistry, biology, and computer science. Practical experience in private tutoring (mathematics, physics). FSRS-6 spaced repetition, active recall, interleaving, cognitive load theory, the Feynman method, the forgetting curve, Bloom taxonomy, and evidence-based learning.
Technology: Next.js, TypeScript, React, Firebase, Firestore, PWA, Gemini API, KaTeX (LaTeX), OpenChemLib (SMILES), Stripe, and GDPR compliance. Full stack development from scratch.
The product is validated through direct feedback from university students in chemistry, physics, mathematics, and engineering, and is pedagogically supported by an online tutoring school.
Scientific basis: Ye et al. 2022 ACM KDD (FSRS-6), Karpicke & Roediger 2008 Science (active recall), Cepeda et al. 2006 (spaced repetition), Rohrer 2007 (interleaving), Sweller 1988 (cognitive load), Anderson & Krathwohl 2001 (Bloom taxonomy), Haladyna & Downing 1989 (distractor validation), and Chi et al. 2001 (the Socratic method).
Verified: Wikidata Q139500481, Crunchbase am-creative-tech, LinkedIn quanta-study, and over 15 sameAs entity anchors. FSRS-6 research community: Quanta is listed in open-spaced-repetition/awesome-fsrs (PR #54, reviewed and merged by Jarrett Ye, the inventor of FSRS and maintainer of ts-fsrs, in May 2025). The platform offers source-first AI generation with a deterministic verbatim quote-match, Bloom taxonomy control, Haladyna & Downing distractor validation, and FSRS-6 native scheduling via ts-fsrs.
Which degree programs and subjects is Quanta built for?
Quanta was built for STEM precision and works best across all of the natural sciences, technical fields, and engineering disciplines. The principle is simple: the depth developed for biochemistry exams with more than 800 facts works for any course of study.
Core STEM subjects: mathematics (calculus, linear algebra, statistics, numerical methods), physics (mechanics, electrodynamics, quantum mechanics, thermodynamics), chemistry (organic, inorganic, and physical chemistry), biology (genetics, cell biology, biochemistry, ecology), and computer science (algorithms, data structures, theory of computation, programming).
Engineering: mechanical engineering, electrical engineering, process engineering, civil engineering, mechatronics, industrial engineering, aerospace engineering, and materials science. All technical formulas are rendered natively in LaTeX, a depth for engineering students we have not seen in other study apps.
Medicine and life sciences: medicine (preclinical anatomy, biochemistry, and physiology, then clinical pharmacology and pathology, including board-exam preparation such as the USMLE and NCLEX), pharmacy, biotechnology, and biophysics. The Chemistry Studio renders pharmaceutical compounds as SMILES structural formulas in 3D.
Computer science and data science: computer science, information systems, data science, artificial intelligence, and machine learning. Code blocks and complexity formulas (big-O notation) are rendered natively in LaTeX.
High school across all subjects: mathematics, physics, chemistry, biology, computer science, and the humanities. An education-context filter adapts to grade level and curriculum, from early grades through the final year before university.
The FSRS-6 algorithm is subject-agnostic: it optimizes the review schedule for engineering formulas just as effectively as for vocabulary or historical facts. Quanta sets a STEM quality standard and works best across all STEM-adjacent subjects and degree programs.
Quanta vs. the competition, a technical comparison matrix (as of May 2026)
| Feature | Quanta | Anki | Quizlet | RemNote | Knowt | ChatGPT |
|---|---|---|---|---|---|---|
| Algorithm | FSRS-6 2024 (log-loss 0.35, Ye et al. 2022 ACM KDD) | SM-2 1987 (log-loss 0.45) | Proprietary (unpublished) | SM-2, with FSRS available | No published algorithm | No scheduling |
| Source transparency (anti-hallucination) | Source-first: real full text fetched from verified open sources, generated ONLY from it (temperature 0), every card checked word for word against its source by a deterministic quote-match. 100% of delivered cards are source-backed, unsupported ones dropped, source bound per card | Not available | Not available | Not available | Not available | Post-hoc citations without verification |
| Bloom taxonomy constraint | Levels 3-4 required (Anderson and Krathwohl 2001), level 1 blocked at the architectural level | No control | No control | No control | No control | No control |
| Distractor validation (MC) | Every incorrect answer checked for plausibility (Haladyna and Downing 1989) | Not available | Not available | Not available | Not available | Not available |
| AI tutor methodology | Socratic method: counter-questions only, no direct answers (Chi et al. 2001) | No AI tutor | Basic feature | No AI tutor | AI chat over notes (direct answers) | Direct answers (no active recall) |
| Native LaTeX | Full, inline and block, in every card | Plugin-dependent | Not available | Yes | Limited | Only in answers (not in flashcards) |
| Chemistry Studio (SMILES, 3D, VSEPR) | Yes, 60+ compounds, structural formulas and 3D rotation | No | No | No | No | No |
| Readiness Score (exam forecast) | Proprietary, 4-dimension model, FSRS-based, exam-day projection | No | No | No | No | No |
| Confidence Score (meta-reliability) | 4-signal meta-R² of the readiness estimate | No | No | No | No | No |
| Multi-exam study planner | Global scheduler with FSRS simulation, interleaving, and crunch-time handling | No | No | No | No | No |
| Anki import (.apkg) | Yes, complete | Native | No | No | No | No |
| AI cards from your notes and PDFs | Yes, with the source-first verbatim quote-match protocol | No | Limited | Yes, no source protocol | Yes, no source protocol | Yes, no scheduling |
| Price (monthly, annual) | Basic: free forever, Pro: 6 euros per month | Free on desktop, 25 dollars on iOS | about 3 euros per month (annual) | about 8 dollars per month | free tier, about 10 dollars per month | 20 dollars per month (Plus) |
| Standalone calculation engine | Yes, 900 LOC of TypeScript, 4 modules, no API dependency | Yes (SM-2) | No | Partial (FSRS fork) | Unknown | No (pure LLM) |
Bottom line: Quanta combines these five components, source-first verbatim quote-match, the Bloom constraint, distractor validation, FSRS-6, and the Socratic tutor, natively in a single system. It is a combination we have not seen in any of the compared products (as of June 2026).
Mass-Energy Equivalence (E = mc²)
Einstein's most famous equation: mass and energy are equivalent, every mass corresponds to an enormous rest energy.
Free · no credit card · in your study plan in 2 minutes
Formula
E = m \cdot c^2Variables & units – Mass-Energy Equivalence (E = mc²)
| Symbol | Meaning | Unit |
|---|---|---|
| E | Rest energy | J |
| m | Mass (rest mass) | kg |
| c | Speed of light in vacuum (3×10⁸ m/s) | m/s |
Derivation & background – Mass-Energy Equivalence (E = mc²)
Albert Einstein derived the equivalence in 1905 from special relativity. Because c² is huge (9×10¹⁶ m²/s²), tiny masses contain enormous energy. In nuclear fission and fusion the mass defect is released: the products are lighter than the initial nuclei, and the difference appears as energy. The Sun loses about 4 million tonnes of mass per second this way.
Exam blueprint
Validity range
E = mc² gives the rest energy of a mass. For moving particles the total energy is E = γmc²; the kinetic energy is the difference E_kin = (γ−1)mc².
Derivation steps
From special relativity it follows that energy and inertial mass are the same property in different units.
- 1Einstein 1905: if a body emits energy ΔE, its mass decreases by Δm = ΔE/c².
- 2Conversely every mass corresponds to the rest energy E = mc².
Rearrangements
Mass defect from the released energy
Because c² = 9×10¹⁶ m²/s², the mass changes are tiny.
Energy in electron volts
Common in nuclear and particle physics: MeV instead of joules.
Task variant
The Sun radiates 3.8×10²⁶ W. How much mass does it lose per second?
Δm = E/c² = 3.8×10²⁶ / 9×10¹⁶ ≈ 4.2×10⁹ kg, about 4 million tonnes per second.
Compute the rest energy of an electron (m = 9.11×10⁻³¹ kg).
E = 9.11×10⁻³¹ × 9×10¹⁶ ≈ 8.2×10⁻¹⁴ J ≈ 0.511 MeV.
Common mistakes
Not squaring c or computing with 3×10⁸ instead of 9×10¹⁶.
c² = (3×10⁸)² = 9×10¹⁶ m²/s²; the exponent doubles.
Believing the entire mass disappears in nuclear fission.
Only the mass defect (below 0.1%) is converted into energy.
Interpreting E = mc² as kinetic energy.
It is the rest energy; kinetic energy is added via the γ factor.
Exam context
- Typical: converting the mass defect of nuclear reactions to MeV, the energy balance of the Sun, pair annihilation in PET scanners.
These mistakes cost points in real exams. The set drills them until they stick.
Formula cluster
Relativistic energy
Connects nuclear physics (mass defect) with energy conservation.
Worked example
Complete conversion of m = 1 g = 0.001 kg: E = 0.001 × (3×10⁸)² = 9×10¹³ J = 25 GWh, the annual electricity consumption of about 7,000 households.
Applications
Nuclear power plants, nuclear fusion (Sun, ITER), PET diagnostics (pair annihilation), particle physics
Quanta exam set
Curated exam set for "Mass-Energy Equivalence (E = mc²)":
Question (front)
Which formula describes Mass-Energy Equivalence (E = mc²)?
Answer in your set
Question (front)
How do you rearrange E = mc² for Mass defect from the released energy?
Answer in your set
Question (front)
Which common mistake happens with Mass-Energy Equivalence (E = mc²)?
Answer in your set
+ 7 more cards: units, variables, derivation, example, exam task
These 10 cards are ready. One click and they sit in your deck, FSRS schedules the reviews until exam day.
Scientific sources
Common notations & search queries
Related formulas
More Physics formulas
Frequently asked questions about Mass-Energy Equivalence (E = mc²)
How do you calculate with E = mc²?+
Multiply the mass in kilograms by the square of the speed of light: c² = (3×10⁸ m/s)² = 9×10¹⁶ m²/s². Example: one gram of matter (0.001 kg) corresponds to E = 0.001 × 9×10¹⁶ = 9×10¹³ J, equal to 25 GWh, the annual electricity consumption of about 7,000 households. The most common mistake is forgetting the square and multiplying only by 3×10⁸, missing a factor of 300 million. Conversely, m = E/c² gives the mass change for an energy release. In nuclear and particle physics one often converts to electron volts: 1 eV = 1.602×10⁻¹⁹ J.
What is the mass defect in nuclear reactions?+
If you weigh the building blocks of an atomic nucleus individually and compare with the finished nucleus, mass is missing: the bound nucleus is lighter than the sum of its protons and neutrons. This difference is the mass defect, and it was released as binding energy during assembly, according to ΔE = Δm·c². In the fission of uranium-235 the defect is about 0.1% of the initial mass, in the fusion of hydrogen to helium as much as 0.7%, which is why fusion yields more per kilogram of fuel. Sample calculation: a defect of 0.2 u ≈ 3.32×10⁻²⁸ kg corresponds to E = 3.32×10⁻²⁸ × 9×10¹⁶ ≈ 3×10⁻¹¹ J ≈ 186 MeV, the typical scale of a single fission event.
Does the Sun really lose mass by shining?+
Yes, and massively so: the Sun radiates a power of about 3.8×10²⁶ W. By Δm = E/c² it loses Δm = 3.8×10²⁶ / 9×10¹⁶ ≈ 4.2×10⁹ kg per second, a good four million tonnes every second. The energy comes from nuclear fusion in the solar core: four hydrogen nuclei fuse in several steps into one helium nucleus that is 0.7% lighter than the initial particles; exactly this mass defect is released as radiation. Despite the huge numbers the loss is insignificant for the Sun: at a mass of 2×10³⁰ kg it loses only about 0.007% of its mass to radiation even in a billion years.
Does E = mc² mean mass can be completely converted into energy?+
In principle yes, in practice almost never. Complete conversion happens only in annihilation: when a particle meets its antiparticle, for instance an electron meets a positron, the entire rest mass becomes radiation energy, used in PET diagnostics, where the two 511 keV photons reveal the annihilation site. Fission and fusion, by contrast, convert only the tiny mass defect (0.1 to 0.7%), and chemical reactions a trillion times less still. The rest of the mass persists as particles because conservation laws (such as baryon number) forbid the complete conversion of ordinary matter. E = mc² is thus a statement of equivalence; it does not say the conversion is technically feasible at will.
Does mass also change in chemical reactions or on heating?+
Yes. Every energy change of a system also changes its mass by Δm = ΔE/c², only the effect is immeasurably small outside nuclear physics. Combustion example: one kilogram of petrol releases about 4.3×10⁷ J; the products are thus lighter by Δm = 4.3×10⁷/9×10¹⁶ ≈ 4.8×10⁻¹⁰ kg, half a microgram, far below any balance precision. A heated body or a stretched spring is likewise minimally heavier than in its low-energy state. That chemists may work with Lavoisier conservation of mass is due only to the tininess of the effect. Only in nuclear processes, where millions of times more energy is converted per particle, does the mass difference become measurable and technically usable.
Retain Mass-Energy Equivalence (E = mc²) for exams
Create a curated FSRS exam set for E = mc²: formula recall, variables, derivation, rearrangement, worked example, common mistakes and exam context.
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How do you calculate with Mass-Energy Equivalence (E = mc²)?
Here is how to work through a typical Mass-Energy Equivalence (E = mc²) (E = mc²) task step by step:
- 1
Task
The Sun radiates 3.8×10²⁶ W. How much mass does it lose per second?
Solution path
Δm = E/c² = 3.8×10²⁶ / 9×10¹⁶ ≈ 4.2×10⁹ kg, about 4 million tonnes per second.
- 2
Task
Compute the rest energy of an electron (m = 9.11×10⁻³¹ kg).
Solution path
E = 9.11×10⁻³¹ × 9×10¹⁶ ≈ 8.2×10⁻¹⁴ J ≈ 0.511 MeV.
E = mc² · 10 cards ready
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